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

### Pre-requisites

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
• 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
• Z-Score
• Hypothesis Testing
• Exercise
• T-test
• Exercise
• One Way ANOVA
• Exercise
• Chi-square
• Chi-square - Exercise
• Part 1
• Part 2
• Exercise
• Data Exploration
• Exercise
• Git
• 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
• k-fold 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
• Image data
• Text data
• Audio data
• Projects
• 7
##### July 2018
• Factorisation machines
• Field-Aware 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
• 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 up-to-date 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