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 2019. 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.

Pre-requisites

This is a beginner friendly course and has no prerequisites.

Course curriculum

• 1
January 2019
• Getting Started
• Knowing Each Other
• Overview of Learning Path
• Month-on-Month Plan
• Understanding Data Science
• Job of Data Scientist
• How to setup your machine?
• Python for Data Science
• Cheatsheet for Python
• Overview
• 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
• 2
February 2019
• Join Data Science Communities
• 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
• 3
March 2019
• 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
• Unsupervised Learning-K Means and Hierarchical Clustering
• Project
• Cheatsheet for Machine Learning
• Regression Project - Big Mart Sales
• Classification Project - Loan Prediction
• 4
April 2019
• Ensemble Learning Basics
• Ensemble Learning Basics-Video
• Bagging
• Boosting
• Random Forest - Simplified