Data Science with Python Training in Atlanta, GA, United States

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

• Anyone Interested in the field of data science
• Anyone looking for a more robust, structured Python learning program
• Anyone looking to use Python for effective analysis of large datasets
• Software or Data Engineers interested in quantitative analysis with Python
• Data Analysts, Economists or Researcher

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

• Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
• 4 GB RAM
• 10 GB of free space

Software Requirements

• Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

• 32 or 64-bit Operating System
• 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

Yes, you can switch the start date to your Data Science with Python training, with prior notice of at least 24 hrs and subject to availability in the desired batch.

Yes, we have a 100% money-back guarantee in line with our 100% Satisfaction Guarantee policy.

Yes, scholarships are available for students and veterans with grants up to 50% of the course fees.

To avail scholarships for our Data Science with Python course, please get in touch with us at support@knowledgehut.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, our panel of experts take a decision on the Grant. The entire process could take about 7 to 15 days.

Yes, group discounts are available and apply to groups as small as three (3) participants. The more participants that attend a training course, the greater the discount. By registering in groups, you can typically save up 20% to 30% on the course fee. For more details, please check out upcoming schedules.

Yes, instalment options are available for payment of course fee. To avail the instalment option, please get in touch with us at support@knowledgehut.com. The team will explain how the instalments work and provide timelines for your case.

Typically, the number of instalments vary from 2 to 3, but the full amount must be paid before you complete the course.

Atlanta, Georgia is home to several renowned institutions like Georgia Institute of Technology, Georgia State University that offers Master’s degree in Data Science. There are also other options available like certification courses and bootcamp, that will help you learn at your own pace.

A qualified data scientist is expected to be an expert in the following technical skills - 

1. Python Coding: Python is undoubtedly one of the most popular and preferred programming languages used in the data science field. It can take various data formats and aids in the data preprocessing. It is very simple and versatile that gives it an advantage over other programming languages. It also allows data scientists to create and perform operations on dataset. 
2. R Programming: If you want to make a data science problem easy to solve, you need to have the knowledge of R programming. To become a master data scientist, you need to have a comprehensive knowledge of this analytical tool. 
3. Hadoop Platform: Knowledge of Hadoop platform is not a must for data science, but it is used in several data science projects. So, it will be better if you have knowledge of the Hadoop platform. 
4. SQL database and coding: SQL, or Structured Query Language, is used by the data scientist for accessing, working, and communicating data. This helps the data scientist gain an understanding of the structure and formation of a database. MySQL is another such language with concise commands that saves time and does not require an expert technical skill level for performing operations on the database. 
5. Machine Learning and Artificial Intelligence: To be a successful data scientist, one needs to be proficient in Machine Learning and Artificial Intelligence. As a potential data scientist, you must make yourself efficient with topics like neural networks, decision trees, logistic regression, reinforcement learning, adversarial learning, machine learning algorithms, etc. 
6. Apache Spark: Used as a data sharing technology, Apache Spark is like Hadoop where it is used for big computation. It is faster than Hadoop. This is because Spark caches its computations in the memory of the system whereas Hadoop reads and writes to the disk. So, it is used for running the data science algorithms faster. Apache Spark also helps in disseminating data processing. It is well equipped to deal with large datasets and handle complex unstructured data. Unlike Hadoop, it can prevent loss of data. The most important factor of why it is so preferred in the field of Data Science is the speed and ease with which it allows the data scientists to carry out the project. 
7. Data Visualization: Once the data has been analyzed, a data scientist has to be able to present this in a form that is understandable to the non-technical members of the team. There are several visualization tools available for this purpose like Tableau, ggplot, d3.js, and matplotlib. Complex results are converted into a format that is easy to understand and comprehend. These results are obtained after a series of processes performed on a dataset. Data visualization also allows the organization to work with data. Data scientists can easily grasp the insights from the data and act on the new outcome. 
8. Unstructured data: Most of the data that is generated is unlabelled, not organized into databases values, and unstructured. This unstructured data includes blog posts, audio samples, videos, customer reviews, social media posts, etc. 

Below are the top 5 essential behavioral traits of a successful Data Science professional-

• Curiosity – If you live for 3Ws like Why What and Where, then data science is for you. 
• Inventiveness - Creativity in data science can be anything from discovering innovative ways to deal with data to develop new tools. You ought to presumably get a handle on what's missing and what ought to be joined to get results. 

A lot of local companies in Atlanta, GA have started using data science to help in improving the efficiency and optimizing their business. Companies like Aderant, Sparity Inc, Neudesic, Nexidia, Innovative architects, BI Brainz, PredictX, Keystone Solutions etc, are currently hiring data scientists. So, needless to say, data scientists will be in demand in Atlanta, GA. Below are some other top advantages of being a Data Scientist -  

1. High Pay: As a result of high demand and low supply, data science career is paying better as compared to other choices.
2. Huge rewards: Despite higher pay, data scientists can expect huge bonuses. 
3. Training: You need to have a Master's degree or a Ph.D. to become a data scientist. There is a huge demand for knowledge in this field. So, you can try getting a job as a lecturer or a researcher in a government or private institution.
4. Mobility: A data scientist job can get you a job in one of the developed countries, which means hefty salary and improved living standards. 
5. Network: A data scientist gets to network with other professionals in the tech world through conferences, tech talks, and other platforms. 

• Training camps: Training camps are the ideal pathway to embark on your Python journey. In Atlanta, GA, a number of institutions organize boot camps to help the students kick start their career as a Data Scientist. DigitalCrafts: Atlanta Coding Bootcamp and Georgia Tech Data Science and Analytics boot camp are some examples of boot camps in Atlanta.
• MOOC courses: These are online courses which teach the most recent models in the business.
• Affirmations: The next step is to get some certifications that will help you build your portfolio. Here are some of the data science certifications that you can go for:
• Cloudera Certified Associate - Data Analyst
• Cloudera Certified Professional: CCP Data Engineer
• Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
• Projects: The more projects you work on, more refined your skills and thinking will be. You need to find new solutions to already solved problems while following the project constraints.
• Competitions: You can try participating in competitions like Kaggle that improves your problem-solving skills by making you find a solution that fulfills all the requirements. 

We live in a world of data. And now slowly, different industries have started to realize the importance and benefits of using data science for optimizing their business. Atlanta, GA is home to many such companies that are hiring data scientists either for their own or to use as a third party solution including TopRight, Stridely solutions, HatchWorks Technologies, Quatrro, Six Consulting, Inc., 7Factor Software, Aderant, Sparity Inc, Neudesic, Nexidia, Innovative architects, BI Brainz, PredictX, Keystone Solutions, etc.

Practicing and working is the best way to master any skills. Similarly, you need to work your way through the data science problem as well. Here are a few ways categorized on the basis of difficulty and expertise level:

• Beginner Level
  • Iris Data Set: This dataset consists of 4 columns and 50 rows. It is one of the most popular, easy, resourceful, and versatile datasets available for pattern recognition. You will be able to learn various classification techniques while dealing with this dataset. This is a great dataset for beginners to start in the field of data science.Practice Problem: Use the parameters to predict the class of a flower.
  • Loan Prediction Data Set: Consisting of 13 columns and 615 rows, this dataset is from the banking domain which uses more data science methodologies and data analytics than any other industry. With the loan prediction dataset, you will have to work with concepts used in the banking and insurance domain like strategies implemented, variables that can affect the outcome, and challenges faced. It is a classification problem dataset.Practice Problem: Predicting if a certain load will be approved or not.
• Intermediate Level:
  • Black Friday Data Set: This is a large dataset with 12 columns and 550,069 rows. This dataset is a regression problem consisting of sales transactions of customers in a retail store. If you want to understand the daily shopping experience of millions of customers and at the same time explore and expand your engineering skills, this is an apt dataset for you. Practice Problem: The problem is predicting the total purchase amount.
  • Human Activity Recognition Data Set: Consisting of 561 columns and 10,299 rows, the human activity data set was collected from 30 human subjects via smartphone’s recordings. These smartphones were embedded with inertial sensors.Practice Problem: The problem is predicting the category of human activity.
  • Text Mining Data Set: With 30,438 rows and 21,219 columns, this data set is a multi-classification, high dimensional problem dataset that was collected during the 2007's Siam Text Mining competition. The dataset had safety reports of aviation describing problems that occurred during the flights. Practice Problem: The problem is using the labels to classify the documents.
• Advanced Level:
  • Urban Sound Classification: There are several basic and simple machine learning problems like the Titanic survival prediction that you will study in the beginning. But these problems do not help you get familiar with the real world problems. This is where urban sound classification comes in. It will help you implement machine learning skills to real-world problems. It also includes the use of audio processing for real-world scenarios of classification. It has 8,732 sound clippings belonging to 10 different classes. Practice Problem: The problem is identifying particular audio and classifying it to its class.
  • Vox Celebrity Data Set: This dataset introduces you to the usage of audio processing in deep learning. This dataset is a large scale speaker identification problem consisting of 100,000 words spoken by 1,251 celebrities. This dataset is extracted from YouTube videos. It is a great example to practice isolating and identifying speech.Practice Problem: The problem is identifying the voice of the celebrity.

• Degree/certificate: There are several institutions in Atlanta, GA that offer Master’s degree in Data Science. It is important that you start with covering your fundamentals through a basic course. This can be either an online or an offline course. You will be able to learn the application of cutting-edge tools that will help you get a tremendous career growth. The field of Data Science demands continuous learning due to the rapid advancements. So, Data Scientists have more PhDs than any other job in the IT industry. 
• Unstructured data: The most important part of the roles of a Data Scientist is the identification of patterns in the data. Usually this data is unstructured and can’t be fit into a database. Structuring this data takes a lot of work and makes the job a lot more complex. As a data scientist, you must have the ability to understand and manipulate the data.  
• Software and Frameworks: As a Data Scientist, you would have to deal with large volumes of unstructured data. For this, you need to be comfortable in using a programming language, software and frameworks involved in Data Science. 
  • R is one such programming language that has a steep learning curve. It is one of the most used programming languages for finding solutions to statistical problems. It is the preferred language for the analysis of about 43% of Data Scientists. 
  • When the amount of data to be analyzed is way too large as compared to the available memory, the majority of Data Scientists use the Hadoop framework. Hadoop is capable of conveying data to various points on the machine. After Hadoop, Spark is a popular choice of framework. It is a faster option for computational work. Unlike Hadoop, it prevents the loss of data. 
  • Once you have a complete understanding of the programming language and the framework, you need to get a thorough knowledge of databases. A Data Scientist must be proficient in SQL queries. 
• Machine learning and Deep Learning: Once you have collected and prepared the data, the next step is the application of Machine Learning algorithms on the data for the analysis. Deep learning is used to train the model to work with the provided data.
• Data visualization: Most of the data science projects involve making business decisions after analyzing and visualizing the data. It is the job of a Data Scientist to analyze the data and provide it to the management in the form of charts and graphs. There are several tools available for data visualization like ggplot2, matplotlib, etc. 

The importance of degree in the field is summarized below:

• Networking: Networking is important in all fields and it can be developed while pursuing degrees.
• Structured education: Having a structured curriculum and a schedule to follow is always beneficial.
• Internships: These allow much needed practical experience
• Qualification for CVs: Earning a degree from a reputed institution is always helpful for your career.

There are many colleges in Atlanta, GA that offer Master’s degree in Data Science like Georgia Institute of Technology and Georgia State University. However, before selecting the college, you need to figure out if you need a master’s degree or not. The most ideal approach to decide if you need a Masters in Data Science is by reviewing yourself on the scorecard underneath. The given scorecard can help you figure that out. You should get the degree if you get over 6 points in total: 

• A strong background in STEM (Science/Technology/Engineering/Management)- 0 point.
• Weak STEM background, such as biochemistry, biology, economics, etc.- 2 points
• Non-STEM background- 5 points
• Python programming experience less than 1 year in total- 3 points
• No job experience in coding- 3 points
• Lack of capability to learn independently- 4 points
• Not understanding that this scorecard follows a regression algorithm- 1 point.

Knowledge of programming is the most fundamental and important skills required to become a data scientist. Here is why:

• Data sets: Working in data science involves dealing with huge volumes of datasets. To analyze these datasets, knowledge of programming is essential.
• Statistics: Knowledge of statistics is a must to analyze the data. But what good would be this knowledge if you don’t have the programming knowledge to implement it? 
• Framework: If you are proficient in programming, you will be able to build systems and frameworks that can help the organization automatically analyze experiments. This is also required to maintain the data pipeline and visualize the data.

The annual income of data scientist in Atlanta and New York is $88,603 and $99,716 respectively, with a difference of $11,113.

The data scientists earn an average of $88,603 in Atlanta as compared to $110,925 in Chicago.

The average income of a Data Scientist is $77,379 per year in Jersey, Georgia.

In Georgia, the demand for a Data Scientist is quite high. There are several firms that have just started using Data Science and are looking to build a Data Science team that will help them make better business decisions by analyzing their raw data.

Following are the benefits of being a Data Scientist in Atlanta:High SalaryJob growthMultiple job opportunityChance of being an integral part of the team from the start

Data Scientists have a few perks and advantages over other jobs. More often than not, they get to gain attention of the upper level management due to their involvement in getting business insights after the analysis of raw data. Also, they get to pick their field of work as Data Science has spread its roots in almost every field that exists.

Cox Automotive Inc., Charter Global and Norfolk Southern Corporation are among the companies hiring Data Scientists in Atlanta.

1. Southern Data Science Conference, Atlanta

• About the conference: This event was organized by southern data scientists to bring together data scientists from around the country to showcase and discuss their latest research and development in data science.
• Event Date: 7 April, 2017
• Venue: Hyatt Regency Atlanta Perimeter, 4000 Summit Blvd NE · Atlanta, GA
• Days of Program: 1
• Timings: 7 A.M. to 6 P.M.
• Purpose: The purpose of the conference was to provide a platform for experts in data science to demonstrate their innovations in the areas of data science.

2. MLconf Atlanta: The Machine Learning Conference, Atlanta

• About the conference: The conference helped the attendees in learning the latest trends in machine learning.
• Event Date: 15 September, 2017
• Venue: The Academy of Medicine
• Purpose: The purpose of the conference was to discuss the latest research in machine learning techniques and practices, application of tools, algorithms, and platforms to solve the issues pertaining to data sets.
• How many speakers:18
• Speakers & Profile:
  • Alexandra Johnson - Software Engineer, SigOpt
  • Anna Kiefer - Software Engineer, Kevala
  • Aran Khanna - Software Engineer, Amazon Web Services
  • Ashrith Barthur - Security Data Scientist, H20.ai
  • Greg Werner - Co-Founder / CEO, 3blades
  • Jennifer Marsman - Principal Developer Evangelist, Microsoft
• Who were the major sponsors:
  • 3blades
  • Airbnb
  • Amazon
  • CRC Press
  • H2O.ai
  • HiringSolve

3. Big Data Technology Workshop and 2017 HPCC Systems Summit Community Day, Atlanta

• About the conference: The Conference brought together data scientists, professionals, and engineers to discuss the use of HPCC Systems Platform and ECL, to solve issues related to data analytics.
• Event Date: October 3-4, 2017
• Venue: Ritz Carlton Buckhead, 3434 Peachtree Rd NE, Atlanta, GA 30326, USA
• Days of Program: 2
• Timings: 9:00 am - 4:00 pm
• Purpose: The purpose of the conference was to develop a better understanding of the HPCC Systems Platform through research projects, and use cases and also to impart knowledge on the use of ECL.
• Registration cost: $100 for each day

4. 7th Global Tech Mining Conference, Atlanta

• About the conference: The conference showcased presentations on development of new methods, enhanced software tools, and case studies in the field of Big Data.
• Event Date: October 10-12, 2017
• Venue: Georgia Tech Global Learning Center 84 Fifth Street N.W. Atlanta, GA 30308-1031
• Days of Program: 3
• Purpose: The purpose of this conference was to connect researchers, analysts, managers, policymakers, and software specialists to enhance the use of textual information in various fields, and discuss the challenges faced in Text-mining tools and methods, Data, and Applied research.

5. DataSciCon: Data Science, Data Analytics, Machine Learning, and Big Data conference, Atlanta

• About the conference: The conference conducted workshops on Data Science with R Workshop, Data Analytics with Tableau, and Introduction to Machine Learning with Python and TensorFlow followed by sessions to develop a better understanding of Machine Learning, Data Visualisation, Artificial Intelligence, Deep Learning, Data Science, and Big Data.
• Event Date: November 29, 2017, to December 1, 2017
• Venue: Georgia Tech Global Learning Center, 84 5th St NW, Atlanta
• Days of Program: 3
• Purpose: The purpose of this conference was to bring together data analysts, data scientists, and data engineers and impart knowledge on technical aspects of Machine Learning, Data Science, Artificial intelligence, Data Analytics, Big Data, and Deep Learning and their applications.

6. IIA 2018 Analytics Symposium, Atlanta

• About the conference: This Conference brought together innovative speakers to discuss the latest and upcoming trends and analytics and the challenges faced. 
• Event Date: 10 October, 2018
• Purpose: The purpose of this conference was to bring together IIA’s clients, advisory network members, and analytics experts to share their opinions and ideas related to the latest and upcoming trends in the analytics industry.
• Speakers & Profile:
  • Paul Ballew
  • Abhi Seth
  • Alex Barclay
  • Tom Davenport and Kathy Koontz
  • Sameer Chopra
  • Jennifer Priestley
  • Mano Mannoochahr
• Who are the major sponsors:
  • Workiva
  • PWC
  • EY
  • KPMG
  • RSM
  • Protiviti
  • RGP

7. Southern Data Science Conference, Atlanta

• About the conference: This event was organized by southern data scientists to bring together data scientists from around the country to showcase and discuss their latest research and development in data science.
• Event Date: 13-14 April, 2018
• Venue: Atlanta Marriott Buckhead Hotel & Conference Center 3405 Lenox Rd NE, Atlanta, GA 30326· Atlanta, GA
• Days of Program: 2 
• Purpose: The purpose of the conference was to provide a platform for experts in data science to demonstrate their innovations in the areas of data science.

Follow the below steps to increase your chances of success for the job of Data Scientist-

• Study: To qualify for an interview, learn all quintessential topics, including-
• Probability
• Statistics
• Statistical models
• Machine Learning
• Understanding neural networks
• Meetups and conferences: Begin developing your system or expanding your connections by visiting Tech meetups and data science gatherings. 
• Contests: Grasp, test and keep refining your aptitudes by looking into online competitions like Kaggle.

The responsibility of a data scientist is to analyze the vast amount of structured and unstructured data, look for patterns, and inference information. This is done in order to meet the needs and goals of the business.  

Today, tons of data is generated every day and this has increased the importance of a Data Scientist. This is because the generated data is filled with ideas and patterns that can help in the advancement of the business. It is the job of a Data Scientist to study the data, extract the relevant information and make sense of this data so that it can benefit the business. 

Data Scientist Roles & Responsibilities:

• First, the data, relevant to the business, is fetched. This data can be structured as well as unstructured. 
• Next comes the organization and analysis of the data. 
• After this, programs, tools, and Machine Learning techniques are created to make sense of the data.
• Lastly, statistical analysis is performed on this data to predict future outcomes.

The average salary for a Data Scientist is $101,323 per year in Atlanta, GA.

The Data Science career path can be explained in the following way:

• Business Intelligence Analyst: A Business Intelligence Analyst’s job is to figure out the latest trends of the business and the market. This can be done by analyzing the data to get a clear picture of where their business stands in the market. 
• Data Mining Engineer: The job of a Data Mining Engineer is to examine the needs of the business by studying the data. They also do the job as a third party. Apart from this, a Data Mining Engineer is also responsible for aiding in the data analysis by creating a sophisticated algorithm. 
• Data Architect: A Data Architect is responsible for working alongside system developers, designers, and users for creating blueprints. These blueprints are then used by the data management system for integrating, maintaining, centralizing and protecting the data sources. 
• Data Scientist: A Data Scientist’s job is the pursuit of a business case after analyzing the data, developing hypotheses and an understanding of data required for exploring patterns. Next, they also create the system and the algorithm for the productive use of the data. This furthers the interests of the business. 
• Senior Data Scientist: The role of a Senior Data Scientist is anticipating the needs of the business in the future. They are also responsible for shaping the system, data analysis and the projects to suit the needs of the business. 

• Atlanta Big Data User Group
• Big Data Developers in Atlanta
• Data Science ATL
• Data Science & Business Intelligence Society of Atlanta
• Startup Atlanta Meetups

The best way to obtain a job in Atlanta, GA is through Referrals. Some of the additional ways to network with data scientists are:

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

We have compiled the key points, which the employers generally look for while hiring data scientists:

• Education: A Bachelor’s degree is a must in Computer Science or related fields. Many institutes are also introducing special courses on data science. Having a Master’s or a Ph.D. in Data Science will boost up your profile and will help you get recognition among various candidates. 
• Programming: There are no prerequisites for learning a programming language like Python, aside from basic computer skills. Python is a great programming language for data scientists. It is the preferred choice for data scientists. Apart from that SQL is a must no matter what you specialize in..
• Machine Learning: Machine Learning is a fast emerging technology. It will be beneficial for you to gain some hands-on experiences on this. It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:
  • Random Forest
  • Clustering (for example K-means)
  • Logistic Regression
  • K Nearest Neighbor
  • Linear Regression.
• Projects: Many projects are available to take up online. You can refine your search by choosing your level of difficulty, starting from beginner to proficient, based on your knowledge and confidence with the tools and technology. Such projects will boost your profile and help you get closer to your desired job.

Here are the 5 most popular programming language used in the field of Data Science:

• R: Despite the steep learning curve, R language has the following advantages:
  • R comes along with high-quality open source packages created by the open source community. 
  • It is capable of handling matrix operations and statistical functions. 
  • With the help of ggplot2, R acts as a great data visualization tool.
• Python: It is one of the most popular and sought after languages used in the field of Data Science. Even though it has fewer packages than R, it offers the following advantages that make up for it:
  • Most of the libraries needed in Data Science are provided by Pandas, scikit-learn, and tensorflow 
  • It is very easy to learn, understand, and implement. 
  • It also comes with a big open-source community.
• SQL: SQL stands for structured query language. It works on relational databases.
  • The syntax of SQL is easy to learn and understand.
  • Updating, querying, and manipulating data is very efficient using SQL.
• Java: Despite the verbosity limit of Java and the less number of libraries that can be used for Data Science, the language offers the following advantages:
  • Compatibility. It is very easy to integrate java in data science projects as there are systems pre-coded in Java. 
  • Overall, Java is a general purpose, compiled and high-performance language. 
• Scala: It is one of the most preferred languages in Data Science despite its complex syntax and running on JVM. It is because of the following reasons:
  • Scala program can run on Java too as it runs on JVM as well. 
  • High-performance cluster computing can be achieved by using Scala with Apache Spark. 

If you want to download and install Python 3 on Windows, you need to follow these steps:

• Download and setup: First, visit the download page and through the GUI installer, start installing python on the windows. While installing, make sure that select the checkbox that asks you to add Python3.x to PATH, the classpath. This will allow using the functionalities of python directly from the terminal.

Alternatively, you can use Anaconda to install python 

If you want to check if Python is installed in the system, you can try using the following command that displays the version of the language installed on the system:

python --version

• Update and install setuptools and pip: Use the following command for installing and updating 2 of most crucial libraries (3rd party):

python -m pip install -U pip

To install python 3 on Mac OS X, just follow the below steps:

1. You should install GCC first which can be obtained by downloading Xcode, the smaller Command Line Tools (must have an Apple account) or the even smaller OSX-GCC-Installer packageInstall.
2. While OS X comes with a large number of Unix utilities, a package manager is a key component. 
3. To install Homebrew, open Terminal or your favorite OS X terminal emulator and run

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

4. Insert the Homebrew directory at the top of your PATH environment variable. For this, add the following line at the bottom of your ~/.profile file

export PATH="/usr/local/opt/python/libexec/bin:$PATH"

5. You can install Python 3 by writing the following code

$ brew install python