Learning path

Change the traditional approach to learning Data Science. Instead of spending months on maths and theory, focus on projects, practical skills and tech that in in-demand

Course Curriculum

We brought together some of the of best data scientists, AI and ML entrepreneurs, Kaggle grand masters to create Industry best courses and projects for you to build a 🤘 ROCKSTAR Data Scientist in you.


Course 1

✔ Learn about Variables, Data Types, and Operators in Python.

✔ Manipulate Python strings using In-built functions.

✔ Understand the Concept of Loops and Conditionals.

Course 2

✔ Learn Data Structures for solving complex problems.

✔ Learn about Lists, Tuples, Dictionaries, Sets, Stacks and Queues.

✔ Learn Sorting, and Searching Algorithms.

Course 3

✔ Learn about Parameters and different Types of Arguments in a Function.

✔ Learn about Comprehensions and  Anonymous Functions.

✔ Learn about OOPs Concepts.

Course 4

✔ Learn about Date and Times and Regular Expressions.

✔ Learn Numpy for Complex Mathematical Operations.

✔ Learn Pandas in depth for Data Frame Manipulation.


Course 5

✔ Learn about SELECT, and WHERE Statements.

✔ Learn to use Logical Operators and aggregate Functions.

✔ Learn to use Order By and Group By Clauses.


Course 6

✔ Learn to create a Databases, and Tables with proper validations and Constraints.

✔ Learn different types of Joins in SQL.

✔ Learn to Handle Complex Queries using Sub Queries in SQL. 


Course 7

Data Exploration

✔ Learn to analyze the Target Data using the Dabl library.

✔ Learn Pandas Profiling for analyzing the whole data at once.

✔ Learn about the Sweet Viz for Comparing Datasets.

Course 8

✔ Learn Bayes Theorem, Basic and Conditional Probabilities.

✔ Learn Normal Distributions, Skewness, and QQ Plots.

✔ Learn Central Limit theorem, and Confidence Intervals.

Course 9

✔ Learn the Types of Error in Hypothesis Testing.

✔ Learn to implement t-Test, z-Test and their types.

✔ Learn ANOVA, Chi squared Goodness Tests, and  Chi square Test of Independence.

Course 10

✔ Learn to Handle Missing values in a Real-world Scenario.

✔ Learn to Handle Outliers in a Real-world Scenario.

✔ Learn Data Manipulation Functions for Cleaning the Data.

Course 11

✔ Learn Univariate, Bivariate and Multivariate Analysis.

✔ Learn Animated and Interactive Data Visualizations.

✔ Plot Charts used in Finance, Marketing etc.

Course 12

✔ Perform Grouping Operations and Filtering Operations.

✔ Perform Interactive Query Analysis using Ipy Widgets.

✔ Learn Cross Tabulation using Cross Tabs.

Course 13

✔ Learn to Extract Features from Dates and Times.

✔ Learn to Extract Features from Numerical Data and Categorical Data.

✔ Learn Advanced Functions to Extract New Features from the Data.

Course 14

✔ Learn advanced Data Transformations Techniques.

✔ Learn Encoding Techniques to deal with Categorical Data.

✔ Learn Cross Validation Techniques.

Course 15

✔ Learn about different Types of Recommender Systems.

✔ Implement Content and Collaborative Based Recommender System.

✔ Learn Matrix Factorization using SVD.


Course 16

✔ Implement Machine Learning Algorithms.

✔ Learn Regularization Techniques such as Ridge, Lasso, and Elastic Net.

✔ Learn Feature Selection,  Hyper Parameter Tuning for Improving the Model.

Course 17

✔ Implement Decision Trees and Random Forestsusing Sklearn.

✔ Learn Gini Index and Information Gain.

✔ Understand the working of Random Forests and Decision Trees in Depth.


Course 18

✔ Understand the Concept of Boosting using Ada Boosting Gradient Boosting.

✔ Learn to implement the XG Boost Model.

✔ Learn about the Ensembling Techniques used to Enhance Accuracy of Predictive Models.

Course 19

✔ Understand the Concept of Over Sampling and Under Sampling.

✔ Implement Over Sampling and Under Sampling Techniques.

✔ Using XG Boost for Imbalanced Data sets.

Course 20

✔ Understand the Working of K Means, Hierarchical, and DBSCAN Clustering.

✔ Implement K Means, Hierarchical, and DBSCAN  Clustering using Sklearn.

✔ Learn Evaluation Metrics for Clustering Analysis.

Course 21

✔ Learn Techniques used for Treating Dimensionality.

✔ Implement Correlation Filtering, VIF, and Feature Selection.

✔ Implement PCA, LDA, and t-SNE for Dimensionality Reduction.

Course 22

✔ Learn to Process and Prepare a Time Series Data.

✔ Learn Advanced AR Models ARIMA, ARIMAX, SARIMA, and SARIMAX.

✔ Learn Evaluation Metrics for Time Series Models.

Course 23

✔ Learn to Clean, Process and Prepare Text Data.
✔ Learn Feature Engineering and Text Extraction Techniques. 
✔ Learn to Perform Text Classification using Machine Learning Models.

Course 24

✔ Learn about the Basics of Neural Network.
✔ Learn Tensorflow 2.0 for Implementing ANN, CNN, and RNN.
✔ Learn Advanced Techniques to Improve the Model.

Success Stories

Meet some of our Extremely Talented and Hardworking Alumnis at DataisGood Career Transition Programs who achieved their Dream Jobs as a Data Scientist and Data Analyst in some of the Most Reputed MNCs and Startups.

Aarti Bisht

Data Analyst at Dhruv Research
Background: Content Manager
Education: MSc Maths

Mithilesh Mahatme

Data Scientist at Data Is Good
Background: Fresher
Education: B.Sc

Sarfaraz Ahmed

Software Developer at Betaout India
Background: Fresher
Education: B.Tech CSE

Prakash Narasanagi

Data Scientist at InViz AI
Background: Fresher
Education: B.Tech CSE

Our learners work at


We are on a mission to create Data Science courses that will make our students not just learn the subject, but fall in ❤️ love with that subject so that they become lifelong passionate learners and explorers of that subject.

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