Data Science Career Track Bootcamp

Best Data Science Boot camps consists of the following :

1. Data Science Bootcamp
2. Data Analyst Bootcamp
3. Data Analytics Bootcamp
4. Machine learning Bootcamp
5. Artificial Intelligence Bootcamp
6. Data Engineering Bootcamp
7. Big Data Bootcamp
8. Python for Data Science Bootcamp

Before we move forward to the list of the best Bootcamps, let’s look at what benefits you’ll reap from a boot camp

Some of the major benefits of Data Science boot camps are listed below -

• Most of the boot camps offer flexible classes, which is very useful for students and professionals.
• Most boot camps charge less than a degree program.
• Most boot camps thoroughly prepare the students, including job interviews and networking opportunities.
• Most of the online boot camps offer services of a tutor who provides one-on-one guidance.

Now that we are well aware of the benefits, let’s proceed to the best boot camps available in the data science industry -

BrainStation

• Course Objectives -  

• To develop students' practical skills in data science and machine learning.
• They also offer an advanced Excel course for analytics and python for the data scientist.

• In person bootcamp, online data science bootcamp or a combination? -

• Online classes are for two weeks which offer a comprehensive hands-on experience for the foundation in data science.
• On-campus course for 10 weeks.
• Classes are available in Toronto, Ontario, and Vancouver and British Columbia.

• Course Syllabus - Majorly focuses on hands-on experiences, the 6 levels interactive model

• Cost - $95k
• Teacher-to-student ratio: 1:8

• Time for individual instruction/help:

• Students have access to the support team during the day and allocated work hours.
• Along with the instructor’s support, the students also receive additional training and tech support.
• Students can also enjoy the benefits like job interviews, resume, portfolio and other such things.
• Duration - 12 weeks

• Hours per week: 30-35 hours weekly.

• Experience of the bootcamp - Started in 2012. 7 years of experience.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - Bachelors degree in either Science, Engineering, Technology or Math.
• Languages, systems, and tools learned - Python, SQL, Matplotlib and scikit learn

• The specialty of the course -

• Ethics
• Research Design
• An alliance between data analysis and key business driver.
• Explanation of results to non-experts
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.
• Taking valid business decisions.

• Review -

"BrainStation provides a fantastic learning ecosystem. Lectures were well delivered and supplemented throughout the course to deliver key and new concepts. This ensures students came away with a solid understanding, and the confidence to go off on their own to continue learning after the course."

Basecamp

• Course Objectives -  

• To develop students' practical skills in technology, machine learning, statistics, and development process.
• Enhancing student’s ability to understand the theories in data science.

• In person bootcamp, online data science bootcamp or a combination? - Offline classes in Vienna, Austria

• Course Syllabus -

• Technology - Python, SQL, NoSQL, APIs, Spark, and GIT
• Statistics - Statistical Inference, A/B Testing Sampling
• Machine learning - Supervised and Unsupervised learning social network analysis, Deep learning recommenders, Ensemble methods
• Development process - Experimentation, Design production code, and deployment.

• Cost - $5608
• Teacher-to-student ratio: 1:5 They only accept 8-10 students per batch.

• Time for individual instruction/help:

• The student can go to the professional instructors during any time of the class.
• Apart from the weekdays, there are some allocated hours for the students to get any kind of help they want.
• Duration - 8 weeks full time

• Hours per week: 30 hours weekly.

• Experience of the bootcamp - Not available.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - Abstract thinking skills and excellent communication skills
• Languages, systems, and tools learned - Python, R, SQL, NoSQL, Git and Spark

• The specialty of the course -

• In-depth Machine learning with concepts of  deep learning in recommender systems and Ensemble methods.
• The boot camp also gives specialized training in the development process to give an all-round development to the student.

• Reviews -

"Doing the Basecamp data science bootcamp was a great decision because it brought me a long way in the space of only two months and I am so much more confident with data and machine learning now. This is all thanks to the patience and encouragement of Juraj and the other mentors. Topics were taught by experts in their respective fields and were taught in an engaging way. Even though some of the topics were difficult to grasp, Juraj went out of his way to make sure we understood the most important points. I really enjoyed it and I have away so much from the experience."

Byte Academy

• Course Objective -  

• To offer a comprehensive development covering the Python language.
• Various project works are offered to incorporate methods of AI, ML, and predictive analytics.

• In person bootcamp, online data science bootcamp or a combination? - Offline classes in New York City and Texas
• Cost -
• $14,950 in New York City
• $12,950 in Houston, Texas.

• Course Syllabus - Can be found on the official website.

• Teacher-to-student ratio: 1:5
• Time for individual instruction/help:
• At least 1 instructor will be present at all times during the class hours
• Instructors are also available online after the class hours so that the student can get their doubts cleared at any point in time.
• Duration -
• 14 weeks for full time
• 24 weeks for part-time.

• Hours per week: 40 hours weekly.

• Experience of the bootcamp - 5 years of experience.

• Percentage of teachers with full-time data science experience -100%

• Required student background - None.
• Languages, systems, and tools learned -  Python, Pandas, NumPy, and Matplotlib.

• The specialty of the course -

• Workshops to enhance communication and business skills
• Many additional subjects are offered in terms of electives
• Review -

"Byte Academy has been fantastic… I really like launching myself into an immersive hands on the program from day one. Where an experience like this teaches you the theory, but every moment is devoted to learning how to apply that theory."

Data Application Lab

• Course Objective -  

• Learnings to prepare the student to become a qualified data scientist, business analyst, and AI engineer.

• In person, online, or a combination?- Combination of offline and online courses.

• Course Syllabus - Data Science Syllabus

• Cost - $6,000.
• Teacher-to-student ratio: 1:10

• Time for individual instruction/help:

• The student can go to the professional instructors during any time of the class.
• Apart from the weekdays, there are some allocated hours for the students to get any kind of help they want.
• Duration - 16 weeks

• Hours per week: 20 hours weekly.

• Experience of the bootcamp - More than 4 years.

• Percentage of teachers with full-time data science experience - 80%

• Required student background - Elementary knowledge of Statistics and probability
• Languages, systems, and tools learned - Python, R, SQL, Hadoop, Spark, TensorFlow, and Tableau

• The specialty of the course -

• Ethics
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.
• Modeling and Testing

DataCamp

• Course Objective -  

• Comprehensive understanding of Data science and analytics.

• In person bootcamp, online data science bootcamp or a combination? - Online course

• Course Syllabus -

• Over 200 courses are available
• Also includes career tracks and skill tracks
• Practice exercises which include above 50 real-life problems
• Cost - There is a trial period followed by the cost of $29 per month or $300 per year for full access.
• Teacher-to-student ratio: Self-paced, online course.

• Time for individual instruction/help:

• The online feature gives it flexibility which allows students to ask and get help anytime
• There is also a DataCamp community forum where the students can clear their doubts.
• Duration - No limitation

• Hours per week:  Recommended study hour is 4 hours per week.

• Experience of the bootcamp - More than 5 years.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - None
• Languages, systems, and tools learned - Python, R, SQL, Git, Shell, and spreadsheets.

• The specialty of the course -

• It teaches the subjects through a practical approach.
• Machine learning
• Probability and statistics
• With real-life examples.

• Reviews -

"The courses were clearly built with love, and the instructors' enthusiasm for the subject shows in every exercise. Fantastic user experience! Not a fan of 'input box learning' like Codecademy etc. and in truth, you won't become a data scientist with Data Camp alone. However, data science is something that many people need a little hand-holding, so the format of instruction lends itself well to the content."

Data Science Dojo

• Course Objective -  

• Comprehensive courses on data science for a product manager, business leader along with artificial intelligence and big data.

• In person bootcamp, online data science bootcamp or a combination? -

Combination of offline and online course -

• 5 days boot camp includes 50 hours of training in the class. The training is combined with 10 hours of pre-boot camp and 20 hours of a post-boot camp which is completely online.
• Classes are available in Austin, Chicago, Las Vegas, Nevada, Seattle, Silicon Valley, and Washington, DC

• Course Syllabus - Available on the official website.

• Cost -  $4000
• Teacher-to-student ratio: Self-paced, online course.

• Time for individual instruction/help:

• Instructors are available before, after and during the classes.
• A student can opt for the sensei package which will allow him/her to clear doubts and get help even after the examination.
• Duration - 5 days program.

• Hours per week:  

• 10 hours + before the bootcamp.
• 50 hours + during the bootcamp.

• Experience of the bootcamp - More than 5 years.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - None
• Languages, systems, and tools learned - R, Python, Azure, AWS, Hadoop, Spark, and Kaggle.

• The specialty of the course -

• Ethics
• Research Design
• An alliance between data analysis and key business driver.
• Explanation of results to non-experts
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.
• Visualization and storytelling.
• Review -

"Data Science Dojo is an amazing boot camp - it worked perfectly well for me although I have a certain background in statistical analyses and ml itself. I believe that same would apply to various types of experts (as it was the case within my class "

Data Society

• Course Objective -  

• Learning on data science which includes Artificial intelligence and machine learning.

• In person bootcamp, online data science bootcamp or a combination? -
• Online training.
• Facility to provide a combined online-offline or in-person training at special requests.
• Based in Washington DC.

• Course Syllabus - Available online.

• Cost - $349 lifetime / $149 per year
• Teacher-to-student ratio: 1:20
• Time for individual instruction/help:
• Students have access to the support team during the day and allocated work hours.
• Along with the instructor’s support, the students also receive additional training and tech support.
• Duration - 1 to 3 months.

• Hours per week: Depends on the program.

• Experience of the bootcamp - 4.5 years of experience.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - None
• Languages, systems, and tools learned -  Excel, SQL, Python, R programming, AWS, and Spark.

• The specialty of the course -

• Ethics
• Research Design
• An alliance between data analysis and key business driver.
• Explanation of results to non-experts
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.
• Taking valid business decisions.

• Review -

"I enjoyed the course and learned a lot in a short time. I was a beginner to R and I was able to keep up with the pace, structure, and fully understood the lectures. Data Society has been incredibly supportive by reaching out to me monthly to check in, offering me advice, sharing their experiences, and answering any questions I have had."

K2 Data Science Bootcamp

• Course Objective -  

• Provides a complete understanding of data science.

• In person bootcamp, online data science bootcamp or a combination? - Online only.

• Course Syllabus - It is visible on the official site

• Cost - $6000.
• Teacher-to-student ratio: 1:5

• Time for individual instruction/help:

• 6-11 PM on weeknights
• 12 to 5 PM on the weekends*Eastern Standard Time

• Duration - 6 months

• Hours per week: 20 hours weekly

• Experience of the bootcamp - 3 years of experience

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• 1 to 3 years experience as an analyst, engineer or developer.
• Master’s degree or Ph.D. in Science, Technology, Engineering or Math.

• Languages, systems, and tools learned - Python, NumPy, Pandas, Matplotlib, Seaborn, web scraping, APIs, SQL, NoSQL, JavaScript, D3.js, Hadoop, Spark

• The specialty of the course -

• Ethics
• Research Design
• An alliance between data analysis and key business driver.
• Explanation of results to non-experts
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.
• Taking valid business decisions.

Reviews -

"I was on the K2 Data Science program part-time track. I had little experience with machine learning by doing some academic projects. I was working full-time and could not move to a location or quit my job to pursue my interest in Data Science. It has a great comprehensive study material and good assignments and projects which can be done at a flexible pace. My mentor and the TA's were very patient with my schedule. My mentor Pavitraa did a great job at making sure I got the concepts right and also helped me prep for my interviews. Overall it was a very pleasant experience."

Metis

• Course Objective -  

• Holistic knowledge of data science.
• Perfect for both beginners and professionals as they can choose the part-time courses.

• In person bootcamp, online data science bootcamp or a combination? -
• Combination of Online and Offline classes in the Bootcamp.
• Classes available in New York City, Illinois, Washington, and San Francisco.

• Course Syllabus - Available on official website

• Cost - $17,000.
• Teacher-to-student ratio: 1:10 to 1:14
• Time for individual instruction/help:
• 4-5 hours of study hour is provided during the bootcamp where the student can ask for help and also get all his doubts and concepts cleared.
• Duration - 12 weeks

• Hours per week: 40-60 hours weekly.

• Experience of the bootcamp - 5 years of experience.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• Different courses have different requirements.
• Beginner courses have no requirements.
• Intermediate courses demand basic data science and statistic knowledge.

• Languages, systems, and tools learned -  Python, Jupyter Notebook, Git and GitHub, HTML, CS, JavaScript, BeautifulSoup, Selenium, Flash, NumPy, SciPy, Pandas, Statsmodels, Scikit Learn, Hadoop, Hive, Spark, AWS, PostgreSQL, MongoDB, D3.js, Matplotlib, Seaborn

• The specialty of the course -

• Ethics
• Research Design
• An alliance between data analysis and key business driver.
• Explanation of results to non-experts
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.
• Taking valid business decisions.

• Review -

"Metis adapted a variety of their curriculum to best fit the needs of our diverse and growing data science and analyst teams. The hands-on exercises made us feel like we could hit the ground running after the training. We certainly came away having a better idea of where the current techniques and tools sit in the landscape of data science and big data."

Northeastern University’s Level Analytics

• Course Objective -  

• Introduction to data analysis for beginners.
• A comprehensive course for intermediate learners in data science.

• In person bootcamp, online data science bootcamp or a combination? -
• Combination of Online and Offline classes in the Bootcamp.
• Boston has full-time classes whereas part-time classes in San-Francisco.
• Part-time online courses are available for everyone around the globe.

• Course Syllabus - Available on request.

• Cost - $7,994.
• Teacher-to-student ratio: 1:10

• Time for individual instruction/help - Online assistance available at 24/7.

• Duration -
• Full-time course - 8 weeks
• Part-time course - 13 to 20 weeks.

• Hours per week -

• Full time - 40 hours
• Part-time - 10-15 hours

• Experience of the bootcamp - 4 years

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• Different courses have different requirements.
• Beginner courses have no requirements.
• Intermediate courses demand basic data science, Excel and statistic knowledge.
• Languages, systems, and tools learned - Excel, R, SQL, Tableau, and Machine learning.

• The specialty of the course -

• Data visualization
• Taking valid business decisions.

NYC Data Science Academy

• Course Objective -  

• Comprehensive understanding of Data Science through the bootcamp.

• In person bootcamp, online data science bootcamp or a combination? -
• Offline, in-person classes.
• Online - Live streaming or recorded videos.
• Available in New York City.

• Course Syllabus - It is visible on the official site

• Cost - $17,600.
• Teacher-to-student ratio: 1:6

• Time for individual instruction/help:

• 2-6 PM - TAs
• 9:30 AM - 5:30 PM - Instructors
• Duration -  12 weeks.

• Hours per week: 80-100 hours weekly.

• Experience of the bootcamp - Started in 2013. 6 years of experience.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• There are no prerequisites.
• However, students are advised to take the customized training module before the course, which is meant to bring all the students to a similar level.
• Languages, systems, and tools learned - R, Python, Linux, Github, SQL, Hadoop, and Spark

• The specialty of the course -

• Ethics
• Research design,
• Big data
• Deep learning, data analysis, and key business drivers
• Skills to clearly communicate the conclusions to different audiences especially those from a non-technical background.

• Review -

"The program is very comprehensive. The syllabus is well structured. They make sure your time is only spent on the most popular and useful technologies which are needed for a Data Scientist. I did some research before I choose the program. In my opinion, NYC DCA has the most competitive arrangement of the courses. Python, R, Docker, Hadoop, Github are detailed explained. Prepare for a very intensive journey. After all the hard works you will be amazed about how much you can learn in 12 weeks. The instructors are very responsive. The TAs are also very helpful. They also hold events to build the relationship between you and the potential hiring partners."

RMOTR

• Course Objective -  

• Teaching visualization and data analysis through Python.
• Introduction to Machine Learning for beginners.

• In person bootcamp, online data science bootcamp or a combination? - Combination of Online-Offline

Classes.

• Course Syllabus - Available online.

• Cost - $1,099 for 4 months or $349/month.
• Teacher-to-student ratio: 1:20

• Time for individual instruction/help:

• Online Live-mentor support during the entire day time.
• Duration - 4 months

• Hours per week: 20 hours weekly.

• Experience of the bootcamp - 14 months

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• There are no prerequisites.
• However, students are advised to have a basic understanding of math and statistics in order to comprehend the course.
• Languages, systems, and tools learned - Python, Pandas, NumPy, Statsmodels, Matplotlib, Seaborn, Pygal, Flask, Requests, Django and Bokeh.
• Review -

"Excellent hands-on experience in Python. Real projects covering the advanced concepts in Python. I particularly loved the group projects also as it depicts a real-world scenario of teamwork"

Science to Data Science (S2DS)

• Course Objective -  

• Workshops are introduced to "trains analytical PhDs and scientists in the commercial tools and techniques needed to be hired into data science roles."

• In person bootcamp, online data science bootcamp or a combination? -
• Combination of Online-Offline classes.
• Offers 2 online courses throughout the year.
• On-campus training in London, United Kingdom.

• Course Syllabus - No particular curriculum is followed. Students go through training, which is designed keeping in mind the real-life problems. They learn to apply the theories and concepts in these problems and ultimately enhance their understanding.

• Cost - $907
• Teacher-to-student ratio:
• Typically every team consists of 3-4 members.
• Each team has 1 mentor who guides them and helps them in resolving their queries.
• A technical mentor is provided to deal with any kind of technical issues.
• A business mentor is allocated who helps the team in understanding the business procedures.
• Time for individual instruction/help - Monday to Friday - 9 AM to 5 PM.
• Duration - 5 weeks

• Hours per week: 40 hours weekly.

• Experience of the bootcamp - 5 years of experience.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• 1000 hours to 10,000 hours of coding experience.
• Masters’ or Ph. D.
• Languages, systems, and tools learned - Python, R.

• The specialty of the course -

• Ethics
• Research Design
• An alliance between data analysis and key business driver.
• Explanation of results to non-experts
• Skills to clearly communicate t
• he conclusions to different audiences especially those from a non-technical background.
• Taking valid business decisions.

• Review -

"Being in the S2SD programme was a great experience for me. During the 5 weeks that it lasted, I worked full time with a team of 4 people in a real data science project, in which we were able to help a company and provide business insights for them. Apart from the technical skills that I acquired or improved (Python, SQL, machine learning libraries, natural language processing...), I gained a lot in terms of communication,team-work, and remote working.

The mentors from Pivigo were very helpful in keeping the team united and solving all sorts of technical issues. To sum up, being in the S2DS programme helped my career by improving my key skills (technical and non-technical) and giving me more confidence when facing new data science projects and meeting recruiters for interesting jobs."

Bit Bootcamp

• Course Objectives -  

• Designed by former Wall street data scientists.
• A more practical approach to data science as the course aims to give the students a taste of how computer science and statistics rule the market in real life.

• In person, online, or a combination?
• Offline course.
• Located in New York.
• Cost - $15,5000.
• Duration - 8 weeks.

• Hours per week: 30 hours weekly.

• Experience of the bootcamp - 6 years of experience.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• Experience with SQL
• Knowledge of at least one of the programming languages such as Java, C or C++.
• Languages, systems, and tools learned - Hadoop, Python, R, Spark
• Reviews -

"I am a former educator transitioning into Data Science, and this course has been an excellent foundation. The course has exceptional instructors, and an impressive curriculum to match. I've had 4 different instructors throughout my time here, and every single instructor is professional, extremely knowledgeable, approachable, and always patient.

I appreciated that the course really hones in on real-world applications of Data Science, very similar to how it would be done in the workplace. No nonsense! It is intense from beginning to end, with lots of relevant and exciting lectures, so definitely be prepared to be challenged and put in the effort!

Overall, it was a wonderful experience. I highly recommend this course to data aficionados, as well as the beginners (like myself) trying to move toward Data Science and Machine Learning."

Data Science Europe

• Course Objective -  

• Tools required to be a data scientist
• Provides mentors to students so that each individual receives a quality education as per their needs.
• A practical approach through various workshops with big companies like Airbnb, Twitter, and King.

• In person, online, or a combination?
• Offline course, as well as Online courses, are available.
• Located in Dublin, Ireland.
• Cost - €7500
• Teacher-to-student ratio - 1:9
• Duration - 6 weeks.

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• Experience with at least one of the programming languages such as Java, C or C++.
• The student must be pursuing a quantitative degree.

• Review -

"When I decided to join Data Science Europe I was not really sure what to expect and whether I will be able to learn all the things promised on the website. But already after the first week, I knew that I had made the right choice in taking the course and that I would learn a lot, e.g. Hive/SQL and machine learning models.

Without the course, I would have needed much longer first to find out what I need to learn and second to review the material."

Not only was Giulio a great teacher for the technical material, but he was also an excellent advisor for building a network, applying for jobs, and preparing for the interviews. What I liked best about the program was that the material we covered was well selected in terms of skills needed for getting a job as a data scientist."

Data Science Retreat

• Course Objectives -  

• Provides courses to develop qualified data scientists and data engineers.
• Through pair programming and portfolio project, students are taught how to handle the materials.

• In person bootcamp, online data science bootcamp or a combination? - Offline course, located in Berlin.
• Cost - €10,000
• Teacher-to-student ratio - 1:10 to 1:14
• Duration - 3 months

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -  Experience with at least one of the programming languages such as Java, C or C++.

• Languages, systems, and tools learned -Python, R, Hadoop.

• Review -

"The learning rate is tremendous. Data Science Retreat has pushed me to my limit and beyond"

Flatiron School

• Course Objectives -  

• To give the student an all-around development in data science.
• Make the student understand how to acquire data and apply their learnings in the actual data market.

• In person, online, or a combination?- Offline course, located in New York.
• Cost - €15,000
• Teacher-to-student ratio - 1:20
• Duration - 15 weeks.

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• No prerequisites required.
• All students must enroll for the pre-course training program which aims to bring all the students to a stage from where they can accurately grasp the further training.

• Languages, systems, and tools learned -Python, SQL, Machine Learning

• Reviews -

"I attended Flatiron School's Access Labs program in Brooklyn and graduated this past January. Since then I have been hired as Junior Frontend Developer, and I can say that it was the absolutely right decision to attend Flatiron. I was very unsure how their reputation stacked up in the real world and what the quality of the education was. I looked into App Academy, and Fullstack as well, and was accepted at Fullstack, (I didn't apply to App Academy).

Ultimately I choose Flatiron because the timing of the cohort was better for me, and the financing option with the Access Labs program is awesome. I currently work with App Academy graduates and they are all top notch and know other people who are Fullstack graduates that are excellent programmers too. I didn't find the boot camp experience to be crazy overwhelming, it's hard work for sure, but if you have a mature attitude and work ethic you'll be fine. Flatiron will teach you how to code, how to build applications, provide a working understanding of how code operates and how the frameworks and languages communicate with each other. If you choose not to do the hard work, you'll still probably graduate but you've just cheated yourself. Like anything in life you get out of it what you put in, and Flatiron has a ton of resources for those who engage and want to learn. Flatiron does an excellent job of helping their students develop their soft skills too, and they offer talks and workshops with working developers.

When you graduate you'll still have a lot of knowledge to fill in, data types, and algorithms, CS concepts, etc. You need to do the work and learn this stuff. Flatiron has a robust career staffing team that actively engage and assist their graduates find jobs, lifetime career coaching support, job fairs, mock tech interviews, and more. This aspect of the school cannot be understated, they offer a lot of job hunting support. The teaching assistants are excellent and extremely helpful, the teachers, for the most part, are senior devs that have switched to teaching. Flatiron can improve by implementing more code reviews of student projects, and by having stricter requirements for passing course sections.

I saw some students that would have benefitted from more focused support from the teaching staff but instead were passed to advanced sections without fulling grasping the concepts. If you as a student feel like you are falling behind you need to take the initiative to get the help you need. All in all, I am very happy with my decision to attend Flatiron, I now have the best job I've ever had."

Galvanize

• Course Objective -  

• To make the students experience on how to handle the data science tools.
• They also teach how to apply the concepts and theories of data munging, modeling, exploration, visualization, validation, and communication in the field.

• In person bootcamp, online data science bootcamp or a combination? - Offline course, located in Denver, San Francisco, Seattle, and Austin.
• Cost - $ 16,000
• Duration - 13 weeks

• Hours per week: 35-40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• The student must have prior experience in Maths, coding, and statistics.

• Languages, systems, and tools learned -Python, SQL, and Hadoop.

• Review -

"As a long-time math teacher, I really appreciate the amount of thought and organization put into curriculum design and feel that it really helped us all to learn a huge amount in a short period of time.

The instructors are kind and brilliant, and the cohort was really on it. I looked at a lot of other boot camps but was so pleasantly surprised to find just how strong and mature this program is.

Highly recommend!!"

General Assembly

• Course Objective -  

• Perfect course for individuals who are looking to start their journey with data science.
• The workshops and activities include real-life problems. This allows the student to apply their concepts and theories accordingly.

• In person bootcamp, online data science bootcamp or a combination? - Offline course, located in
• Atlanta, Austin, Boston, Denver, New York, Los Angeles, San Francisco, Seattle, Washington D.C, Chicago.
• London, UK.
• Hong Kong
• Melbourne and Sydney, Australia.
• Cost - $ 15,950
• Duration - 12 weeks

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - No requirements.

• Languages, systems, and tools learned -Machine learning, Big Data, GIT, Unix, Python, and SQL.

• Review -

“First, off I love GA. GA changed my life completely. I took part in the full-time web development immersive program and by far it is one of the best decisions I've made in my life.

Never would I have thought I would be where I'm currently at in life. My instructors at the DTLA campus are amazing and very dedicated to teaching and preparing their students for a job in tech. Of course, at the end of the day, it is all up to the students to put in the work and apply those skills.

The curriculum is very well designed to take you to the level needed to be hirable after completing the course. As for the job search, it all comes down to you. It's a little hard at the beginning but once you have your foot in the door your in. Most tech interviews are the same so the repetition of interviewing helps in your favor. software engineers are in such high demand there are open positions everywhere in tech. The skills you learn at GA get you one of those positions but like I said it's all up to you.”

Insight

• Course Objectives -  

• This fellowship is designed to reduce the gap between the academics and the professional field.
• Since it's a highly advanced course, it incorporates in-depth data science concepts, theories, and tools in the student.
• Prepares the student for the real world by conducting workshops, projects and job interviews.
  
  

• In person, online, or a combination?- Offline course, located in New York and Silicon Valley.
• Cost - Free (Fellowship)
• Duration - 7 weeks

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - Ph.D.

• Languages, systems, and tools learned -R, Python, and Hadoop.

• The specialty of the course - 100% job placement guarantee.

• Review -

"The program combines mentoring by data experts from companies such as Facebook, Twitter, Google and LinkedIn with exposure to actual big data challenges” - Harvard Business Review"

Level

• Course Objectives -  

• To prepare qualified data analysts for business and technology.
• Incorporate data analytics concepts through projects and field experience.
• Enhance problem-solving abilities.

• In person bootcamp, online data science bootcamp or a combination? - Offline course, located in Boston, Charlotte, Seattle, and Silicon Valley.
• Cost - $ 7995.
• Duration -
• 8 weeks - Full time
• 20 Weeks - Part-time

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• Bachelor’s degree
• Prior experience in analytics and statistics is recommended.

• Languages, systems, and tools learned - MySQL, R, SQL, and Excel

• Review -

“Level is a very intensive 8-week course focused on teaching you data analytics and data science. I really enjoyed my time at Level. I believe I definitely got my money's worth and the capstone project was extremely useful. Though, ties with Northeastern and their resources proved to be a huge bonus as well. I'll start with the capstone because that is the most important part of this class. You are paired up with a real company with a real problem that you have to solve.

This is immensely motivating as I am very interested in education and was paired up with Raising A Reader in MA. Working with real data and real employers is the best way to test the skills you learn. The curriculum was very sound. Everything that you learn will be applicable and is exactly what employers are putting in job descriptions of their open Data positions. I was especially taken in by the section on R. Once I started using R, it was hard to turn away.

You should definitely have an idea of the very basics of programming. It will help you learn much more while you are there. My instructor was Dale Joachim, and I could not have asked for a more enthusiastic, professional person to lead the class. I believe his curiosity made the lectures much more intriguing and helpful”

Microsoft Research DS3

• Course Objectives -  

• Offers the chance to directly work with Microsoft’s data science team.
• Students can learn many things such as material and tool handling, data analytics concepts and other valuable aspects while working with the team.
• The hands-on experience enhances their visualization and problem-solving skills.

• In person bootcamp, online data science bootcamp or a combination? - Offline course, located in New York
• Cost -
• Free course.
• Microsoft pays the student stipend worth $5000.
• Duration - 8 weeks - Full time

• Hours per week: 25 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - College student which includes graduating seniors.

• Languages, systems, and tools learned - Python and R

SPICED Academy

• Course Objective -  

• Prepare students to grow into an essential part of a data-driven organization.
• Enhance their observation skills and teach them to follow data trends and patterns.

• In person bootcamp, online data science bootcamp or a combination? - Offline course, located in Berlin.
• Cost - €9800
• Duration - 18 weeks

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -None

• Languages, systems, and tools learned -Python and Algorithms

• Review -

“Attending SPICED Academy was one of the best decisions I have made. Since I was moving from the US to Berlin for this course, I did a substantial amount of research in order to find the right school for me and I ultimately chose SPICED. I applied in March 2018 to start in the October 2018 cohort and during that time I completed the prep course work several times to get a good grasp on the concepts.

I joined the bootcamp as a complete beginner and once I finished the course, I was amazed at the incredible progress I made, how much I learned, and the applications I was able to build. The teachers are incredibly helpful and always willing to help you work through a problem but also ensure you understand why that problem occurred in the first place. That being said, this course is also incredibly demanding - it is a bootcamp after all!

I spent an average of 10-12 hours every day at school for 6 days of the week. This is a fast-paced learning environment where you are challenged on a daily basis and expected to learn by doing. One of the first things I learned at SPICED was getting comfortable with certain parts of your application not working and how to patiently troubleshoot and work with your peers to figure things out together. This is all part of the learning process! There were times when I was incredibly frustrated and other times when I was overjoyed because I made something work on my own. In all, I absolutely recommend SPICED for anyone interested in making a commitment to learning programming.

It was a gratifying experience that provided me with the skills necessary to apply to the workplace as a junior developer. You just have to be willing to put in the work in order to truly see progress in your abilities and get the most out of this course.”

Spirngboard

• Course Objective -  

• The fully online course allows the student to take classes from anywhere, at any time.
• Taught by industry experts and professionals. This helps the student understand the practical approach of data science rather than just being limited to the theoretical approach.

• In person bootcamp, online data science bootcamp or a combination? - Online only.
• Cost - $1000 per month along with $499 for the workshops.
• Duration -
• 200 hours course
• Recommended duration is 4-5 months.

• Hours per week: 40 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -
• Knowledge of Statistics and probability.
• Experience with at least one programing language.

• Languages, systems, and tools learned -Python, Git, and SQL.

• Reviews -

“I enrolled in the Data Science career track. I completed my course a couple of weeks back. I opted out of the career assistance because I already had a job which I am not planning to quit any time soon. I completed my course work. I had to interrupt my course twice. Once due to flooding and the other time when I was about to be a father.

On both occasions, Springboard paused my course and was supportive of me resuming it later.

Pros:-

1. Structure: The course is well structured and gives you a good idea of how to progress
2. Exercises: There were a lot of exercises which gave a very good insight into how a data science project progresses. This included basic and intermediate statistical analyses and advanced topics.
3. Projects: Two capstones make you practice a lot. The capstones along with mentor guidance solidified many concepts in my mind.
4. Mentors (Biggest Pro): Having a mentor was the biggest pro. I was matched up to an excellent mentor, an expert in the field and he was extremely patient with me and always pushed me in the right direction. I understand a mentor can be different for different people but if they match you up with a mentor who is helpful, extract everything from the opportunity.

Cons:-

1. I felt that not enough attention was paid to big data and data engineering. Maybe they will fix it in the future.
2. The content was...ok. You can find equally good video materials on YouTube if not better materials.

Overall:- It's a very good program for the amount of money you pay. I am not sure about the job support but based on the quality of my mentors, I am sure that is solid too..”

The Data Incubator

• Course Objective -  

• This advanced course aims to give the student an intensive knowledge of data science.
• The course offers professional mentorship which gives the student hands-on experience with real-life problems.
• The flexibility of the course allows the students to grasp the concepts efficiently.

• In person, online, or a combination?
• Combination of online and offline classes.
• Offline classes are based in the three major cities, namely, New York, Washington DC, and San Francisco.
• Cost -
• Free (fellowship)
• $3495 for the online course.
• Duration - 8 weeks

• Hours per week: 45 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background -

The student needs to be in the 1st year of either Masters’ or Ph. D.

• Languages, systems, and tools learned -Python and Hadoop.

• Review -

“As a recent graduate of the Winter 2018 cohort, going through the 8-week intensive data science training at The Data Incubator has taught me a great deal about various data science tools and has prepared me with the essential skills to thrive at my first data science job.

I've gained a full data science stack, such as creating a web application, web-scraping, data cleaning, exploratory analysis and visualization, SQL, machine-learning, big data tools, and cloud computing, as well as a business mindset. More importantly, networking with and learning from other talented and brilliant fellows has taught me a lot about myself and how to become a great data scientist. More importantly, I made a lot of connections that I can see will be long term.”

Thinkful

• Course Objective -  

• Incorporate various major elements of data science in a limited period.
• The online course makes it flexible thus allowing the student to learn at his own pace.
• Provides one-on-one mentors to enhance the student’s understanding of the data science concepts.

• In person bootcamp, online data science bootcamp or a combination? - Online
• Cost - $8,550.
• Duration - 6 months.

• Hours per week: 20 hours weekly.

• Percentage of teachers with full-time data science experience - 100%

• Required student background - None.

• Languages, systems, and tools learned - Python.

• Reviews -

“I spent a good few weeks researching all the available boot camps - emailing alum and current students to find out what they thought. With Thinkful - the overriding feedback was that the course was great as long as you were willing to work hard and do what you need to succeed.

Thankfully, that's what I planned to do! What separated Thinkful from the others was that they offered the money back if they didn't help land you a role within 6 months of graduating. To me, that spoke highly of how successful they believe themselves to me.

They were willing to put their money where their mouth was - again, as long as I played my part! I attended the flex course, which meant I could continue working full time. It worked perfectly for me, and I loved that I was able to meet twice a week with a mentor to help me push through the barriers that slowed me down to allow me to keep on growing..”

Data Analytics

Data analytics involves the process of using specialized computer systems to extract meaning from raw data. During the process, patterns are identified and conclusions are drawn after transforming, organizing, and modeling the data. So basically, it is used to describe the analysis of large volumes of data. This presents unique data handling and computational challenges that can be overcome by skilled data analytics professionals.

To become a data analyst, one must have knowledge of a programming language like R or Python, statistical skills, machine learning skills, communication and data visualization skills, data wrangling skills and finally data intuition.

According to Glassdoor, the average annual salary of a data analyst is $60,476. With more and more sectors adopting data analytics, the demand for data analysts has increased. In the healthcare industry, a high volume of structured and unstructured data is used by data analytics to make quick decisions. Also, in the retail industry, data analytics is used to fulfill the demands of shoppers.

DATA ENGINEERING

Data Engineering is an aspect of Data Science that deals with the practical applications of data collection and data analysis. Data Scientists collect and validate the large set of information to deliver insights and answer questions.  So, there needs to be a mechanism for collecting that information and applying it to real-world operations. This is the job of a Data Engineer: applying science to practical and functional systems.

The role of a Data Engineer is to find how to harvest and apply big data. There is not much analysis or experimental design. They create the technology and interface for collecting and ensure the easy flow and access to that information. They are experts in programming, system architecture, interface and sensor configuration, and database design & configuration. They build the data store that is used in providing insights after combining and querying big data sources. Many organizations like Hadoop vendor, Cloudera Inc, and IBM have started offering certifications for data engineering professionals.

BIG DATA

Big data refers to the large volumes of data that can be structured or unstructured. If this data is analyzed and converted into insights, it leads to strategic business moves and better decisions. This data can enable time reductions, cost reductions, smart decision making, optimized offerings, and new product development. Combined with high-powered analytics, big data can be used for a lot of business-related tasks like determining the cause of issues, defects, and failures, calculating risk portfolios, generating coupons based on the buying habits of the customer, and detecting fraudulent behavior.

We have so much data that we have collected since the dawn of the digital age. Today, we create as much data from the beginning of time until 2000 in just 2 days. This growth is not going to reduce anytime soon. Almost every move that we make leaves a digital trail. Whether we are communicating with our friends, using social media or just shopping, we are leaving digital footprints. By 2020, the data available will have grown to 50 zetta bytes from just 5 zettabytes.

Machine Learning

Machine Learning is a branch of Artificial Intelligence that automates the analytical model building. We know that systems can not only just identify patterns after analyzing the data, but also make decisions without any human intervention. Most of the industries that deal with large volumes of data have understood the value of Machine Learning. Gaining insights from the data helps these industries work efficiently and gain an advantage over their competitors.

With a tremendous growth rate, we will soon be seeing the applications of Machine Learning in all of the major domains. According to IFI Claims Patent Services, between 2013 and 2017, there has been a growth rate of 34% in the Machine Learning patents. Most of these patients were from major tech companies like Microsoft, Google, IBM, LinkedIn, etc. In a survey done by Google Cloud and MIT, about 60% of the organizations have started implementing Machine Learning strategies. The Machine Learning technologies are helping these organizations automating several processes which in turn is increasing their productivity.

ARTIFICIAL INTELLIGENCE

Artificial Intelligence is the simulation of human nature and intelligence processes by computer systems or simple machines. These processes include learning (acquiring information and rules), reasoning (using the learned rules to reach conclusions), and self-correction. Artificial Intelligence has application in several fields including speech recognition, expert systems, and machine vision.

Artificial Intelligence has found major application for organizations looking to automate and optimize their processes by extracting the value from data and producing actionable insights. With Machine Learning, Artificial Intelligent systems are able to use a large volume of available data to discover patterns and deliver insights. This would be impossible for humans. Companies are now able to predict critical care events, deliver targeted & personalized communications, identify fraudulent transactions and much more.

According to Harvard Business Review, in the coming decade, the effect of AI is only going to increase. Slowly, every other industry including manufacturing, healthcare, retailing, entertainment, insurance, law, transportation, finance, advertising, education, etc. are going to transform their business models and core processes to take advantage of Machine Learning.

So you want to become a Data Scientist? Vet your earning potential as a Data Scientist in your State compared to the neighbouring States.

So you want to become a Data Scientist? Explore how much you will be able to earn as a Data Scientist in your city compared to the neighbouring ones.

Named as the sexiest job of the 21st century by the 2012 edition of Harvard Business Review, the role of a data scientist has become a popular career choice for a number of good reasons. Big corporations like Facebook, Google, etc. accumulate the user data and sell them to the highest bidder to gain huge margins on their profits. Data is the answer to many questions like - how is a website like Amazon able to predict and recommend products that a consumer is more likely to buy? Or the fact that when surfing the net, you get ads specifically for items that you were looking to buy on the internet a few moments or days back? Some other major reasons why data science is a popular career choice today are:

1. The industry has started giving more importance to data-driven decisions.
2. The demand for data scientists is very high, while the supply is too low, so the salaries are still very high.
3. Due to data explosion in recent years, an enormous amount of data is being collected and data scientists are needed to make sense of them. The analysis also helps the market move in a consumer-centric direction.

Data Science Career Path :

A mathematician, a computer scientist, and even a trend spotter - all these are the characteristics of a data scientist. Their job is to decode and make sense of large amounts of data. This data is then efficiently analyzed with inferences made and presented to all the stakeholders who can be both, technical as well as non-technical. There are multiple career paths in data science which are explained below:

Business Intelligence Analyst: One of the most important applications of data science is used by a Business Intelligence analyst. It is the job of a business intelligence analyst to analyze the data to create a clear picture of the direction the business needs to go in and tap-in on both, business as well as market trends.

Data Mining Engineer: A data mining engineer, as the name suggests mines the relevant data for an organization. The main job of a data mining engineer is to examine the data for the needs of the business. Other than this, a data mining engineer also needs to keep on creating/improving algorithms that would further help improve the analysis of the data itself.

Data Architect: A data architect has to work together with developers, system designers, and users as well to create blueprints which are used by data management systems for the integration, protection, centralization as well as maintenance of the data sources.

Data Scientist: The main job of a data scientist is to further the interests of a business through the analysis of data given to them. They should drive a business case by analysis, development of a hypothesis, and the development of an understanding of data. This would help in exploring relationships between the different data points in the data set.

Senior Data Scientist: This is a role for someone who is experienced in this field. The responsibility of a senior data scientist is to predict and anticipate what the business needs could be in the future, and accordingly fine-tune projects and analysis.

We have compiled a list of 8 top skills one needs to become a successful data scientist:

1. Python Coding
2. R Programming
3. Hadoop Platform
4. SQL database and coding
5. Machine Learning and Artificial Intelligence
6. Apache Spark
7. Data Visualization
8. Unstructured data

1. Python Coding: Python is the language of choice for most when it comes to data science. There are many reasons for its popularity among the data scientists, some of which are - its versatile nature which allows Python to be used for many different kinds of applications; simplicity is also a major factor, Python language is easy to read and write; most important of all is the thriving open source community that Python has worldwide which keeps adding to the features of this programming language.
2. R Programming: R programming is preferred by many in the data science field due to the number of tools it offers while programming. Being proficient in at least one of the many analytical tools it offers is important if data science is going to be your choice of career.
3. Hadoop Platform: Although not mandatory, this is an important skill  to have for a career in data science. According to a study done by CrowdFlower on 3490 LinkedIn data science jobs, Hadoop is the second most important skill to become a data scientist.
4. SQL database and coding: Learning SQL database is an important task to do for any data scientist enthusiast. MySQL offers quick commands that save time while performing operations on the database while also decreasing the level of technical expertise required to manage it.
5. Machine Learning and Artificial Intelligence: Machine learning is becoming the next hot prospect in the tech industry and its applications are endless. It is a field of data science as all Machine learning algorithms are applied to data. If you want to become a successful data scientist then proficiency in these skills is necessary. A data science enthusiast should have good command over the following:
1. Reinforcement Learning
2. Neural Network
3. Adversarial learning
4. Decision trees
5. Machine Learning algorithms
6. Logistic regression etc.
6. Apache Spark: Apache Spark is a big data computation tool and is also one of the most used data sharing technologies around the globe. Data scientists prefer Spark over Hadoop due to its speed. Apache Spark is faster due to the fact that it makes caches of the computations inside system memory while Hadoop uses the disk for read/write operations.Easy to use and high-speed computations are what makes Apache Spark stand apart. The tool is used to make the algorithms run faster. It significantly helps in the division of data processing of large chunks as well as in the case of complex and unstructured data sets. Apache Spark prevents any loss of data.
7. Data Visualization: A data scientist is just given a large chunk of data and tasked with analyzing it. To make relations between different data points, it is imperative that a data scientist has skills to use visualization tools such as d3.js, Tableau, ggplot, and matplotlib. When data scientists create results from the data, these tools help to put these results in a visual format for everyone to understand it better. One of the most important aspects of data visualization is that it significantly helps the organization in a way that brings them closer to the customer’s experience and needs by working directly with the data. Data scientists are able to gain insights from a particular data and consequently use that result to act on a new outcome.
8. Unstructured data: Data given to data scientists is largely unstructured, so it is essential that a data scientist is aware of the necessary skills required to manipulate unstructured data as well. Unstructured data generally means content without any labels and unorganized into database values. For example, videos, social media posts, audio samples, customer reviews, blog posts, etc.

For any successful data scientist, below are the 4 essential behavioral traits that one must have -

• Curiosity – Dealing with a huge amount of data every day can become mundane and so it is important to stay curious to discover new insights from raw data.

• Clarity – Data Science is a huge field and you may end up getting confused about why you are doing what you are doing. It becomes imperative that you understand the need at every step and why it is the best way to do so as well.

• Creativity - Main aim in data science is to visualize or manipulate data in a way that makes its analysis simple and easy to comprehend, so data scientists should always keep thriving for innovative ways to do so. Keeping abreast of latest visualization tools can help.

• Skepticism – Skepticism is an important behavioral trait that distinguishes data scientists from other creative minds. As a data scientist, you would have to be skeptical about how you use and analyze data.

Being a part of the ‘Sexiest Job of the 21st century’, as quoted in Harvard Business Review, has its own benefits. Below are the top 5 proven benefits of being a data scientist-

1. High Pay: For any job, let alone the data scientist job, we expect high pay. And highly qualified professionals such as data scientists, naturally get higher pay. Also due to the high demand in industry and low supply of well-trained data scientists, these jobs are one of the highest paying jobs in the tech world today.
2. Good bonuses: Organizations do whatever they can to attract best data scientists as well as retain who are already performing well. So good bonuses are usual if you are a good performer. These bonuses can also be in the form of perks such as signing perks, or equity shares, etc.
3. Education: The qualification bar to become a data scientist is really high so naturally anyone who is a data scientist would be a scholar. You would probably have a Masters or a Ph.D. degree with you by the time you are searching for data scientist jobs. Due to an extensive educational background, sometimes you might also be offered a job as a lecturer or a researcher in the field for both, governmental as well as private institutions.
4. Mobility: Data science is used in every field which means job opportunities are present around the globe where data is being collected - generally in developed countries. This means that wherever you might be traveling for your data scientist job, you would be getting a hefty salary to go along with a great standard of living as well.
5. Network: Naturally, after investing so much time into education you would be having an educative and useful network of data scientists. This network is generally expanded by your involvement in international journals through research papers, technical talks at data science conferences and many more. These networks help in getting better jobs as well through referrals.

We have provided below 3 educational paths for you if you are aiming to become a data scientist:

1. The most common and safest way is to complete a degree. Although it involves investing a lot of your resources, be it money in the form of fees or even time in terms of duration of courses, the benefits are immense. Some of these advantages include structure to learning, internship experience, networking, and most of all you get recognized academic qualifications for your résumé.
2. Degrees impose a timeline which one has to adhere to. However, you can also opt for Online courses and learn at your own pace. Just like degree courses, they assign projects but studying online gives you more flexibility. Some people even report better concentration in online classes due to the lack of classroom activity.
3. Finally, the easiest and shortest path is going through Machine Learning Bootcamps. Unlike online courses and degree courses, these boot camps are highly intensive and follow a rapid pace. They combine theory with hands-on experience to get students ready for the job market. The only drawback is that you won’t have a degree after you complete the boot camp.

Academic qualifications are highly valued in fields like data science according to a 2017 published report. The report states that around 90% of data scientists are academically qualified with 49% of them having a Master’s degree and the rest having a Ph.D.

The field of data science has taken all industries by storm. Today, it is one of the highest paid as well as the most in- demand field in the market. Let’s understand what are the different roles in the industry along with their skills, responsibilities, and salaries.

Data Scientist

One of the most in- demand jobs in the data science industry is that of a Data Scientist. You can estimate the demand for a data scientist by the fact that they earn 22% more than any other employee in the analytic field.

• Role of a data scientist -

A data scientist has to collect raw data. Most of the times these data are mixed up with un-required, not usable or damaged data. It is the data scientist who has to clean it up, process and analyze in order to gain useful insights from the data.

• Mindset -

Since a data scientist is expected to deal with heavy and crude data daily, he must have a curious mindset which would allow him to enjoy the job.

• Following are the skills required for the job -
• Ability to disintegrate and distribute the data.
• Modeling skills- After observing the data, he must be able to predict how the data will be used in the future.
• Storytelling and visualizing- In order to make the data useful for the organization, a data scientist must be able to process it and explain it to others on how they should work with the data or what they should do with it.
• A data scientist must have a thorough knowledge of subjects like Mathematics, Statistics and Machine learning.

• Languages -

R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark

• Responsibilities  -
• Using machine learning techniques, he should be able to pick features, create and optimize classifiers.
• Data mining.
• Analyzing third party data sources information and then choose the useful ones to enlarge the company’s data.
• Increasing data collection methods to incorporate more appropriate information for the analytic system.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $105,000 per annum. The range differs between $70,000 - $1,50,000.

Companies like HP and IBM are often in need of a Data Analyst. Just like a data scientist, this job also demands a vast knowledge and skills so that the analyst is able to combine his technical skills with imagination. Data Analysts are often referred to as Data detectives.

• Role -

A data analyst has to accumulate data. More often these data constitutes of raw data which cannot be used unless process.

Therefore, the data analyst processes this data before analyzing them statistically.

• Mindset -

A data analyst must be very intuitive while dealing with the sources. He must have curiosity and a “figure it out” attitude to be successful in the field.

• Following are the skills required for the job -
• Ability to handle and excel in the statistical tool. For example, MS-Excel.
• Has in-depth knowledge of database systems such as SQL and NOSQL.
• Must have excellent communication skills to able to explain his analysis to the team.
• A data analyst must have a thorough knowledge of subjects like Mathematics, Statistics and Machine learning.

• Languages -

 R, Python, HTML, Javascript, and SQL.

• Responsibilities  -
• Recommend better methods to obtain and analyze data
• Suggest and implement procedures to enhance the quality and efficiency of the data collecting system.
• Should be able to collect and statistically analyze data such that the company can have adequate and accurate information.
• Should be able to recognize the patterns and data trends.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $60,000 per annum. The range differs between $40,000 - $89,000.

The demand for a data architect is growing day by day as more and more companies are searching for data. Data is everywhere, but one needs skills to collect it and organize it in such a way that it can be useful for the future. Banking sectors are often in search of data analysts as they help them to effectively manage their data sources. A data architect is also known as a contemporary data modeler.

• Role -

A data architect must be able to create blueprints for data. This allows them to manage and integrate the data into the system. Ultimately, the management skill must be effective enough to centralize, protect and maintain the data source.

• Mindset -

He must have an active and intriguing mindset. He must also enjoy working with the designs and patterns of the data architecture.

• Following are the skills required for the job -
• Ability to give solutions to data warehousing.
• Has in-depth knowledge of database architecture.
• Be at ease with handling the ETL, spreadsheet and BI tools.
• Should be able to model data.
• Have an understanding of system development.

• Languages -

SQL, XML, Hive, Pig, Spark.

• Responsibilities  -
• Ability to identify a structural solution to maintain a company’s database.
• Work with other data science workers such as the data analysts and administrators to design safe access to the company’s data.
• Designing database solutions.
• Should be able to evaluate the requirements and work accordingly.
• Form design reports.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $1,21,000/yr. The range differs between $83,000 - $1,60,000.

This job requires in-depth knowledge of statistical methodologies and theories. A statistician is the one who acquires information from the pile of data and converts it into a useful resource. Due to their vast knowledge of the field, they are also referred to as the historic leaders of data.

• Role -

Using their vast knowledge and statistical methods, they assemble, dissect, and decipher the data.

Their interpretation is both quantitative as well as qualitative.

• Mindset -

A statistician needs a logical mindset to accomplish his task.

• Following are the skills required for the job -
• Knows how to effectively handle cloud tools.
• Has in-depth knowledge of statistical theories and methodologies.
• Have an understanding of Machine learning and data mining.
• Knowledge of database systems.

• Languages -

R, SAS, SPSS, Matlab, Stata, Python, Perl, Hive, Pig, SQL, Spark

• Responsibilities -
• Ability to effectively utilize the cloud knowledge to incorporate the statistical data on the cloud server.
• Suggest and implement procedures to enhance the quality and efficiency of e-data collecting system.
• Plan database solutions.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $84,000/yr. The range differs between $56,200 - $1,20,000.

This job demands expertise in various programming languages, such as SQL and XML along with Java. They are also known as database caretakers.

• Role -

• To make sure that every stakeholder is able to get easy and secure access into the database.
• To ensure that the database is performing effectively and proper measures are implemented to keep the data secure.

• Mindset -

A database administrator is also the caretaker of the database. In order to accomplish this, he should have a mindset which allows him to block disasters.

• Following are the skills required for the job -
• Should know the process of backup and recovery.
• Have in-depth knowledge of data modeling and design.
• A comprehensive understanding of data security,
• Knowledge of database systems.

• Languages -

SQL, Java, Ruby on Rails, XML, C# and Python

• Responsibilities -
• Ability to effectively utilize customized software to organize the data.
• Should be able to effectively plan.
• Suggest and implement methods to secure the database.
• In a time of emergency, he/she should be able to efficiently manage and recover data.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $85,400/yr. The range differs between $50,000 - $1,20,000.

Unlike other jobs in the data science field, this one is the least technical. But the lack of technical aspects is compensated by an in-depth understanding of business methods. They are also known as the change agents.

• Role -

• Implement methods that enhance the business procedures in the company.
• Act as a link between the business and the IT sector.

• Mindset -

The job demands a flexible mindset which allows them to be the middleman between the IT sector and business.

• Following are the skills required for the job -
• Efficient handling of basic tools like MS office.
• Have knowledge of Data visualization tools.
• Have a great listening and storytelling skill.
• In-depth understanding of Business intelligence.

• Languages -

SQL, Java, Ruby on Rails, XML, C# and Python

• Responsibilities -
• Ability to suggest technical solutions in the business models.
• Implement methods that help the company to increase its sales.
• Should be able to effectively plan and execute as per the economic requirements.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $69,000/yr. The range differs between $49,000 - $98,000.

This job demands someone with past experience in programming and heavy software knowledge. Most of the data scientists are either an ex-computer engineer, programmer or mathematician.

• Role -

• Develop, construct,  maintain and implement architectures.
• Design database and large-scale processing systems.

• Mindset -

Due to past experience in computer programming and now dealing with data, a successful data engineer should have the mindset of an all-rounder.

• Following are the skills required for the job -
• Understanding of Database systems such as SQL and NOSQL
• Suggest solutions for data warehousing.
• Data Modeling
• Data APIs

• Languages -

SQL, Hive, Pig, R, Matlab, SAS, SPSS, Python, Java, Ruby, C++, Perl

• Responsibilities -
• Ability to manage and optimize data.
• Conduct tests to ensure the credibility of the database.
• Should be able to effectively plan and execute as per the database requirements.

• Average salary -

As per LinkedIn Salary, the base salary is reported to be $98,000/yr. The range differs between $65,000 - $140,000.

The job requires leadership qualities as a Data and Analytic Manager gives direction to the data science team. The job demands high commitment and therefore rewards the employee with one of the highest packages in the industry.

• Role -

• Superior management skills as they have to manage a whole team of data analysts and scientists.

• Mindset -

Being a manager, they must be optimistic and visionary.

• Following are the skills required for the job -
• Understanding of Database systems such as SQL and NoSQL
• Excellent communication skills to properly channel their message in the team.
• Leadership and project management skills
• In-depth understanding of data mining and predictive modeling.

• Languages -

SQL, R, Matlab, SAS, Python, Java.

• Responsibilities -
• Manage the team of scientists and analysts
• Take hiring decisions
• Proper selection skills
• Superior decision making qualities.
• Oversee the work of the team and extract the required result.

• The average salary -

As per LinkedIn Salary, the base salary is reported to be $1,07,000/yr. The range differs between $75,000 - $1,42,000.

Below are the steps which you can follow in order to become a successful, top-notch data scientist:

1. Getting started: First of all, choose a programming language of your preference. Be also wary of the tools it offers in the ML field. We recommend Python or R programming language.
2. Mathematics and statistics: Data science is nothing without mathematics and statistics. All the data given to a data scientist is in its raw form and with the help of tools, mathematics, and statistics, it is transformed into  patterns that exhibit different kinds of relationships between data points. It is, therefore, a must that you have a good command over both, mathematics as well as statistics.
3. Data visualization: A part of the job of a data scientist is to visualize the data. Sometimes it is the end result, but many times visualization of the data is needed to understand it and help it analyze better. For communication with all stakeholders, it is imperative that you know how to visualize the data and present to even a non-technical stakeholder.
4. ML and Deep learning: Data science and Machine learning go hand in hand, so if you want to become a successful data scientist then grasping and implementing machine learning techniques to tackle problems is mandatory. Deep learning is another aspect of ML that is heavily used because of the efficiency it provides with large data sets. It is strongly advised that you get yourself some valuable experience in both, Machine learning as well as Deep Learning.

Being declared as ‘The sexiest job of the 21st century’, the job of data scientist naturally demands an equally flashy academic background. If you are looking to become a successful and top-notch data scientist, look no further as we have compiled a list of key skills and steps you can follow on the path.

1. Degree/certificate: It is no secret that most of the data scientists are academic scholars. Most of them have completed their higher education be it Masters or even Ph.D.’s. Completing these degrees require you to do intensive research to help you get accustomed to the tools that real data scientists use for real-world problems. These courses impart fundamental knowledge and teach how to use cutting-edge tools. Certificates also help in a significant way. They garner global recognition by adding credibility to your skills and make your CV attractive to the employers.
2. Unstructured data: The primary data which data scientists have to work with is unstructured. It is the job of the data scientist to structure it and make it fit inside a database.
3. Software and Frameworks: Softwares and frameworks are the tools used by the data scientists to deal with the huge chunks of unstructured data. Therefore, it is essential to be proficient in using the software and frameworks to go along with a suitable programming language for you, preferably R.

1. It is important to know that at least 43% of data scientists use R language for their data analysis despite it having a steep learning curve. This really goes on to show that the benefits of R are huge and so are the returns you get after learning it.
2. When your data exceeds the memory capacity that is used to process it, tools such as Hadoop come in handy. This framework relays the data to different points in the machine. Spark, Hadoop’s counterpart, is also gaining popularity after Hadoop itself. It is also used for processing and computation work like Hadoop but is much faster than its counterpart. One major advantage of Spark is that it prevents loss of data during processing and this feature is not provided by Hadoop.
3. c. After you process the data, you would be needing a database to store it and use it for later analysis. Therefore, it is important that you are proficient in databases as well. The most popular skill found in most of the data scientists is of SQL.

4. Machine learning and Deep Learning: After gathering and preprocessing the data, comes the step to inject it into your model. This is where you employ your ML and deep learning skills.

5. Data visualization: Data visualization is an important part of the job for data scientists. As the industry is moving towards data-driven decision-making systems, it becomes essential that all the stakeholders, technical as well as non-technical, are able to understand the data and make customer-centric decisions through charts and graphs provided by the data scientists. Some of the famous tools which help fulfill this are matplotlib, ggplot2, etc.

According to a survey, more than 90% of data scientists hold a degree. That distribution is further divided such that 49% of them hold Master’s while 41% are Ph.D.’s.

A degree helps in various ways in the data science field  –

• Networking – Pursuing a degree helps you meet like minded individuals and network. This network can  eventually help you search for an appropriate opening or get good references that can increase your chances of landing a job.  

• Structured learning – A degree course imparts knowledge in a structured way so that every concept is clearly understood with a clear picture in mind of the whole syllabus. The curriculum is also designed in a way so that it incorporates all the latest trends and is, therefore, more effective when compared to doing learning in an unplanned manner.

• Internships – Internship is an important part of your degree program. The aim of an internship is to get real-world experience.

• Recognized academic qualifications for your résumé – A degree is something which you can show in your resume and will give you more preference in the race for top jobs.

Getting a Master’s degree is definitely beneficial to your career but it is not mandatory. You can determine whether you need a Master’s in Data science or not by evaluating yourself in the below questionnaire. If you score more than 6 points, then earning a Master’s degree is recommended.

Without a doubt, knowledge of programming languages is invaluable and a must for any data science enthusiast. Some major reasons why knowledge in programming is mandatory in the data science field include:

• Data sets: As is known, there is generally a huge amount of data that is to be processed by a data scientist and these data sets are not always preprocessed. Therefore, your programming language skills help you here by aiding you to tackle both, large as well as small data sets with much ease.

• Statistics: Ability to work with statistics is significantly improved by programming skills. If a data scientist is proficient in statistics and not in any programming language then even the knowledge of statistics decreases in its usefulness.
• Framework: Frameworks are important in a way that organizations use them to automate experimenting, visualization of data as well as managing the pipeline at some large organization. And programming is the key to create a suitable and appropriate framework.

There are many skills that you would need in order to become a successful data scientist, however, we have compiled a list of the top 6 technical skills.

1. Mathematics
2. Machine Learning
3. Coding
4. Data mining
5. Data cleaning and munging
6. Data visualization

1. Mathematics - You need to know how to crunch huge amounts of numbers in data and make sense of it. For this, you don’t necessarily need a Ph.D. in Mathematics but being skilled in fields like algebra, algorithms, and statistics is important.
2. Machine Learning – It goes without saying that Machine learning is one of the hottest skills in the market. Techniques such as logistic regression, decision trees, and supervised learning along with many others help you stand out from the rest of data scientists. All these skills can be employed while tackling different kinds of data science problems.
3. Coding – Data is present in another form and requires many iterations. All these things can be simply coded into a program and the data is simply fed to it. One of the easiest and popular programming languages in the market is Python.
4. Other important skills are –

a. Software engineering skills (e.g. distributed computing, algorithms and data structures)

b. Data mining

c. Data cleaning and munging

d. Data visualization (e.g. ggplot and d3.js) and reporting techniques

e. Unstructured data techniques

f. R and/or SAS languages

g. SQL databases and database querying languages

h. Big data platforms like Hadoop, Hive & Pig

i. Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch

j. Cloud tools like Amazon S3

Along with technical skills, in today’s world, it has become important to gain business skills as well if you want to become a successful data scientist. Below are the top 4 business skills you need:

1. Analytic Problem-Solving
2. Communication Skills
3. Intellectual Curiosity
4. Industry Knowledge

1. Analytic Problem-Solving – Major part of your job is to analyze the data. You need to have a problem-solving approach to the problems at hand in order to come to an efficient and optimized solution. Knowledge of best practices and the right strategies are also needed.
2. Communication Skills – As a data scientist, you will analyze the data and provide insights or results. Sometimes these would be technical, sometimes not. Therefore, your communication skills become key when you are a data scientist. You should be able to communicate your findings to the other person irrespective of one’s technical expertise.
3. Intellectual Curiosity: To stay alive in this competitive field, you need to always find the answer to “why” we are doing what we are doing. If not, then data science is not for you. To produce value to organizations, you’d need a combination of both curiosity and hunger for the same.
4. Industry Knowledge – Lastly, one of the most important and underrated skills is having solid industry knowledge of data science. This will give an understanding of what needs to be prioritized and what needs to be ignored.

Although the main job of a data scientist is to analyze the data and deal with numbers, communicating the result and the analysis is also a part of the job and an important one for that matter. A data scientist must be able to communicate customer analytics and business insights to all the stakeholders.It is important to remember that not all the stakeholders hold technical expertise but they do need to know the analysis and data in order to make decisions. This is why it is imperative that a good data scientist is able to do so irrespective of one’s level of technical expertise.

Therefore, understanding the requirements of a customer and relaying your findings to the customer is also a key skill that a data scientist must have to be successful.

If you want to brush up your data science skills then look no further, we have put together a list of ways you can take up to polish your skills before going for a data science job interview:

• Boot camps: The main objective of boot camps is to provide you with both, theory as well as practical, knowledge at a rapid pace.A Bootcamp is a high energy, intensive training, combining theory and hands-on learning. These can be between 4 to 5 days in duration.

• MOOC courses: Online courses provided by international institutions impart knowledge which is in trend and useful to out-of-touch data scientists. These courses, taught by data science experts, polish your conceptual as well as implementation skills in the form of lecture videos, assignments, etc.

• Certifications: Certifications give you recognition and help improve your CV in a major way. The more famous your certification, the more weight it adds to your CV. Some of the top certifications in the data science field are:

• Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
• Cloudera Certified Associate - Data Analyst
• Cloudera Certified Professional: CCP Data Engineer

• Projects: Projects help you in many ways. From finding a new way to tackle an already answered question with new constraints, to help push your implementation skills,  projects are a significant way to polish or brush up your skills.

• Competitions: Competitions like Kaggle etc. help you refine your thinking ability and force you to use an out-of-the-box solution which is optimized and one which satisfies all the constraints.

We live in an era of data explosion. From something as small as your browsing history to something as big as medical diagnosis, or analysis of e-commerce website sales, everything is stored in the form of data. Many organizations accumulate data, analyze it and then accordingly fine-tune their application to significantly improve customer experience. Data science jobs offered by these organizations directly determines what kind of organizations they are:

• Google Analytics is used by smaller organizations as they have less both, amount of data as well as operations to be performed on it.
• Organizations which are mid-size have data to play with and analyze and so would need data scientists to employ ML algorithms on it.
• High profile organizations such as Facebook or Google already have big teams for data science to go along with the huge amount of data they collect. Normally, these organizations often look for data scientists with a specialization to fill in specific roles in their already established team.

Best way to practice and gain expertise on your data science skills is to simply tackle as many problems as you can while increasing the difficulty along the way. There are different levels of difficulty of data science problems, and we have categorized them appropriately according to the expertise level required to work on them:

Beginner Level

• Iris Data Set: The Iris data set is widely popular and known as the stepping stone for all data science enthusiasts. It is one of the easiest ones to analyze as well. This data set is versatile, resourceful, and easy in the field of pattern recognition. It tests various classification techniques. The Iris data set contains 4 columns and 50 rows in it.Practice Problem: Predict the class of a flower on the basis of these parameters.  

• Loan Prediction Data Set: The sector which has the greatest use of data analytics and the technologies of data science is that of the banking domain. The Load prediction data set offers the experience of what concepts are really employed in the banking and insurance sector. It is a classification problem data set. The concepts that it teaches are:
• New challenges in the banking domain
• What strategies are implemented in this sector
• How an outcome depends and changes upon the values of a variable etc.

The Loan prediction data set contains 13 columns and 615 rows

Practice Problem: Predict if a given loan will be approved by the bank or not.

• Bigmart Sales Data Set: Another sector which heavily employs the methodologies of data analytics and data science is the Retail Sector. Below are the operations in which data science is used in this sector:
• Bundling of products
• Offer customizations
• Inventory management system etc.

This data set is generally used in Regression problems. It contains 8523 rows and 12 variables.

Practice Problem: Predict the sales of a retail store.

Intermediate Level:

• Black Friday Data Set: From a retail store, all the sales transactions were collected and put into this data set. In order to gain insight into the shopping sector - as to how a customer shops in today’s world and explore new methodologies along with it, this is the best data set to play with. The black Friday data set is a regression problem and contains 550,069 rows and 12 columns.Practice Problem: Predict the amount of total purchase made.
• Human Activity Recognition Data Set: The Human Activity Recognition data set was made by collecting data from inertial sensors placed inside smartphones. These recordings of around 30 human subjects are what constitutes this data set. This data set contains 10.299 rows and 561 columns.Practice Problem: Predict the human activity category.
• Text Mining Data Set: Obtained from competition, the Siam Text Mining Competition held in 2007, this data set contains reports of aviation safety. These reports describe the problems/issues occurred on certain flights. It is a multi-classification and high dimensional problem.Practice Problem: Classify the documents on the basis of their labels.

Advanced Level:

• Urban Sound Classification: As the name suggests, it is a collection of audio samples. Rather than artificial, this is a real data and real-life scenario. This data set contains around 8,732 audio clippings of urban sounds. These clippings are categorized into 10 classes and hence, this problem introduces the person to the concepts of how audio is processed in the real world. This is a classification problem as it consists of 10 classes into which all the clippings can be classified.Practice Problem: Classify the type of sound that is obtained from particular audio.
• Identify the digits data set: This data set enables the learner to explore, analyze and recognize the different and unique elements of an image. Some of its features are:
• Data set contains 7000 images.
• Size of this data set is around 31 MB.
• 28x28 is the dimensions of each image.Practice Problem: Identify the digits present in a given image.

• Vox Celebrity Data Set: This is a really popular data set as it involves recognizing the speaker which in this case happens to be celebrities. Extracted from YouTube videos, this data set boasts a collection of words spoken by celebrities themselves. This problem introduces the concept of audio processing to the learner. The aim is to isolate each speech and recognize it. The Vox Celebrity data set contains 100,000 words spoken by a total of 1,251 celebrities in YouTube videos.Practice Problem: Identify the celebrity that a given voice belongs to.

There are many sources that you can start learning from in order to prepare yourself for a data science job but learning in a sequential way with proper resources is necessary. We have listed down 8 important steps that you need to follow sequentially in order to get a job in Data science with all certainty.

1. Getting started
2. Mathematics
3. Libraries
4. Data visualization
5. Data preprocessing
6. Machine Learning and Deep Learning
7. Natural Language processing
8. Polishing skills

1. Getting started: First things first, understand what data science means at its core and what are the responsibilities of a data scientist. Then, choose a programming language suitable to you and relevant for data science. This language should have a good global community and support enough tools for analysis.

2. Mathematics: As a data scientist, your job would be to crunch numbers, making sense of raw data by determining relationships between various data points and then finally representing or visualizing them. For all these tasks, mathematics is required. You need to have a good command over mathematics and statistics. Some of the major topics that you can pay attention to are:

a. Descriptive statistics

b. Probability

c. Linear algebra

d. Inferential statistics

3. Libraries: Programming languages alone don’t pack enough stuff to process the huge amounts of data a data scientist would need. Therefore, an open source community and several organizations come together to provide various packages, tools, and libraries to do almost everything possible with data. Some of these famous libraries are:

a. Scikit-learn

b. SciPy

c. NumPy

d. Pandas

e. Ggplot2

f. Matplotlib

4. Data visualization: It’s the job of a data scientist to determine patterns and relationships between different data points and provide all the stakeholders with the visualization of the same. The most common solution is to visualize the data by plotting a graph and the following libraries are best suited for this task:

a. Matplotlib - Python

b. Ggplot2 - R

5. Data preprocessing: Data is generally provided in the raw and unstructured form to a data scientist which is why it becomes essential to preprocess this data and make it ready for further analysis and processing. There are basically two things that can be done to preprocess the data - feature engineering and variable selection.

6. ML and Deep learning: Having machine learning and deep learning skills are important to have on your CV if you are looking for a data scientist job in the market. As most data sets are huge and deep learning algorithms are employed in the case of big data sets, it is important that along with Machine learning algorithms, you learn deep learning skills as well. Methodologies such as neural networks, CNN, and RNN are some of the top skills.

7. Natural Language processing: Data given to data scientists are also sometimes in the form of text.

8. Polishing skills: Online competitions such as Kaggle etc. help in boosting as well as polishing your data science skills. Other than these competitions, projects are a way to explore the field and push your thinking and analytical skills.

We have compiled a list top 5 steps to follow if you are preparing for a data science job:

• Study: Goes without saying that studying the concept is the most important part, cover the following topics in detail-

• Probability
• Statistics
• Statistical models
• Machine Learning
• Neural networks

• Meetups and conferences: Seminars, conferences, and technical meetups are a way of interacting with like-minded people and expanding your network.

• Competitions: Implementation is the most important skill in your preparation journey for a data science job. Online competitions help in this aspect.

• Referral: According to a recent survey, the primary source of interviews in the data science organizations are referrals. Be sure to use your networks for this purpose.

• Interview: Interviews are the best way to prepare at the end of the day. Communication skills and other soft skills are polished and you face real-world problems during them. Learn from your failures and work on them.

A data scientist is someone who is responsible for providing the business with a view or visualization of data collected from the user base. These inferences then help the businesses take decisions which are customer-centric. In today’s world where data is exploding, the data scientist’s job is becoming more and more valuable. More data means more information about the customer. The Data scientist can help understand the mindset of a customer and make data-driven decisions for the business.

Data Scientist Roles & Responsibilities:

• Gathering data and turning unstructured data into structured data is relevant from the business point of view.
• After the data is extracted from its source, it is properly organized and analyzed.
• Employ machine learning algorithms, and other tools in order to model the data and make sense of it.
• Perform mathematical and statistical analysis and provide inferences from it to all the stakeholders.

The Data scientist job, as declared by the Harvard Business Review, is the hottest jobs of the 21st century. This is so because data has found its usage across varied industries and data science methods use this data to draw inferences from it and drive the industry according to the customer needs. Obviously, also due to high demand and low supply of well-trained data scientists, the pay for this job is very high and is approximately 36% higher than what other analytics professionals get. The salary a data scientist gets depends on majorly two things:

• Type of company

• Data Science roles and their salaries

Data science is a popular field has many professional groups and associations around the globe but the following are some of the top ones –

• Data Science Association
• International Institute for Analytics (IIA)
• International Machine Learning Society (IMLS)
• Institute for Operations Research and the Management Sciences (INFORMS)
• Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD)
• AAi: Advanced Analytics Institute
• American Statistical Association.
• SIGKDD, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining

• National Center for Data Mining (NCDM)

According to a recent survey, referrals are the primary source of hiring which can be done with the help of a good network. You can network with other data scientists in many ways such as :

• Data science conference
• An online platform like LinkedIn
• Social gatherings like Meetup

Based on the career opportunities in 2019, we have listed down top eight opportunities –

1. Data Scientist
2. Data Architect
3. Data Administrator
4. Data Analyst
5. Business Analyst
6. Marketing Analyst
7. Data/Analytics Manager
8. Business Intelligence Manager

Employers look for more than one characteristic of a candidate, especially in a data science job. We have, therefore, compiled some of the key points which employers certainly look for while hiring data scientists:

• Education: Most of the data scientists hold either a Master’s or Ph.D. degree. So, naturally to get on par with everyone you need to get a degree. It also adds to your profile if you have gathered certifications in the related field.

• Programming: Programming languages like R and Python are popular among data scientists due to the number of tools they provide for data analysis. Therefore, it becomes important to learn the basics of a programming language before learning to implement data science tools and libraries.

• Machine Learning: After the data is preprocessed, ML methods and deep learning is used to model the data and make some sense of it. Without ML skills, you can’t be a data scientist.
• Projects: This is the best way to learn data science. Increase your implementation skills by practicing real-world scenarios. Projects also help to increase the value of your CV.

• Multi paradigm programming language: Python is a multi-paradigm programming language which makes it useful in the field of data science as well. Other than that, Python has a global open source community and loads of tools, packages, and libraries which make it easy to analyze data in this language. It is a structured and OOP language.

• Simple and Readable: Python as a programming language is simple and readable which makes it easy to learn. In addition, there are a number of analytical libraries that Python’s community offers which can be directly used in the project easily. This is the reason why Python is preferred by many data scientists in the first place. The job of a data scientist is not to focus more on programming but use programming as a means to achieve the analysis of the data.

• Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available at the disposal of a data scientist, should he/she get stuck at a particular point or problem while developing a Python program or model for Data Science.
• Global Python community: It is no secret that Python has probably the biggest open source community worldwide among any programming languages. This vast community helps in solving problems as simple as on a coding level to something as complex as creating a library.

Programming is a means to achieve other tasks in the data science field. So, a good programming language would be one that manipulates different kinds of data well and is supported by a big open source community along with libraries and tools. Top five programming languages in the data science field are:

• R: It has a steep learning curve, but the returns are very beneficial as well.

• R has a big open source community which supports it and provides all with all kinds of libraries and tools.
• The biggest feature of R is its mathematical prowess, it solves matrix multiplications at a quick pace and has various statistical functions.
• ggplot2 is the most famous visualization tool offered by R.

• Python: Due to its ease of learning to go along with the plethora of libraries and tools which it provides, Python is one of the most preferred languages by data scientists.

• Pandas, scikit-learn, and sensor flow are some of the most important libraries that are used in almost all data science projects.
• Easy to read the code and write it as well.
• The big open source community helps in more than one ways.

• SQL: A structured query language, SQL works with relational databases.

• It has a readable syntax such that non-technical person can learn it as well.
• Manipulation, updating, and querying of data is done at great speeds in the relational database.

• Java: Although it offers fewer libraries for data science than its counterparts, it has its own advantages:

• Compatibility. Java is an old language with many systems already coded in it. Therefore, it becomes easy for newer programs to be compatible with old systems.
• Java is a high-performance, and a general purpose language making it a candidate for data science projects as well.

• Scala: Even though it has a complex syntax, Scala - run on JVM - is still preferred by data scientists due to the following reasons:

• Scala runs on JVM making it compatible with Java programs as well.
• It provides the best efficiency when used along with Apache Spark.

Follow these steps to successfully install Python 3 on windows:

• Download and setup: Go to the download page and set up your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from the terminal.

Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:

python --version

• Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

python -m pip install -U pip

Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.

You can simply install python 3 from their official website through a .dmg package, but we recommend using Homebrew to install python as well as its dependencies. To install python 3 on Mac OS X, just follow the below steps:

1. Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:

$ xcode-select --install

2. Install brew: Install Homebrew, a package manager for Apple, using the following command:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Confirm if it is installed by typing: brew doctor

3. Install python 3: To install the latest version of python, use:

brew install python

a. To confirm its version, use: python --version

You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.