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 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
    • AI&ML Blackbelt Plus Program (Sponsored)
  • 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
    • Random Forest - Detailed with implementation
    • Boosting - Detailed with implementation
    • XGBoost
    • LightGBM
    • CatBoost
    • Introduction to Time Series Forecasting
    • Handling a Non-Stationary Time Series in Python
    • Time Series Modeling using ARIMA
    • Time Series Modeling using Prophet Library
    • Project
  • 5
    May 2019
    • Introduction to validation
    • Different Types of Validation Techniques
    • K-fold Cross Validation - Implementation
    • Summary
    • 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
    • Advanced Ensemble Learning-Stacking
    • Blending
    • Feature Engineering
    • Project - Black Friday
  • 6
    June 2019
    • 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
  • 7
    July 2019
    • Profile Building
    • Learn Github
    • Building your Resume
    • Up Level your Data Science Resume - Course (Sponsored)
    • Ace Data Science Interview Course (Sponsored)
    • Participating in Competitions
    • 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
  • 8
    August 2019
    • SQL for Data Science - Overview
    • SQL Questions for Aspiring Data Scientists
    • Understanding Convolutional Neural Networks (CNNs)
    • Build Image Classification Model using Keras
    • Transfer Learning
  • 9
    September 2019
    • Computer Vision Project 1
    • Computer Vision Project 2
    • Computer Vision Course (Sponsored)
    • Computer Vision Course (Sponsored)
  • 10
    October 2019
    • Introduction to Structured Thinking
    • Commonly Asked Puzzles in Interviews
    • How to solve Guesstimates?
    • Excercise: Strategic Thinking
    • Recurrent Neural Network
    • Long short Term Memory Networks (LSTM)
    • Gated Recurrent Unit (GRU)
    • Useful resources-GRU
    • Text Preprocessing
    • Text Cleaning
    • Text Classification
  • 11
    November 2019
    • Topic Modeling - Overview
    • Latent Semantic Analysis
    • Latent Dirichlet Allocation (LDA)
    • Text Summarization - Overview
    • TextRank for Automatic Summarization
    • Resources
    • NLP Course (Sponsored)
    • NLP Course- Video
    • Word Embeddings
    • Word Embeddings-Text
  • 12
    December 2019
    • Jobs and Internships
    • Up Level your Data Science Resume - Course (Sponsored)
    • Ace Data Science Interview Course (Sponsored)
    • Way Forward

Instructor

  • 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.

Here's what our students have to say about our A comprehensive Learning path to become a data scientist in 2019 course

  • DS Learning Path

    ABDULRAHEEM ADESINA

    Great tutorial structure and systematic approach to knowledge transfer. I rate the course 93%

    Great tutorial structure and systematic approach to knowledge transfer. I rate the course 93%

    Read Less

  • good one to learn from basics

    prabhas_kulkarni prabhas_kulkarni

    good

    good

    Read Less

  • January 2019

    candra saputra

    it's awesome, i hope each video in the lesson have the subtitle

    it's awesome, i hope each video in the lesson have the subtitle

    Read Less

  • Methodical training plan

    Ravi Sankar Kodamarti

    This learning path is both methodical and practical. Strongly recommend newbies to check this one.

    This learning path is both methodical and practical. Strongly recommend newbies to check this one.

    Read Less

  • Very Much Useful course

    Vaibhav Kumar

    Very Much Useful course for beginners. Every newbie must attend this course.

    Very Much Useful course for beginners. Every newbie must attend this course.

    Read Less

  • Good for data science

    Taghreed Hamdy

    I think this successful step for me to start my career

    I think this successful step for me to start my career

    Read Less

  • Good beginning

    Vengala Reddy Illuri

    Very good beginning to start analytics

    Very good beginning to start analytics

    Read Less

  • Precise

    Shashank Upadhyay

    A very intuitive beginner level course!

    A very intuitive beginner level course!

    Read Less

  • Well arranged

    Godfrey Njoka

    The learning material is in a very procedural way, it is easy to follow. Thanks

    The learning material is in a very procedural way, it is easy to follow. Thanks

    Read Less

  • A comprehensive Learning path to become a data scientist ...

    Abhishek Shrivastava

    A very great platform for learning.

    A very great platform for learning.

    Read Less

  • Contains nicely organized flow of contents from the best ...

    Manasvini Ganesh

    Contains nicely organized flow of contents from the best resource from each topic.

    Contains nicely organized flow of contents from the best resource from each topic.

    Read Less

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 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

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