Live Sessions
Quizzes and Assignments
Data Sets
Case Studies and Projects
Capstone Project
The recent LinkedIn workforce reports state that there are over 151,717 Data Scientist job openings in the USA
According to Glassdoor, the average salary of a data scientist is nearly $121,931 per year
By 2020, there will be a requirement of more than a million data scientists globally
Decisions made by companies are changed according to the data-driven approach by data scientists
Data science, a fascinating and prolific discipline, simplifies the world and makes it a better place.
Being a data scientist will enable you to make a positive impact on society, thus making lives better.
The ever-increasing demand for data around the world makes Data Science a seemingly evolving career option.
This data science bootcamp has been designed for people with prior experience in statistics and programming, such as Engineers, software and IT professionals, analysts, and finance professionals.
Pre-requisitesInteract with instructors in real-time— listen, learn, question and apply. Share opinions and improve your coding skills with assistance from the instructors.
Learn theory backed by practical case studies, exercises, and coding practice. Get skills and knowledge that can be effectively applied.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on all projects and case studies from professional Data Scientists and Architects.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Build a portfolio of real professional projects to demonstrate your abilities and learning
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I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also helped me with real world examples.
The KnowledgeHut course covered all concepts from basic to advanced. My trainer was very knowledgeable and I really liked the way he mapped all concepts to real world situations. The tasks done during the workshops helped me a great deal to add value to my career. I also liked the way the customer support was handled, they helped me throughout the process.
I am glad to have attended KnowledgeHut's training program. Really I should thank my friend for referring me here. I was impressed with the trainer who explained advanced concepts thoroughly and with relevant examples. Everything was well organized. I would definitely refer some of their courses to my peers as well.
Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.
I had enrolled for the course last week at KnowledgeHut. The course was very well structured. The trainer was really helpful and completed the syllabus on time and also provided real world examples which helped me to remember the concepts.
The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.
The teaching methods followed by Knowledgehut is really unique. The best thing is that I missed a few of the topics, and even then the trainer took the pain of taking me through those topics in the next session. I really look forward to joining KnowledgeHut soon for another training session.
I was totally impressed by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. The training session gave me the confidence to do better in my job.
The following are the prerequisites for taking up the Data Science Bootcamp:
If you don’t meet the above-mentioned criteria, you can attend a pre-boot camp workshop, which will help you meet the requirements.
Yes, you need to have prior knowledge as well as coding experience of Python for this Bootcamp. Else, you can opt to attend the pre-boot camp workshop, which will make you ready for the boot camp.
The Data Science Bootcamps conducted are interactive in nature and fun to learn as a substantial amount of time is spent on hands-on practical training, use-case discussions, and quizzes. The Bootcamp classes are Online-instructor based and can be taken part from anywhere in the world, suiting your ease.
The Bootcamp has been divided into three stages:
1. Pre-Bootcamp
Data Science Bootcamps are really a good way to start your journey in the field of data science and the way to fill your knowledge gap. It is a course in which you earn the knowledge required right from the start of your chosen course of study. In order to get admitted to the pre-Bootcamp, we want you to have some basic knowledge of data science and complete a pre-work course before Day 1. A free preliminary course will be provided to you in order to help you with your preparation for Bootcamp. This prework will help you come in prepared and will help you keep pace with the class.
During the pre-Bootcamp workshop, you will have to make sure that you spend at least 100 hrs (25 hrs per week) practicing on the designed curriculum. In addition to this, you will be undergoing 4 hrs of Instructor-Led Sessions every week.
| Number of hours to spend | 100 hours |
| Hours per week | 25 hours |
| Instructor-Led Sessions | 4 hours per week |
Apart from the curriculum, you will also get access to a dedicated Mentor (4 One-on-One sessions in a week), who shall help you in assignments and solving the queries. Also, you will have access to the discussion forum which includes Instructors and Mentors, comprising Alumni’s where you can solve your queries immediately.
After the completion of the pre-Bootcamp workshop, you are required to take up an assessment just to make sure that you have acquired the foundational knowledge to proceed with the Bootcamp.
2. During Bootcamp:
Our Data Science Bootcamp comprises of 120 hours of live sessions along with 300 hours of hands-on learning built on assignments. In the process, you will also be working on 14 case studies and projects to build a portfolio. We shall also be putting you across in competitions.
a. Live Streaming
The Bootcamp is completely instructor-led which will help you to learn the fundamentals of data science and introduce you to the basic functions. Also, you will have daily sessions depending on the batch that you choose and you will have access to the recordings in the learning platform.
b. Practice - Hands-on learning
One of the big advantages of data science Bootcamp is that they typically offer hands-on-learning. Tons of coding and cloud-based machine learning exercises will help you improve. Start working with it as early as possible.
c. Online Learning Platform
You can make your code learning easier with our auto-grading tools. These Auto-grader tools support numerous programming languages like R, Python, MySQL, Bash, and help you in getting instant feedback during work. This saves lots of time as you don’t need to submit the homework manually and wait for the answers.
d. Online Community
Our online community will offer you peer interaction. Our online community platform is carefully designed to facilitate student interaction. We have a community Slack, where you can interact to undertake group projects, ask questions and network with each other and also with other members of the community. You’ll check and learn from others’ codes, and build the team skills needed in a real working environment.
e. Mentoring Support
A dedicated mentor will be assigned to you who will help you out in assignments, cracking the coding challenges, etc. Get your code and assignments reviewed timely by a dedicated mentor to make sure it’s accurate and aligned to industry standard. Our dedicated mentor is also available on Online Community where you can clarify doubts.
3. Post-Bootcamp:
You will get access to all the webinar series that we run with Data Science experts around the globe once you are done with the Capstone Project. Also, the access to the community platform is also unlimited and can get answers to queries you have even after graduation
In addition to other perks like help and support, You will also receive access to the Career Counseling Center, who shall help you with the following valuable career counseling like:
For the Data Science Bootcamp, you will have to devote 120 hours for instructor-led sessions and 300 hours of hands-on assignments, quizzes and assignments, projects, and case studies. It is required to spend 8 to 10 hours on instructor-led sessions per week and 20 hours on practice sessions.
| Type | Total No of hours | No of Hours per week |
| Instructor-led sessions | 120 hrs | 8 to 10 hours |
| Hands-on sessions (assignments, quizzes, projects and case studies) | 300 hrs | 20 hours |
The instructors and mentors at the Data Science Bootcamp are extremely qualified industry practitioners who have years of relevant industry experience.
Instructors will take you through the live sessions whereas Mentors will be assigned to a specific person and will help the participant one-on-one on various assignments, projects, and challenges. Moreover, they will help you to overcome the challenges that you might face.
No. Although we are partners with many companies who are hiring data scientists, we do not guarantee any job placements. It purely depends on the individual and the decision made by the hiring company. We do not offer any job placements.
But we do have career support services where the counselors and mentors would help you in CV/Resume preparation, Linkedin/Github Profiling, Portfolio, Mock Interviews, and etc.
Yes, an individual with a non-technical background can also take this course if that individual has a passion for solving problems, loves coding and mathematics. An individual just needs a bit of knowledge about a specific business or industry to grasp the concepts.
No, it is not at all mandatory to participate in Challenges/Hackathons. But, it is highly recommended to participate in such events where you can get lots of hands-on practice to grow your coding skills.
Well, spotting out the mistakes and correcting them, this is how we learn and grow. We follow Agile practices and pair programming during project development. You don’t need to worry, as you will always be getting support from our trainers and mentors to take you out of the problems.
The training conducted is interactive in nature and easy to learn, focusing on hands-on practical training, use case discussions, and quizzes. In order to improve your online training experience, our trainers use an extensive set of collaborative tools and techniques.
You can attend the training and learn from anywhere in the world through the more preferred, virtual live and interactive training.
It is live and interactive training led by an instructor in a virtual classroom.
You will receive a registration link to your email id from our training delivery team. Just log in from your PC or other devices.
There would be a maximum of 8 participants in each workshop.
If it happens that you miss a class, then you can opt for any of the following two options:
Yes, we do have an installment option available for the course fees. To avail installments, please get in touch with us at support@knowledgehut.com. Our dedicated team will help you with how installments work and would provide the timelines for your case.
Cancellation
If for any reason, you are unable to attend the course and want a refund prior to the course commencement date, we will gladly refund the full amount.
Withdrawal
If you want to discontinue within the first 2 days, we will still proceed with the 100% refund.
Transfer
We would also be happy to transfer your registration to another bootcamp. In such a case, refund cannot be processed.
In case you are unable to attend the course don't worry! We will be happy to give you back the full amount prior to the course commencement date. And suppose if you want to discontinue within the first two days of Bootcamp we will still proceed with the 100% refund.
Yes, for a group of 3-5 participants a discount of 15% is available.
After completion of the Bootcamp, we will provide you with career services, where you can interact with our mentors in order to seek guidance for profile building. Our mentors will be there for your support on Slack even after the Bootcamp has been concluded. Moreover, you can get your projects reviewed by them, and work with them toward building a better CV/Resume.
Individuals who graduate from our boot camps are prepared for jobs such as data scientist, data engineer, and data analyst and can find employment in almost any industry.
Attendees will receive a certificate of completion. But, it will be given only upon completing the final Capstone project and meeting certain attendance and code quality criteria.
More than certification, it is the core skills and portfolio that would be of more help to you which will also help you advance in your career.
Knowledgehut trainers are remarkably qualified industry experts having several years of relevant industry experience. Our unlimited mentored support will help you understand the concepts in-depth and overcome the challenges you may face. Following are the various career support you will receive:
According to Josh Wills, Director of Data Engineering at Slack, “Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician”. In simple terms, Data science can be explained as the science of making data useful. It is a fusion of machine learning principles, algorithms, and various tools for the identification, representation, and extraction of useful and meaningful information from a pool of data.
Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. It is the most in-demand career paths for skilled professionals, however, is being challenged by the dire shortage of talent. In August 2018, LinkedIn reported that there's a shortage of 151,717 people with data science. According to a recent study revealed by Indeed, demand for data scientists continues to grow, as the average salary for a data scientist is around $100,000. The value of this specialized field is evident in its huge demand and high pay.
Best Data Science Boot camps consists of the following :
Sr. No | Name of the Bootcamp | Rating | Cost | Location |
| 1 | 4.62 | $14,950 for full- and part-time boot camps, $5,500 for a la carte options. | New York City, Bangalore and online | |
| 2 | 4.22 | Free with limited access or $29 per month/$25 per year for an unlimited subscription | Online | |
| 3 | 4.71 | Free for those accepted | Boston, Washington (D.C.) and online | |
| 4 | 4.88 | $3,000 | Seattle, Silicon Valley, Washington (D.C.), Paris, Chicago, Toronto, New York City, Barcelona, Amsterdam, Austin and Singapore | |
| 5 | 4.96 | Free with limited access, $29 per month for a basic account and $49 per year for a premium account | Online | |
| 6 | 4.21 | $16,000 for the 13-week course | Denver, San Francisco, Boulder, Seattle, Austin, Phoenix and New York City | |
| 7 | 4.18 | Payment varies depending on the program you choose, but financing options are available | Boston, London, Los Angeles, New York City, San Francisco, Sydney, Washington (D.C.) and online | |
| 8 | Level | 4.41 | Varies by programming, location and the schedule you choose | Boston, Charlotte, Seattle, San Jose, Toronto and online |
| 9 | 4.88 | $2,350 for in-person professional development $1,900 for Live Online professional development courses. | Chicago, New York City, Seattle, San Francisco and online | |
| 10 | 4.92 | $17,600 | New York City and online | |
| 11 | 4.89 | $7,500 total, with month-by-month payment options | San Francisco and online | |
| 12 | 4.79 | $7,999 upfront, or $1,495 per month with the option for loans, financing and other payment plans | Washington (D.C.), Portland, Dallas, Los Angeles, Phoenix, San Diego, Atlanta and online | |
| 13 | NA | Free for those accepted | Chicago | |
| 14 | NA | Free | Boston, Washington (D.C.) and online | |
| 15 | 5 | Free for those accepted | Silicon Valley, New York, Boston, Seattle | |
| 16 | NA | $12,000 | Online | |
| 17 | 4 | Free for those accepted, a $5,000 stipend and a Microsoft-provided laptop that you can keep. | New York City |
Before we move forward to the list of the best Bootcamps, let’s look at what benefits you’ll reap from a boot camp
Now that we are well aware of the benefits, let’s proceed to the best boot camps available in the data science industry -
"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."
"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 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."
"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."
Combination of offline and online course -
"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 "
"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."
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 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
"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."
Classes.
"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)
"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."
"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."
"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."
"The learning rate is tremendous. Data Science Retreat has pushed me to my limit and beyond"
"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."
"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!!"
“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.”
"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
“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”
“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.”
“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:-
Cons:-
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 student needs to be in the 1st year of either Masters’ or Ph. D.
“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.”
“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.
Data Scientist Salary in States | ||
State | Hourly Wage | Annual Salary |
New York | $58.75 | $122,194 |
| Massachusetts | $58.48 | $121,634 |
| Maryland | $55.67 | $115,791 |
| California | $54.85 | $114,080 |
| Nebraska | $54.23 | $112,805 |
| Vermont | $54.03 | $112,385 |
| Alaska | $53.85 | $112,000 |
| Nevada | $53.85 | $112,000 |
| Montana | $53.85 | $112,000 |
| North Dakota | $53.85 | $112,000 |
| Wyoming | $53.85 | $112,000 |
| Idaho | $53.85 | $112,000 |
| West Virginia | $53.80 | $111,899 |
| Hawaii | $53.67 | $111,633 |
| Washington | $53.52 | $111,318 |
| Virginia | $52.77 | $109,759 |
| Delaware | $52.75 | $109,725 |
| Connecticut | $52.65 | $109,505 |
| Arizona | $52.51 | $109,215 |
| Rhode Island | $52.17 | $108,520 |
| New Hampshire | $52.05 | $108,270 |
| Minnesota | $51.36 | $106,832 |
| Pennsylvania | $51.10 | $106,293 |
| Oregon | $50.95 | $105,968 |
| South Dakota | $50.87 | $105,801 |
| Louisiana | $50.85 | $105,772 |
| Colorado | $50.85 | $105,760 |
| Kansas | $50.83 | $105,721 |
| Kentucky | $50.63 | $105,306 |
| South Carolina | $50.62 | $105,295 |
| Tennessee | $50.51 | $105,063 |
| Iowa | $50.50 | $105,040 |
| Ohio | $50.15 | $104,320 |
| Oklahoma | $50.05 | $104,106 |
| New Jersey | $50.01 | $104,014 |
| Indiana | $49.96 | $103,926 |
| Utah | $49.52 | $103,001 |
| Wisconsin | $48.81 | $101,519 |
| Alabama | $48.74 | $101,387 |
| Georgia | $48.55 | $100,979 |
| New Mexico | $48.22 | $100,299 |
| Texas | $47.97 | $99,783 |
| Mississippi | $46.93 | $97,622 |
| Maine | $46.93 | $97,617 |
| Missouri | $46.77 | $97,275 |
| Michigan | $46.66 | $97,058 |
| Illinois | $46.60 | $96,927 |
| Arkansas | $46.32 | $96,347 |
| Florida | $45.35 | $94,335 |
| North Carolina | $42.45 | $88,301 |
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.
Data Scientist Salary in Cities | ||
City | Hourly Wage | Annual Salary |
| San Francisco, CA | $62.87 | $130,776 |
| San Jose, CA | $60.50 | $125,837 |
| New York City, NY | $58.75 | $122,194 |
| Seattle, WA | $58.27 | $121,194 |
| Boston, MA | $58.14 | $120,930 |
| Arlington, VA | $57.65 | $119,903 |
| Washington, DC | $57.33 | $119,248 |
| Los Angeles, CA | $56.84 | $118,222 |
| Fairfax, VA | $56.16 | $116,812 |
| Irvine, CA | $55.59 | $115,624 |
| Baltimore, MD | $55.18 | $114,777 |
| Chicago, IL | $55.16 | $114,723 |
| Minneapolis, MN | $55.07 | $114,551 |
| San Diego, CA | $54.93 | $114,248 |
| Modesto, CA | $54.44 | $113,239 |
| Saint Paul, MN | $54.39 | $113,127 |
| Dallas, TX | $54.23 | $112,805 |
| Atlanta, GA | $54.16 | $112,654 |
| Denver, CO | $54.09 | $112,505 |
| Calgary, AB | $53.85 | $112,000 |
| Ottawa, ON | $53.85 | $112,000 |
| Montreal, QC | $53.85 | $112,000 |
| Philadelphia, PA | $53.80 | $111,895 |
| Houston, TX | $53.65 | $111,582 |
| Portland, OR | $53.61 | $111,514 |
| Milwaukee, WI | $53.36 | $110,998 |
| Santa Ana, CA | $53.18 | $110,617 |
| Irving, TX | $52.85 | $109,921 |
| Vancouver, BC | $52.68 | $109,578 |
| Kansas City, MO | $52.58 | $109,365 |
| Columbus, OH | $52.47 | $109,139 |
| Raleigh, NC | $52.35 | $108,887 |
| Plano, TX | $52.16 | $108,503 |
| Charlotte, NC | $52.13 | $108,431 |
| Austin, TX | $52.12 | $108,405 |
| Nashville, TN | $52.09 | $108,343 |
| St. Louis, MO | $51.96 | $108,084 |
| Pittsburgh, PA | $51.74 | $107,609 |
| Phoenix, AZ | $51.71 | $107,547 |
| North Las Vegas, NV | $51.50 | $107,111 |
| Miami, FL | $51.41 | $106,926 |
| Fort Worth, TX | $51.37 | $106,847 |
| Cleveland, OH | $51.02 | $106,123 |
| Toronto, ON | $50.85 | $105,767 |
| Shreveport, LA | $50.63 | $105,315 |
| Mississauga, ON | $50.24 | $104,502 |
| Tampa, FL | $50.00 | $104,002 |
| Orlando, FL | $49.60 | $103,165 |
| San Antonio, TX | $48.49 | $100,849 |
| Tallahassee, FL | $47.73 | $99,273 |
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:
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:
For any successful data scientist, below are the 4 essential behavioral traits that one must have -
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-
We have provided below 3 educational paths for you if you are aiming to become a data scientist:
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.
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.
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.
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.
R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark
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.
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.
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.
R, Python, HTML, Javascript, and SQL.
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.
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.
He must have an active and intriguing mindset. He must also enjoy working with the designs and patterns of the data architecture.
SQL, XML, Hive, Pig, Spark.
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.
Using their vast knowledge and statistical methods, they assemble, dissect, and decipher the data.
Their interpretation is both quantitative as well as qualitative.
A statistician needs a logical mindset to accomplish his task.
R, SAS, SPSS, Matlab, Stata, Python, Perl, Hive, Pig, SQL, Spark
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.
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.
SQL, Java, Ruby on Rails, XML, C# and Python
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.
The job demands a flexible mindset which allows them to be the middleman between the IT sector and business.
SQL, Java, Ruby on Rails, XML, C# and Python
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.
Due to past experience in computer programming and now dealing with data, a successful data engineer should have the mindset of an all-rounder.
SQL, Hive, Pig, R, Matlab, SAS, SPSS, Python, Java, Ruby, C++, Perl
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.
Being a manager, they must be optimistic and visionary.
SQL, R, Matlab, SAS, Python, Java.
As per LinkedIn Salary, the base salary is reported to be $1,07,000/yr. The range differs between $75,000 - $1,42,000.
Data Analyst vs Data Scientist |
Roles |
A data analyst has to accumulate data. More often these data consists of raw data which cannot be used unless processed. Therefore, the data analyst processes this data before analyzing them statistically. | A data scientist has to clean up the data, process and analyze it in order to gain useful insights from the data. |
Responsibility |
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. | Using machine learning techniques, A data scientist should be able to pick features, creating and optimizing 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 |
$60,000 per annum | $105,000 per annum |
Data Analyst vs Data Architect |
Roles |
A data analyst has to accumulate data. More often these data constitutes of raw data which cannot be used unless processed. Therefore, the data analyst processes this data before analyzing them statistically. | A data architect must be able to create blueprints for data. This allows them to manage and integrate the data into the system. The management skill must be effective enough to centralize, protect and maintain the data source. |
Responsibility |
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. | Ability to identify a structural solution to maintain a company’s database. Work with other data scientists, such as 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 |
$60,000 per annum | $121,000 per annum |
Data Engineer vs Data Scientist |
Roles |
A data engineer should be able to develop, construct, maintain and implement architectures. He should also have the ability to design database and large-scale processing systems. | It is the data scientist who has to clean it up, process and analyze in order to gain useful insights from the data. |
Responsibility |
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. | Using machine learning techniques, he should be able to pick features, creating and optimizing 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 |
$98,000/yr per annum | $105,000 per annum |
Data Engineer vs Data Architect |
Roles |
A data engineer should be able to develop, construct, maintain and implement architectures. He should also have the ability to design database and large-scale processing systems. | 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. |
Responsibility |
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. | 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 |
$98,000/yr per annum | $121,000 per annum |
Below are the steps which you can follow in order to become a successful, top-notch data scientist:
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.
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 –
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.
You have ... | Points |
Strong STEM (Science/ Technology/ Engineering/ Management) background | 0 |
| Weak STEM background (Biochemistry/ Biology/ Economics or similar degree/diploma) | 2 |
| Non-STEM background | 5 |
| Less than 1 year of experience in Python | 3 |
| Never been a regular coder as part of your job | 3 |
| Less confidence in independent learning | 4 |
| Not understanding when we say that this table format is itself an application of regression algorithm | 1 |
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:
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.
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:
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:
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:
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
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.
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:
Advanced Level:
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: 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:
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:
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 | Pay |
Startups | Highest |
Public | Medium |
Governmental and Education Sector | Lowest |
Role | Salary |
Data scientist | ₹6,50,000/yr |
Data analyst | ₹4,05,000/yr |
Database administrator | ₹6,48,987/yr |
Data science is a popular field has many professional groups and associations around the globe but the following are some of the top ones –
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 :
Based on the career opportunities in 2019, we have listed down top eight opportunities –
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:
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:
Follow these steps to successfully install Python 3 on windows:
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
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.
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