Course Content

1. Introduction to Data Science
Understand the Data Science lifecycle and real-world applications. Learn roles, tools, and industry use cases.

2. Python for Data Science
Overview of NumPy, Pandas, and Matplotlib. Perform data manipulation and basic analysis.

3. Data Collection & Importing Data
Work with CSV, Excel, JSON, and APIs. Load and manage datasets efficiently.

4. Data Cleaning & Preprocessing
Handle missing values, duplicates, outliers, and inconsistent data. Prepare clean datasets for analysis.

5. Exploratory Data Analysis (EDA)
Analyze patterns using statistical summaries and visualizations. Draw meaningful insights from raw data.

6. Data Visualization
Create graphs using Matplotlib and Seaborn. Build clear and professional visual reports.

7. Statistics for Data Science
Understand mean, median, variance, probability, distributions, and hypothesis testing basics.

8. Feature Engineering
Select and transform important features to improve model performance.

9. Introduction to Machine Learning
Understand supervised and unsupervised learning concepts.

10. Regression Algorithms
Learn Linear Regression and model evaluation techniques.

11. Classification Algorithms
Study Logistic Regression, KNN, Decision Trees, and Random Forest.

12. Model Evaluation & Validation
Use train-test split, cross-validation, confusion matrix, accuracy, precision, recall, and F1-score.

13. Introduction to SQL for Data Science
Learn basic queries, filtering, grouping, and joins for data analysis.

14. Dashboard & Reporting (Basic)
Create simple dashboards using Python tools or Excel/Power BI basics.

15. Model Deployment (Basic)
Deploy models using Flask or Streamlit for real-world use.

16. Capstone Project
Complete an end-to-end Data Science project from data collection to deployment.