Data Science

Duration: 12–16 weeks · Mode: Live & Self-paced

Become a job-ready data scientist: master statistics, machine learning, data engineering, model deployment, and storytelling with data.

Machine Learning: Supervised & unsupervised methods

Programming: Python, libraries (Pandas, NumPy, scikit-learn)

Data Engineering Basics: ETL, databases, data pipelines

Model Deployment: APIs, basic MLOps concepts

Visualization & Storytelling: Dashboards and reports

Core Tools & Technologies

Python

Programming Language

NumPy

Numerical Computing Library

Pandas

Data Analysis Library

Jupyter

Interactive Notebooks

Matplotlib

Data Visualization Library

Scikit-Learn

Machine Learning Library

PyTorch

Deep Learning Framework

TensorFlow

Deep Learning Framework

Plotly

Interactive Visualization

PyCharm

IDE for Python

Visual Studio Code

Code Editor

AWS

Cloud Platform

Google Cloud

Cloud Platform

Git

Version Control

GitHub

Code Hosting

GitLab

Code Hosting

SQL Server

Database & Query Language

Tableau

Business Intelligence Tool

About Data Science

Data Science is the interdisciplinary field that combines statistical analysis, programming expertise, and domain knowledge to extract meaningful insights from complex datasets and build predictive systems that drive real business value.

Statistical Foundation

Master probability, hypothesis testing, and statistical modeling to make data-driven decisions with confidence.

Programming Mastery

Build expertise in Python, SQL, and essential libraries for data manipulation, analysis, and machine learning.

Business Impact

Learn to translate complex data insights into actionable business strategies and measurable outcomes.

Our comprehensive programme emphasizes hands-on learning through real-world projects, statistical thinking, advanced model building, and practical deployment strategies that prepare you for immediate impact in data-driven organizations.

Exclusive Course Offerings

A curated set of features and supports designed to make you job-ready and confident as a data scientist.

Industry-Oriented Curriculum

Curriculum designed around tasks and tools used in actual data teams.

Comprehensive Learning Content

Complete modules, readings and hands-on labs covering core data science topics.

Weekend Live Sessions

Interactive live workshops scheduled on weekends for working professionals.

Capstone Project

A final, industry-grade capstone that synthesizes the skills you've learned.

Practice Exercises

Problem sets and notebooks to reinforce coding and analysis skills.

Assignments and Projects

Weekly assignments and multi-stage projects that mirror workplace tasks.

Live Doubt Resolution Sessions

Regular live sessions to clear doubts and walk through challenging problems.

SME Support Session

Subject matter experts available for deep-dive sessions on specialized topics.

Career Guidance & Interview Preparation

Mock interviews, resume reviews and employer-facing project showcases.

Email Support

Ongoing email support for course queries and follow-up guidance.

Peer Networking

Connect with cohorts, collaborate on projects and grow your professional circle.

Course Curriculum

Module 1: Statistics & Probability Foundations

Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
Probability Distributions: Normal, Binomial, Poisson
Hypothesis Testing: T-tests, Chi-square tests, ANOVA
Confidence Intervals and Statistical Significance
Bayesian Statistics Fundamentals
Correlation vs Causation Analysis

Module 2: Python Programming for Data Science

Python Fundamentals: Data Types, Control Structures
NumPy: Arrays, Mathematical Operations, Broadcasting
Pandas: DataFrames, Data Manipulation, Merging, Grouping
Data Cleaning: Missing Values, Outliers, Data Types
Exploratory Data Analysis (EDA) Techniques
Feature Engineering and Selection
Working with APIs and Web Scraping

Module 3: Data Visualization & Storytelling

Matplotlib: Basic Plots, Customization, Subplots
Seaborn: Statistical Visualizations, Heatmaps
Plotly: Interactive Charts, Dashboards
Data Storytelling Principles
Creating Executive Dashboards
Business Intelligence Reporting

Module 4: Machine Learning Fundamentals

Supervised Learning: Linear & Logistic Regression
Classification: Decision Trees, Random Forest, SVM
Model Evaluation: Accuracy, Precision, Recall, F1-Score
Cross-Validation and Hyperparameter Tuning
Unsupervised Learning: K-Means, Hierarchical Clustering
Dimensionality Reduction: PCA, t-SNE
Scikit-learn Library Deep Dive

Module 5: Advanced Machine Learning

Ensemble Methods: Bagging, Boosting, Stacking
Gradient Boosting: XGBoost, LightGBM
Neural Networks Introduction
Deep Learning with TensorFlow/Keras
Natural Language Processing Basics
Time Series Analysis and Forecasting

Module 6: Data Engineering Essentials

SQL: Advanced Queries, Joins, Window Functions
Database Design and Normalization
ETL Processes and Data Pipelines
Working with Big Data: Spark Basics
Cloud Platforms: AWS, Google Cloud, Azure
Data Warehousing Concepts

Module 7: Model Deployment & MLOps

Model Serialization: Pickle, Joblib
REST API Development with Flask/FastAPI
Containerization with Docker
Cloud Deployment: AWS SageMaker, Google Cloud ML
Model Monitoring and Versioning
CI/CD for Machine Learning Projects
A/B Testing for Models

Module 8: Capstone Project & Portfolio

End-to-End Data Science Project
Problem Definition and Business Understanding
Data Collection and Cleaning
Exploratory Analysis and Feature Engineering
Model Building, Evaluation, and Deployment
Project Documentation and Presentation
GitHub Portfolio Development
Resume Building for Data Science Roles