Editorial Team

According to the World Economic Forum’s “Future of Jobs” Report published in 2020, the top job role in the world – with the highest demand – will be that of a Data Analyst and Scientist by 2025. The pandemic-driven digital transformation has led to greater demand for data science skills, where employers are willing to pay a premium, along with other benefits and long-term incentives, in order to attract and retain data scientists and data engineers.

The demand for data scientists outstrips supply worldwide, and India is no exception. The domain offers tremendous opportunities to take one’s career to the next level, opening up access to various job roles such as – Data Scientist, Data Architect, Data Engineer, Data Analyst, Business Analyst, Analytics Manager, and  Business Analytics Specialist.

Data Science skill is not restricted to learners and professionals from STEM fields. It is not uncommon for companies globally to build data science teams with talent from a broader choice of fields — including social sciences — alongside traditional hires like computer scientists, creating opportunities for a diverse set of professionals to get data science jobs.

According to Coursera’s Global Skills Report 2021, learners can prepare for an entry-level role of a Data Analyst with just around 64 hours of online learning sessions. Data Science courses on Coursera are among the most sought after  courses on the platform. In fact, there have been over 1.6 million enrollments in data science courses among Indian learners on Coursera in the past one year.

If you are looking to build a career in Data Science, here are the top skills to acquire, as per Coursera’s GSR 2021:

1. Python Programming

a. Programming for Everybody (Getting Started with Python) by University of Michigan

b. Python for Data Science, AI & Development by IBM

2. Statistics

a. Statistical Inference by Johns Hopkins University

3. Machine Learning

a. Machine Learning by Stanford University

4. Probability & Statistics

a. Probability and Statistics: To p or not to p? by University of London

b. Introduction to Probability and Data with R by Duke University

5. Machine Learning Algorithms

a. Machine Learning Algorithms: Supervised Learning Tip to Tail by Alberta Machine Intelligence Institute, University of Alberta

b. Artificial Intelligence Algorithms Models and Limitations by LearnQuest

6. Applied Machine Learning

a. Introduction to Applied Machine Learning by Alberta Machine Intelligence
Institute, University of Alberta

b. Applied AI with DeepLearning by IBM

c. Applied Machine Learning in Python by University of Michigan

7. Data Management

a. Data Management and Visualization by Wesleyan University

b. Prepare Data for Exploration by Google

8. Econometrics

a. Econometrics by HSE University

b. Econometrics: Methods and Applications by Erasmus University Rotterdam

9. Deep Learning

a. Neural Networks and Deep Learning by DeepLearning.AI

b. Introduction to Deep Learning & Neural Networks with Keras by IBM

10. SQL

a. SQL for Data Science by University of California, Davis

b. Databases and SQL for Data Science with Python by IBM

c. Introduction to Structured Query Language (SQL) by University of Michigan

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