About the course
This is a free learning path for people who want to become a data scientist in 2018. 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.
Why take 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
We assume no prior information about data science. Background in Mathematics and Computer Science would be beneficial, but is not required.
Course curriculum

1
January 2018
 Overview of Learning Path
 Introduction to Data Science
 Job of Data Scientist
 Exercise
 How to setup a machine?
 1. Python Basics – Numbers and Maths
 2. Variables and Inputs
 3. Lists, sets and tuples
 Exercise
 Dictionary
 Exercise
 Conditional Statements
 Exercise
 Loops
 Excercise
 Reading and Writing
 Excercise
 DataHack Summit 2018
 Information DHS 2018

2
February 2018
 Overview
 Important applications of Statistics
 What is Descriptive Statistics?
 Optional
 Introduction to Design experiments
 Introduction to Design experiments
 Optional
 Exercise
 Visualizing Data
 Visualizing Data
 Central tendency
 Exercise
 Variability
 Exercise
 Normal distribution – Part 1
 Normal distribution – Part 2
 Exercise
 ZScore
 Hypothesis Testing
 Exercise
 Ttest
 Exercise
 One Way ANOVA
 Exercise
 Chisquare
 Chisquare  Exercise
 Part 1
 Part 2
 Exercise
 Data Exploration
 Exercise
 Git
 Blogs and Newsletters

3
March 2018
 What to expect  March 2018
 Overview
 Principal Of Counting
 Exercise
 Permutation
 Exercise
 Combination
 Exercise
 Conditional Probability – Part 1
 Conditional Probability – Part 2
 Exercise
 Binomial Distribution
 Random variable
 Expectation and variance
 Exercise
 Introduction to Machine Learning
 Linear Regression
 Exercise
 Logistic Regression Part 1
 Logistic Regression – Part 2
 Exercise
 Decision Tree
 Exercise
 Naives Bayes
 Clustering algorithms
 Exercise
 KNN
 Exercise

4
April 2018
 Ensemble Learning Basics
 Different Ensemble Learning methods with code
 Bagging (Bootstrap Aggregation)
 Random Forest  Simplified
 Random Forest  Detailed with implementation
 Exercise
 Boosting  Simplified
 Boosting  Detailed with implementation
 Exercise

5
May 2018
 Introduction to validation
 Hold out cross validation
 Leave one out cross validation
 kfold cross validation
 Implementation in Python
 Implementation in R
 Summary
 Exercise
 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
 Black friday
 Loan Prediction
 Big mart sales

6
June 2018
 Image data
 Text data
 Audio data
 Projects

7
July 2018
 Factorisation machines
 FieldAware Factorization Machines
 Implementation using XLearn
 Introduction to Vowpal Wabbit
 Projects

8
August 2018
 What is Neural Networks?
 Theory and Implementation
 Exercise
 Introduction to CNN
 Theory
 Implementation
 Exercise
 Project
 Theory
 Implementation
 Theory
 Implementation
 Project 1
 Project 2

9
September 2018
 Image Classification
 Project
 Object detection/Localisation
 Research papers

10
October 2018
 Audio classification  Theory and Implementation
 Project
 Speech recognition  Theory and implementation
 Project
 Speaker Identification  Theory and implementation
 Project
 DataHack Summit 2018

11
November 2018
 Text Classification
 Competition
 Text Summarization
 Author Identification
 Competition
 Machine Translation

12
December 2018
 Profile Building
 Introduction to Github
 Building your Resume
 Participating in Competitions
 Project and Certifications
 Jobs and Internships
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 much 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