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.
Prerequisites
This is a beginner friendly course and has no prerequisites.
Course curriculum

1
January 2020
 Overview of Learning Path
 MonthonMonth 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 experimentsVideo
 Visualizing Data
 Visualizing Data
 Central tendency
 Variability
 Unimodal Distribution of Data
 Bimodal Distribution of Data
 Normal distribution – Part 1
 Normal distribution – Part 2
 ZScore
 Introduction to Pandas/NumPy Part1
 Introduction to Pandas/NumPy Part2

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: InferentialHypothesis Testing
 Ttest
 One Way ANOVA
 Chisquare
 Cheatsheet on Statistics
 Exploratory Data Analysis (EDA) Data Exploration
 Cheatsheet on EDA
 Project1  Loan Prediction
 Project2  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 RegressionVideo
 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 LearningK Means and Hierarchical Clustering
 Clustering  Project
 Cheatsheet for Machine Learning
 Learn Github

4
April 2020
 Ensemble Learning Basics
 Ensemble Learning BasicsVideo
 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
 Kfold 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 NonStationary 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 dataVideo
 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 resourcesGRU
 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 EmbeddingsText
 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
Instructor

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.
FAQ

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 uptodate 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:
 Phone  10 AM  6 PM (IST) on Weekdays Monday  Friday on +918368253068
 Email training_queries@
analyticsvidhya.com (revert in 1 working day)