Introduction to Data Science with Python

Module 1: Python for Data Science Setup

⏳ 1 week

• Topics: Conda environments, Jupyter notebooks, Python basics refresher

• Hands-on: Configure your environment and load CSV/JSON data into pandas

Module 2: Numerical Computing with NumPy

⏳ 1 week

• Topics: ndarray operations, broadcasting, vectorized computations

• Hands-on: Analyze large numeric arrays for summary statistics and transformations

Module 3: Data Wrangling with pandas

⏳ 1 week

• Topics: DataFrame creation, indexing, grouping, merging, and pivot tables

• Hands-on: Clean and reshape a messy dataset with missing values and inconsistent formats

Module 4: Data Visualization

⏳ 1 week

• Topics: Matplotlib fundamentals, Seaborn plot types, customizing aesthetics

• Hands-on: Create histograms, boxplots, heatmaps, and multi-facet visualizations to tell a story

Module 5: Exploratory Data Analysis (EDA)

⏳ 1 week

• Topics: Outlier detection, correlation analysis, feature engineering basics

• Hands-on: Perform end-to-end EDA on a public dataset to identify key drivers and relationships

Module 6: Statistics for Data Science

⏳ 1 week

• Topics: Descriptive statistics, probability distributions, confidence intervals, t-tests

• Hands-on: Test hypotheses (e.g., A/B test scenario) and interpret p-values

Module 7: Introduction to Machine Learning

⏳ 1 week

• Topics: Supervised learning workflow, train/test split, regression vs. classification, overfitting

• Hands-on: Build and evaluate a linear regression and a decision-tree classifier using scikit-learn

Module 8: Capstone Project

⏳ 1 week

• Topics: Problem scoping, model selection, performance metrics, storytelling with insights

• Hands-on: Complete a mini data science project: from data ingestion through model deployment mock-up

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