Introduction to Data Science with Python Course Syllabus

Overview: This course provides a hands-on introduction to data science using Python, guiding you from setup to a capstone project over 8 weeks. Each module combines theory with practical labs using real-world datasets, totaling approximately 40-50 hours of learning. You'll gain fluency in Python's core data science libraries and build a foundational workflow for data analysis and modeling.

Module 1: Python for Data Science Setup

Estimated time: 6 hours

• Set up Conda environments for data science
• Launch and navigate Jupyter notebooks
• Review Python basics for data workflows
• Load and inspect CSV and JSON data using pandas

Module 2: Numerical Computing with NumPy

Estimated time: 6 hours

• Work with ndarray objects and data types
• Perform vectorized operations and broadcasting
• Compute summary statistics on numeric arrays
• Apply transformations to large numerical datasets

Module 3: Data Wrangling with pandas

Estimated time: 7 hours

• Create and manipulate DataFrames
• Handle missing values and inconsistent formats
• Use indexing, grouping, and merging operations
• Reshape data with pivot tables and melting

Module 4: Data Visualization

Estimated time: 7 hours

• Generate plots using Matplotlib fundamentals
• Create statistical visualizations with Seaborn
• Customize aesthetics and multi-facet plots
• Tell stories with histograms, boxplots, and heatmaps

Module 5: Exploratory Data Analysis (EDA)

Estimated time: 7 hours

• Detect outliers and assess data quality
• Analyze correlations and relationships
• Perform feature engineering basics
• Conduct end-to-end EDA on public datasets

Module 6: Statistics for Data Science

Estimated time: 7 hours

• Apply descriptive statistics to datasets
• Interpret probability distributions and confidence intervals
• Conduct hypothesis testing with t-tests
• Analyze A/B test scenarios and interpret p-values

Module 7: Introduction to Machine Learning

Estimated time: 7 hours

• Understand supervised learning workflows
• Split data into training and testing sets
• Compare regression and classification tasks
• Build and evaluate models using scikit-learn

Module 8: Capstone Project

Estimated time: 10 hours

• Scope a real-world data problem
• Apply end-to-end workflow: cleaning, EDA, modeling
• Create visualizations and performance reports

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental math concepts
• No prior data science experience required

What You'll Be Able to Do After

• Utilize NumPy, pandas, Matplotlib, and Seaborn effectively
• Clean and transform messy real-world datasets
• Perform exploratory data analysis and visualize insights
• Apply statistical methods to test hypotheses
• Build and evaluate basic machine learning models