Part I: Data Engineering & Analytics
Module 1: Python Programming for Data
Duration: 6 Days
- Core Python: Variables, loops, functions, and data structures
- Jupyter Notebooks and setting up Anaconda environments
- Introduction to NumPy: Multi-dimensional arrays and mathematics
Module 2: Data Manipulation with Pandas
Duration: 8 Days
- Series and DataFrames: Filtering, sorting, and aggregating
- Handling missing data and data imputation techniques
- Merging, joining, and concatenating datasets
- Reading data from CSV, Excel, JSON, and APIs
Module 3: SQL & Relational Databases
Duration: 6 Days
- Database architecture and writing complex SQL queries
- Joins, Subqueries, and Window Functions
- Connecting Python to SQL databases using SQLAlchemy
Module 4: EDA & Data Visualization
Duration: 5 Days
- Exploratory Data Analysis (EDA) best practices
- Creating plots with Matplotlib and Seaborn
- Interactive dashboards using Plotly and Tableau fundamentals
Part II: Machine Learning & Deep Learning
Module 5: Statistical Foundations
Duration: 5 Days
- Descriptive vs. Inferential statistics
- Probability distributions, Variance, and Standard Deviation
- Hypothesis testing, P-values, and A/B Testing
Module 6: Machine Learning: Regression & Classification
Duration: 10 Days
- Supervised learning with Scikit-Learn
- Linear & Logistic Regression, Decision Trees, and Random Forests
- Model evaluation: Accuracy, Precision, Recall, and ROC Curves
- Hyperparameter tuning and Cross-Validation
Module 7: Unsupervised Learning & Clustering
Duration: 4 Days
- K-Means Clustering and Hierarchical Clustering
- Principal Component Analysis (PCA) for dimensionality reduction
- Anomaly detection techniques
Module 8: Deep Learning & Neural Networks
Duration: 8 Days
- Introduction to TensorFlow and Keras
- Building Artificial Neural Networks (ANNs)
- Convolutional Neural Networks (CNNs) for Image Processing
- Capstone Project: End-to-End AI Model Deployment
About the Mentor
Susheel Singh
Principal Data Scientist
With deep expertise in predictive modeling, statistical analysis, and machine learning infrastructure, Susheel has helped scale data pipelines and AI models for enterprise businesses. His approach to teaching breaks down complex mathematical algorithms into intuitive, visual, and highly practical Python code. He focuses on real-world datasets rather than textbook theories, ensuring you are job-ready.
Enrollment Options
Data Science
₹ 40,000
Perfect for transitioning careers.
- Access to Part I: Data Engineering & Analytics
- Over 50+ real-world datasets to practice
- Downloadable Python & Jupyter notebooks
- Official Certificate of Completion
Most Popular
AI Data Scientist
₹ 50,000
Master the complete AI pipeline.
- Everything in the Data Analyst tier
- Access to Part II: Machine Learning & Deep Learning
- Code review on your AI Capstone Project
- Private Discord community access
- Interview preparation guide & Resume review
Frequently Asked Questions
Do I need a strong background in Math or Programming?
No prior coding experience is required; we teach Python from absolute scratch in Module 1. A basic high-school level understanding of math is helpful, but we cover all necessary Statistics and Probability concepts entirely inside the course.
What software and tools will we use?
We utilize the industry standard tools: Python, Jupyter Notebooks, Pandas, NumPy, Scikit-Learn, Matplotlib, and TensorFlow. We will guide you step-by-step on how to install and set up all of these on your machine for free.
Will we work on real datasets?
Absolutely. We don't use clean, perfect "toy" datasets. You will be learning how to handle messy, real-world data pulled from Kaggle, financial APIs, and public government records to build your portfolio.
What is the difference between Data Analysis and Data Science?
A Data Analyst typically looks at historical data to explain what happened (Part 1 of the course). A Data Scientist builds predictive Machine Learning models to forecast what will happen in the future (Part 2 of the course).
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