COURSE OUTLINE

Day 1: Foundations and Data Manipulation

Focus: Understanding the field and mastering the tools of the trade.

• What is Data Science? The intersection of statistics, computer science, and domain expertise.
• Introduction to Python for Data: Setting up Jupyter Notebooks and learning NumPy fundamentals.
• Pandas Deep Dive:
• Loading data (CSV, Excel, SQL).
• Slicing, dicing, and filtering dataframes.
• Dealing with missing values and "dirty" data.
• Hands-on Lab: Taking a raw "Retail Sales" dataset and transforming it into a clean, structured format for analysis.

Day 2: Exploratory Data Analysis & Visualization

Focus: Extracting meaning and visualizing the "Why" behind the numbers.

• Statistical Thinking: Understanding mean, median, standard deviation, and distributions.
• Exploratory Data Analysis (EDA): Techniques for spotting trends, outliers, and correlations.
• The Art of Visualization:
• Choosing the right chart (Bar, Line, Scatter, Heatmap).
• Customizing plots for clarity and impact using Seaborn.
• Hands-on Lab: Visualizing a "Global Health" dataset to identify key factors influencing life expectancy across different regions.

Day 3: Modeling, Ethics, and the Future

Focus: Predicting the future and staying responsible.

• Introduction to Machine Learning: The difference between "Classic" programming and "Learning" from data.
• Your First Models:
• Regression: Predicting numerical values (e.g., forecasting revenue).
• Classification: Categorizing data (e.g., Identifying spam vs. not spam).
• The Ethical Data Scientist: Recognizing bias in algorithms and the importance of GDPR/Data Privacy.
• Final Project: Building a "mini-analysis" end-to-end—from cleaning a dataset to presenting a final predictive insight.

The Road Ahead: Careers in Data Science and building your professional portfolio.