Data Science
Learn Python, Data Analysis, Machine Learning, and real-world project implementation.
Module 1
Introduction to Data Science
- What is Data Science?
- Data Science vs AI vs ML vs Data Analytics
- Applications in Healthcare, Finance, Marketing
- Data Science Lifecycle
- Career Roles & Tools Overview
Module 2
Python Programming Fundamentals
- Python Installation & Anaconda
- Jupyter Notebook & VS Code
- Variables, Data Types, Operators
- Conditions & Loops
- Functions & Lambda
- Exception & File Handling
Module 3
Python for Data Analysis
- NumPy Arrays & Operations
- Pandas Series & DataFrames
- CSV, Excel, JSON Handling
- Data Filtering & GroupBy
- Merge, Join & Missing Values
- Data Cleaning Techniques
Module 4
Data Visualization
- Matplotlib – Line, Bar, Pie, Histogram
- Seaborn – Heatmap, Boxplot, Pairplot
- Plot Customization
- Visual Storytelling
Module 5
Exploratory Data Analysis (EDA)
- Understanding Dataset Structure
- Descriptive Statistics
- Outliers & Correlation Analysis
- EDA on Real Datasets
Module 6
Statistics for Data Science
- Mean, Median, Mode
- Variance & Standard Deviation
- Probability & Distribution
- Z-Test & T-Test
- Correlation & Covariance
Module 7
Machine Learning Fundamentals
- Supervised & Unsupervised Learning
- Model Training & Testing
- Bias-Variance Tradeoff
Module 8
Supervised Machine Learning
- Linear & Logistic Regression
- KNN, Decision Tree, Random Forest
- SVM
- Accuracy, Precision, Recall, F1
Module 9
Unsupervised Learning
- K-Means & Hierarchical Clustering
- PCA – Dimensionality Reduction
Module 10
Projects & Career Preparation
- Capstone Project (Real Dataset)
- GitHub Portfolio
- Resume & Interview Preparation
- Industry Case Studies
- Freelancing & Job Guidance
- SQL for Data Science