DATA Science & AI / ML Program
Data Science
Data science is the field that combines statistics, programming, and domain knowledge to extract insights and knowledge from data. It involves collecting, cleaning, analyzing, and visualizing data to make data-driven decisions or build predictive models.
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Beginner
Updated 11/2025
Hinglish
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Course Description
- Definition: Data Science is the field of extracting meaningful insights from raw data.
- Key Components: Statistics, programming, and domain knowledge.
- Data Handling: Collecting, cleaning, and organizing data for analysis.
- Analysis & Modeling: Using algorithms, machine learning, and statistical methods to find patterns and predictions.
- Visualization & Communication: Presenting findings through charts, graphs, and reports for decision-making.
- Applications: Business intelligence, healthcare analytics, finance, AI, and more.
- Goal: Transform raw data into actionable insights that drive decisions.
1. Introduction to Data Science
5 Lectures
Introduction to Data Science
Data Science Fundamentals
Getting Started with Data Science
Foundations of Data Science
Data Science: Concepts and Applications
2. Mathematics and Statistics for Data Science
5 Lectures
Mathematics & Statistics for Data Science
Foundations of Math and Statistics in Data Science
Essential Mathematics and Statistics for Data Science
Math & Statistics Concepts for Data Science
Mathematical and Statistical Foundations of Data Science
3. Programming for Data Science
4 Lectures
Data Science Programming Fundamentals
Coding Techniques for Data Science
Programming Essentials for Data Science
Data Science with Programming
4. Data Wrangling and Preprocessing
4 Lectures
Preparing and Cleaning Data for Analysis
Data Preprocessing and Transformation Techniques
Data Wrangling Fundamentals for Data Science
"Data Cleaning and Preprocessing Essentials
5. Data Visualization
5 Lectures
Introduction to Data Visualization
Data Visualization Fundamentals
Visualizing Data for Insights
Data Visualization Techniques & Tools
Effective Data Representation and Visualization
6. Exploratory Data Analysis (EDA)
4 Lectures
Introduction to EDA
Data Exploration and Visualization
EDA Techniques & Methods
Insights through Exploratory Data Analysis
7. Machine Learning Basics
5 Lectures
Introduction to Machine Learning
Machine Learning Fundamentals
Types of Machine Learning
Machine Learning Workflow & Process
Applications of Machine Learning
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Frequently Asked Questions
This course provides comprehensive knowledge with practical examples for
beginners and professionals.
No prior experience required. We start with basics and progress to
advanced topics.
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