About the course

The most common question we get from beginners in the field of Data Science is - Where to begin? The journey to becoming a Data Scientist can be diffficult if one does not have the right resources to follow. There are a million resources to refer and it is tough to decide where to start from.

We are here to help you take your first steps into the world of Data Science. Here is a free learning path for people who want to become a data scientist in 2020. We have arranged the best resources in a logical manner along with exercises to make sure that you only need to follow one single source to become a data scientist.

Key takeaways of this course?

The course is ideal for beginners in the field of Data Science. Several features which make it exciting are:

Beginner friendly course: The course assumes no prerequisites and is meant for beginners

Curated list of resources to follow: All the necessary topics are covered in the course, in an orderly manner with links to relevant resources and hackathons.


This is a beginner friendly course and has no prerequisites.

Course curriculum

  • 1
    January 2020
    • Overview of Learning Path
    • Month-on-Month Plan
    • Understanding Machine Learning and its impact
    • Job of Data Scientist
    • How to setup your machine?
    • Python for Data Science
    • Cheatsheet for Python
    • Overview of Statistics
    • Important applications of Statistics
    • What is Descriptive Statistics?
    • Introduction to Design experiments
    • Introduction to Design experiments-Video
    • Visualizing Data
    • Visualizing Data
    • Central tendency
    • Variability
    • Unimodal Distribution of Data
    • Bimodal Distribution of Data
    • Normal distribution – Part 1
    • Normal distribution – Part 2
    • Z-Score
    • Introduction to Pandas/NumPy- Part-1
    • Introduction to Pandas/NumPy- Part-2
    • AI&ML Blackbelt Plus Program (Sponsored)
  • 2
    February 2020
    • Subscribe to Data Science Newsletter and Podcast
    • Introduction to Probability- An Overview
    • Principal Of Counting
    • Permutation
    • Combination
    • Conditional Probability – Part 1
    • Conditional Probability – Part 2
    • Binomial Distribution
    • Random variable
    • Expectation and variance
    • Cheatsheet for Probability
    • Statistics: Inferential-Hypothesis Testing
    • T-test
    • One Way ANOVA
    • Chi-square
    • Cheatsheet on Statistics
    • Exploratory Data Analysis (EDA)- Data Exploration
    • Cheatsheet on EDA
    • Project-1 | Loan Prediction
    • Project-2 | Big Mart Sales
    • Linear Algebra
    • Free Course
    • SQL for Data Science - Overview
    • SQL Questions for Aspiring Data Scientists
    • Structured Query Language (SQL) Course
  • 3
    March 2020
    • Understanding Data Science Pipeline
    • Get Familiarised with Command Line (Linux)- Guide
    • Linear Regression
    • Linear Regression-Video
    • Logistic Regression- Part 1
    • Logistic Regression – Part 2
    • Decision Tree Algorithm
    • Naive Bayes
    • Support Vector Machine
    • Regression Project - Big Mart Sales
    • Classification Project - Loan Prediction
    • Unsupervised Learning-K Means and Hierarchical Clustering
    • Clustering - Project
    • Cheatsheet for Machine Learning
    • Learn Github
  • 4
    April 2020
    • Ensemble Learning Basics
    • Ensemble Learning Basics-Video
    • Bagging
    • Boosting
    • Random Forest - Simplified
    • Random Forest - Detailed with implementation
    • Boosting - Detailed with implementation
    • XGBoost
    • LightGBM
    • CatBoost
    • Advanced Ensemble Technique - Blending
    • Advanced Ensemble Learning - Stacking
    • Participating in Competitions
  • 5
    May 2020
    • Introduction to validation
    • Different Types of Validation Techniques
    • K-fold Cross Validation - Implementation
    • Summary - Validation Techniques
    • Different methods for finding best hyperparameters of an algorithm
    • Hyperparameter tuning for Random Forest
    • Hyperparameter tuning for GBM
    • Hyperparameter tuning for XGBoost
    • Hyperparameter tuning for LightGBM
    • Bayesian Hyperparameter Optimization
    • Feature Engineering
    • Introduction to Time Series Forecasting
    • Handling a Non-Stationary Time Series in Python
    • Time Series Modeling using ARIMA
    • Time Series Modeling using Prophet Library
    • Time Series Project
    • Project - Black Friday
    • Profile Building
    • Building your Resume
    • Up Level your Data Science Resume Course
    • Ace Data Science Interview Course
  • 6
    June 2020
    • Basics of Matrix Algebra
    • Matrix Calculus
    • Dimensionality Reduction - Overview
    • Principal Component Analysis (PCA)
    • Singular Value Decomposition (SVD)
    • Singular Value Decomposition (SVD)-Text
    • Image data
    • Text data
    • Audio data
    • Audio data-Video
    • Projects
    • Introduction to Recommendation Systems
    • Introduction to Recommendation Systems - Video
    • Implementation in Python
    • Project: Recommendation System
  • 7
    July 2020
    • Setting up the System for Deep Learning
    • Introduction to Deep Learning
    • Build your first Neural Network in Numpy
    • Why are GPUs necessary for Deep Learning?
    • The Evolution and Core Concepts of Deep Learning & Neural Networks
    • An Introduction to Implementing Neural Networks using TensorFlow
    • Introduction to Keras
    • Optimizing Neural Networks using Keras (with Image recognition case study)
    • Cheatsheet for Keras
    • Write for Analytics Vidhya's Medium Publication
  • 8
    August 2020
    • Understanding Convolutional Neural Networks (CNNs)
    • Build Image Classification Model using Keras
    • Transfer Learning for Computer Vision
  • 9
    September 2020
    • Computer Vision Project 1 : Identify the Apparels
    • Computer Vision Project 2: Scene Classification
    • Computer Vision using Deep Learning Course
  • 10
    October 2020
    • Introduction to Structured Thinking
    • Commonly Asked Puzzles in Interviews
    • How to solve Guesstimates?
    • Excercise: Strategic Thinking
    • Structured Thinking and Communication Course
    • Recurrent Neural Network
    • Long short Term Memory Networks (LSTM)
    • Gated Recurrent Unit (GRU)
    • Useful resources-GRU
    • Text Preprocessing
    • Text Cleaning
    • Text Classification
  • 11
    November 2020
    • Topic Modeling - Overview
    • Latent Semantic Analysis
    • Latent Dirichlet Allocation (LDA)
    • Text Summarization - Overview
    • TextRank for Automatic Summarization
    • Resources
    • Natural Language Processing (NLP) Using Python Course
    • Word Embeddings
    • Word Embeddings-Text
    • Introduction to BERT: NLP Transfer Learning Framework
    • Introduction to ELMO: NLP Transfer Learning Framework
  • 12
    December 2020
    • Jobs and Internships
    • Up Level your Data Science Resume Course
    • Ace Data Science Interview Course
    • Way Forward


  • Analytics Vidhya

    Analytics Vidhya

    Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals.


  • What web browser should I use?

    Our training platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above. See our list of supported browsers for the most up-to-date information.

  • How do I need to pay for this course?

    Nothing! Yes - you read it right. This course is free for our community members as a way to get them started in Data Science.

  • Do I get certificate upon completion of the course?

    No, we do not provide certificate with this course.

  • Where do I ask my queries?

    You can post your queries on the discussion for the course or share them on the discuss portal at discuss.analyticsvidhya.com

Support for A comprehensive Learning path to become a data scientist in 2020

Support for A comprehensive Learning path to become a data scientist in 2020 course can be availed through any of the following channels: