Data Science with Python

About This Course

Data Scientists are among the highest-paid professionals in the global tech industry, with average salaries in India reaching ₹10–25 LPA. They sit at the intersection of mathematics, programming, and business intelligence — and companies across every sector are competing to hire them.
This 8-week program is designed for CS, IT, and Mathematics background students who want to build a rigorous, practical foundation in data science. Unlike courses that rush through concepts, this program takes the time to genuinely develop your skills — from Python fundamentals through to building and evaluating machine learning models on real datasets.
You will work with the core Python data science stack: Pandas for data manipulation, NumPy for numerical computing, Matplotlib and Seaborn for visualization, and Scikit-learn for machine learning. You will follow the complete data science workflow on real-world datasets — from raw data ingestion and cleaning through exploratory analysis, feature engineering, model building, and evaluation.
Every week builds on the last, and every topic is reinforced with hands-on projects. By the end of the course, you will have a portfolio of data science notebooks and a complete end-to-end project that demonstrates your capability to employers and graduate programs.

Curriculum Highlights

Week 1 — Python for Data Science
Python refresher: lists, dictionaries, functions, file handling; NumPy arrays: creation, indexing, broadcasting, mathematical operations; working with Google Colab; introduction to the data science workflow
Week 2 — Data Manipulation with Pandas
DataFrames and Series: creation and indexing; loading data from CSV, Excel, and APIs; data cleaning: handling missing values, duplicates, and type conversions; filtering, sorting, and grouping data; merging and joining DataFrames
Week 3 — Data Visualization
Matplotlib: line plots, bar charts, histograms, scatter plots; Seaborn: advanced statistical visualizations; choosing the right chart for the right question; visualizing distributions, correlations, and trends; creating publication-quality charts
Week 4 — Exploratory Data Analysis (EDA)
The EDA workflow on a real dataset; univariate and bivariate analysis; correlation analysis and heatmaps; outlier detection and treatment; feature understanding and business insight generation; EDA project on a real-world dataset
Week 5 — Introduction to Machine Learning
What is machine learning and how it works; supervised vs unsupervised learning; train/test split and cross-validation; model evaluation metrics: accuracy, precision, recall, F1, RMSE; introduction to Scikit-learn
Week 6 — Supervised Learning Algorithms
Linear Regression: simple and multiple; Logistic Regression for classification; Decision Trees and Random Forests; K-Nearest Neighbors; Support Vector Machines basics; hyperparameter tuning
Week 7 — Unsupervised Learning & Feature Engineering
K-Means Clustering; Principal Component Analysis (PCA); feature scaling: normalization and standardization; feature selection techniques; encoding categorical variables; building ML pipelines
Week 8 — Capstone Project & Career Prep
End-to-end data science project on a real dataset: EDA, modeling, and presentation; organizing Jupyter notebooks as portfolio pieces; deploying a simple model with Streamlit; data science interview preparation; roadmap toward advanced topics: deep learning, NLP, and time series

What You'll Achieve

Manipulate and clean real-world datasets using Pandas and NumPy
Create professional data visualizations with Matplotlib and Seaborn
Perform comprehensive exploratory data analysis
Build and evaluate supervised and unsupervised machine learning models
Apply feature engineering and model optimization techniques
A complete end-to-end data science portfolio project
Job-ready for Data Scientist, Data Analyst, and ML Engineer entry-level roles