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Data Science Course
The Data Science Course covers the fundamentals of data analysis, machine learning, and statistical modeling. It equips you with the skills to extract insights and make data-driven decisions using Python, R, and other tools.
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Updated 11/2025
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Course Description
This Data Science course, in collaboration with IBM, propels your career to become a certified data scientist. The training program helps you gain expertise in skills like Python, SQL, Excel, Machine Learning, Tableau, generative AI, and more. Dive deep into data interpretation nuances and enhance your programming skills to elevate your Data Science career.
1. Introduction to Data Science
4 Lectures
Overview of Data Science and Its Applications
Data Science Lifecycle: From Data to Insights
Key Tools and Technologies in Data Science
Fundamentals of Statistics, Probability, and Machine Learning
2. Python for Data Science
5 Lectures
Introduction to Python for Data Science
Python Basics: Data Types, Variables, and Control Structures
Working with Data: Lists, Dictionaries, and DataFrames
Data Analysis and Manipulation with Pandas and NumPy
Data Visualization and Reporting Using Python Libraries
3. Data Visualization and Communication
4 Lectures
Introduction to Data Visualization and Its Importance
Fundamentals of Visual Design: Charts, Graphs, and Infographics
Storytelling with Data: Communicating Insights Effectively
Interactive Dashboards and Visualization Tools
4. Statistics and Probability for Data Science
4 Lectures
Introduction to Statistics and Probability for Data Science
Descriptive Statistics: Summarizing and Exploring Data
Probability Concepts and Random Variables
Inferential Statistics: Hypothesis Testing and Confidence Intervals
5. Exploratory Data Analysis (EDA)
4 Lectures
Introduction to Exploratory Data Analysis (EDA)
Understanding Data Distributions and Summary Statistics
Detecting Outliers and Missing Data
Visualizing Data Patterns and Relationships
6. Machine Learning Fundamentals
5 Lectures
Introduction to Machine Learning and Its Applications
Supervised Learning: Regression and Classification Basics
Unsupervised Learning: Clustering and Dimensionality Reduction
Model Evaluation and Performance Metrics
Data Preprocessing and Feature Engineering for Machine Learning
7. Advanced Machine Learning
4 Lectures
Ensemble Methods: Bagging, Boosting, and Stacking
Advanced Regression and Classification Techniques
Dimensionality Reduction and Feature Selection
Time Series Analysis and Forecasting Models
8. Deep Learning and Neural Networks
4 Lectures
Introduction to Deep Learning and Neural Networks
Understanding Perceptrons, Activation Functions, and Architectures
Training Neural Networks: Backpropagation and Optimization
Convolutional Neural Networks (CNNs) and Image Applications
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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.
₹50,000
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