Explore, Learn, Develop
Master key concepts, build real-world projects, and prepare for industry roles in machine learning and AI — all in one comprehensive path.
Step1
Complete Python Programming
Numpy for Data Science
Pandas for Data Science
SQL for Data Science - Level I (Basics)
SQL for Data Science - Level II (Intermediate)
SQL for Data Science - Level III (Advanced)
SQL for Data Science - Window Functions
Programming for
Data Science
Step2
ML Algorithms
Step3
Linear algebra for Machine Learning
Statistics for Data Science
Data Pre-processing & EDA
Linear Regression and Regularisation
Classification: Logistic Regression
Imbalanced Classification
Supervised ML Algorithms
Ensemble Learning
Fixed and mixed effects modeling
Introduction to Time Series Analysis
Time Series Analysis - I (Beginners)
Time Series Analysis - II (Intermediate)
Time Series Forecasting Part 1 - Statistical Models
Time Series Forecasting Part 2 - ARIMA Modeling and Tests
Time Series Forecasting Part 3 - Vector Auto Regression
Time Series Forecasting III - Singular Spectrum Analysis (SSA)
Feature Engineering for Time Series Projects - I
Feature Engineering for Time Series Projects - II
Time Series Forecasting
Step4
Deep Learning
Step5
Foundations of Deep Learning in Python
Foundations of Deep Learning in Python - Part 2
Applied Deep Learning with PyTorch
Detecting Defects in Steel Sheets with ComputerVision
Project Text Generation using Language Models with LSTM
Project Classifying Sentiment of Reviews using BERT NLP
Estimating Customer Lifetime Value for Business
Microsoft Malware Detection Project
Credit Card Fraud Detection
Optimizing Marketing Budget Spend with Market Mix Modelling
Predict Rating given Amazon Product Reviews using NLP
Uplift modeling: Estimating incremental impact of marketing campaigns
Survival Analysis Part 1: Predicting Time to Event in real world applications
Survival Analysis Part 2: Predicting Time to Event for lungs cancer patients
Attribution Models in Marketing
Industry Data Science Projects
Step6
Machine Learning Ops
Step7
ML Deployment in AWS EC2
Deploy ML Models in AWS Lambda
Deploy ML Models in AWS Sagemaker
PySpark for Data Science - I: Fundamentals
PySpark for Data Science - II: Statistics for Big Data
PySpark for Data Science - III: Data Cleaning and Analysis
PySpark for Data Science - IV: Machine Learning
PySpark for Data Science - V: ML Pipelines
MLFlow in Action: Hands on guide to ML experiments
Mastering LangChain Part: 1
Generative AI
Step8
Optimization for Data Science
Step9
Introduction to Optimization Linear Programming
Inventory Planning and Holding Cost Optimization
Optimization Part 2 Integer Programming
Spacy for NLP
Base R Programming
Dplyr for data wrangling
Wrangling data with Data Table
GGPlot2 visualization for data analysis
Statistical Foundations for ML in R
Regression Model in R
Caret package in R
Supplementary courses
Step9
How long will it take for me to complete?
I can spend
hours / day
≈ 8-9 Months
* This is based on averages from our students. This may change depending on your experience and level of expertise.
Data Scientist
Average Salary
$156,717 /year
What day-to-day looks like
- Meetings to discuss ongoing projects and priorities
- Task organization and prioritization
- Data wrangling and feature engineering
- Modeling and experimentation using ML algorithms
- Code review and feedback from colleagues
- Afternoon meetings with stakeholders or team
- Evening research and learning to stay updated on latest data science trends and technologies
Topic based learning paths
Learning Path
Data Science Programming
9 Courses
25h
Learning Path
Machine Learning
11 Courses
42h
Learning Path
ML Ops
9 Courses
11h
Learning Path
Deep Learning
6 Courses
6h
Learning Path
Time Series Forecasting
9 Courses
16h
Learning Path
Industry DS Projects
15 Courses
33h
Learning Path
Generative AI
1 Course
2h
Learning Path
Supplementary Courses
8 Courses
24h
FAQs
How do I start learning Machine Learning?
How do I get refund?
How to access the courses?
How to get the course materials?
How do I ask questions?
How long does it take to get answers to my questions?
Is there Placement support?
What course should I learn first?
How much maths is needed for ML?
I don't have a masters degree, can I still pursue Data Science?
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