Data Science Foundation

Week 1: Introduction to Data Science

 • Module 1: Overview of Data Science

 ◦ What is Data Science?

 ◦ Importance and Applications

 ◦ Data Science Lifecycle

 • Module 2: Tools and Technologies

 ◦ Introduction to Python/R

 ◦ Jupyter Notebooks

 ◦ Basic Libraries (NumPy, Pandas)

Week 2: Data Collection and Cleaning

 • Module 3: Data Collection

 ◦ Types of Data (Structured vs. Unstructured)

 ◦ Data Sources (APIs, Web Scraping, Databases)

 • Module 4: Data Cleaning

 ◦ Handling Missing Values

 ◦ Data Transformation

 ◦ Data Normalization

Week 3: Data Analysis and Visualization

 • Module 5: Exploratory Data Analysis (EDA)

 ◦ Descriptive Statistics

 ◦ Data Visualization Techniques (Matplotlib, Seaborn)

 • Module 6: Data Visualization Tools

 ◦ Creating Visualizations

 ◦ Interpreting Visual Data

Week 4: Introduction to Machine Learning

 • Module 7: Basics of Machine Learning

 ◦ Supervised vs. Unsupervised Learning

 ◦ Key Algorithms (Linear Regression, K-Means Clustering)

 • Module 8: Practical Applications

 ◦ Building a Simple Model

 ◦ Model Evaluation and Validation