According to the latest reports, data science along with machine learning and big data analytics will play a key role in creating jobs in India. Globally, the demand for data scientists is expected to grow by 40% by 2020. The foreign universities are also seeing a huge number of applications for graduate programs in data science and related fields. Additionally, 94% of techies feel the huge skill gap and need for re-skilling; and search for the best online courses on data science and machine learning.

All these trends are also making MOOC providers create more and more online courses on data science, machine learning, and big data analytics. It’s a good thing that learners have multiple options to choose from. But, at the same time, it also overwhelming and confusing to choose the right online courses. In an attempt to make lives easier, we have aggregated the best online courses for data science 2022.

Before getting on to the list, we will look at a few key trends and stats on the data science job market in India.

best online courses for data science — *Data Science Salaries 2019 (Source: Analytics India)*

Data Science & Analytics Job Market Trends in India

The total number of analytics and data science job positions available in India is 97,000. Out of these, 97% of job openings are on a full-time basis while 3% are part-time or contractual.

The median salary of data science professionals in India is INR 15 Lakhs.

BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom.

The hiring trend has been more favorable for young talent with 21% of all jobs being posted for freshers.

The favorite language for data scientists in today’s era is Python, as almost 44% of the professionals use it the most and with 17% jobs listing asking for Python. Other most in-demand skills are Java (16%) and R (10%).

72% of data scientists used Logistic Regression most at work.

Pandas emerged as a clear choice for most data scientists at almost 41%

51% of all data scientists prefer to use Tableau as a dashboard or visualization tool. Other popular tools are Microsoft Power BI & Qlikview.

27% of data science professionals use GitHub to find open data

Almost 38% prefer using RStudio

52% of the users said they used Hadoop the most. After Hadoop, Spark is the 2nd most in-demand big data tool.

Among cloud solutions, recruiters demand AWS as the most preferred skill, followed by Microsoft Azure.

SQL continues to be the most popular database platform among recruiters followed by NoSQL and MongoDB

Now, we will move on to our main agenda of this article – top online courses on data science in 2019.

18 Best Online Courses on Data Science and Machine Learning

Data Science Specialization – Johns Hopkins University (Coursera)

It is one of the most enrolled in and highly rated online courses in data science across the globe. JHU did an incredible job with the balance of breadth and depth in the curriculum. One thing that’s included in this series that’s usually missing from many data science courses is a complete section on statistics, which is the backbone of data science.

The Data Science specialization is an ideal mix of theory and application using the R programming language. As far as prerequisites go, you should have some programming experience (doesn’t have to be R) and you have a good understanding of Algebra. Previous knowledge of Linear Algebra and/or Calculus isn’t necessary, but it is helpful.

For learners with financial constraints, Coursera does waive the course fee as well.

Curriculum:

The Data Scientist’s Toolbox
R Programming
Getting and Cleaning Data
Exploratory Data Analysis
Reproducible Research
Statistical Inference
Regression Models
Practical Machine Learning
Developing Data Products
Data Science Capstone

Enroll here.

Applied Data Science with Python Specialization – University of Michigan (Coursera)

This is appropriate for someone that already knows R and/or is learning the statistical concepts elsewhere. One of the best courses in the market for the folks who want to learn python for data science.

Learners will get introduced to commonly used tools in data science like Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx, and also learn how to use them on real data.

Curriculum:

Introduction to Data Science in Python
Applied Plotting, Charting & Data Representation in Python
Applied Machine Learning in Python
Applied Text Mining in Python
Applied Social Network Analysis in Python

Statistics and Data Science MicroMasters – MIT (EdX)

MicroMasters from edX are advanced, graduate-level courses that carry real credits you can apply to a select number of graduate degrees. The inclusion of probability and statistics courses makes this series from MIT a very well-rounded curriculum for being able to understand data intuitively. It’s a great data science certification.

You will begin from the very basics of probability and statistics before moving on to data analysis techniques and machine learning algorithms.

Due to its advanced nature, you should have experience with single and multivariate calculus, as well as Python programming. There isn’t any introduction to Python or R like in some of the other courses in this list, so before starting the ML portion, they recommend taking Introduction to Computer Science and Programming Using Python to get familiar with Python.

Python for Data Science and Machine Learning Bootcamp – Udemy

A very reasonably priced course for the value. It’s a comprehensive course, developed by Jose Portilla (Santa Clara University) and Pierian Data International. It has large chunks of machine learning content but covers the whole data science process. It’s more of a very detailed introduction to Python. Probably, the best course for python training and for introduction to machine learning with python.

The instructor does an outstanding job explaining the Python, visualization, and statistical learning concepts needed for all data science projects. A huge benefit to this course over other Udemy courses are the assignments. Throughout the course you’ll break away and work on Jupyter notebook workbooks to solidify your understanding, then the instructor follows up with a solutions video to thoroughly explain each part.

You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.

Curriculum:

Python Crash Course
Python for Data Analysis – Numpy, Pandas
Python for Data Visualization – Matplotlib, Seaborn, Plotly, Cufflinks, Geographic plotting
Data Capstone Project
Machine learning – Regression, kNN, Trees and Forests, SVM, K-Means, PCA
Recommender Systems
Natural Language Processing
Big Data and Spark
Neural Nets and Deep Learning

IBM Data Science Professional (Coursera)

If you have decided to pursue a career in Data Science or machine learning then this is a fairly good place to begin. It has got a very well designed content and all the topics are covered elaborately.

This certification consists of a series of 9 courses that help you to acquire skills that are required to work on the projects available in the industry. The lectures cover a wide range of topics including data visualization, analysis, libraries, and open-source tools. By the end of the program, you will have multiple assignments and projects to showcase your skills and enhance your resume.

Microsoft Professional Program in Data Science (edX)

This professional program by Microsoft consists of 9 courses in addition to a project and will take about 16 – 32 hours per course. It is a 10-course program and you can also choose individual courses if you want.

Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion.

You will learn about using Microsoft Excel to explore data, using Transact-SQL to query a relational database, creating data models using Excel or Power BI, applying statistical methods to data and using R or Python to explore and transform data Follow a data science methodology.

Data Science at Scale Specialization – University of Washington (Coursera)

This course from the University of Washington is about scalable data management. It covers data science topics including scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. It’ll also deal with privacy, ethics, and governance concerning this field.

In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.

Enroll for the course here.

Statistics with R – Duke University (Coursera)

Here is another pretty solid data science online course. In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference.

You will perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions.

Additionally, you will also learn data wrangling and data visualization with R packages for data analysis.

Data Engineering on Google Cloud Platform (Coursera)

This 5-week accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.

You will learn to:

Design and build data processing systems on the Google Cloud Platform
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
Derive business insights from extremely large datasets using Google BigQuery
Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML

Enroll for the course now.