Become expert in Data Science & Business Analytics Project Management
Applied Data Science with Python
Learn to analyze, visualize, and model data using Python in real-world applications.
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Intermediate
Updated 11/2025
Hinglish
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Course Description
The Data Science with Python course empowers you to excel in Python programming. In this course, you'll delve into data science, data analysis, data visualization, data wrangling, feature engineering, and statistics. Upon finishing the course, you'll excel in using essential data science tools with Python.
1. Introduction to Data Science
4 Lectures
Introduction to Data Science
Fundamentals of Data Science
Principles and Concepts of Data Science
Foundations of Data Science and Analytics
2. Python Fundamentals for Data Science
4 Lectures
Introduction to Python for Data Science
Core Python Concepts for Data Science Applications
Foundations of Python Programming for Data Science
Python for Data Science: Basics and Applications
3. Data Manipulation with Pandas
4 Lectures
Introduction to Data Manipulation with Pandas
Core Pandas Techniques for Data Analysis
Advanced Data Handling and Transformation Using Pandas
Data Cleaning and Preparation with Pandas
4. Numerical Computations with NumPy
4 Lectures
Introduction to Numerical Computations with NumPy
Core Array Operations and Mathematical Computations
Data Manipulation and Analysis Using NumPy
Advanced NumPy Techniques for Efficient Computation
5. Data Visualization
4 Lectures
Introduction to Data Visualization Concepts
Fundamentals of Charts, Graphs, and Visual Analytics
Creating Interactive Dashboards and Reports
Advanced Data Visualization Techniques and Best Practices
6. Exploratory Data Analysis (EDA)
4 Lectures
Introduction to Exploratory Data Analysis (EDA)
Data Cleaning and Preprocessing for EDA
Univariate and Multivariate Analysis Techniques
Visualizing Data for Insights and Patterns
7. Introduction to Statistics for Data Science
4 Lectures
Fundamentals of Statistics for Data Science
Descriptive Statistics: Summarizing and Understanding Data
Probability Concepts and Distributions
Inferential Statistics: Hypothesis Testing and Estimation
8. Machine Learning Fundamentals
4 Lectures
Introduction to Machine Learning Concepts
Supervised Learning: Algorithms and Applications
Unsupervised Learning: Clustering and Dimensionality Reduction
Reinforcement Learning and Core Principles
9. Advanced Machine Learning with Python
4 Lectures
Introduction to Advanced Machine Learning Techniques with Python
Ensemble Methods and Boosting Algorithms in Python
Support Vector Machines and Kernel Methods
Deep Learning Basics and Neural Networks with Python
10. Applied Projects in Data Science
4 Lectures
Introduction to Applied Data Science Projects
Data Collection, Cleaning, and Preprocessing in Projects
Exploratory Data Analysis and Visualization for Projects
Implementing Machine Learning Models in Real-World Projects
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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.
₹10,000
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