Understanding and Visualizing Data with Python Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This beginner-friendly course offers a hands-on introduction to statistical thinking and data visualization using Python. Over four weeks, learners will explore data types, create visualizations, analyze relationships, and understand sampling methods. Each module includes practical labs in Jupyter Notebooks using core Python libraries. Total time commitment: approximately 16–20 hours.
Module 1: Introduction to Data & Statistical Thinking
Estimated time: 4 hours
- Identify types of data: categorical and quantitative
- Understand study design and data collection methods
- Introduction to Jupyter Notebook environment
- Hands-on: Variable identification and Python basics
Module 2: Univariate Visualizations & Summaries
Estimated time: 4 hours
- Create bar charts and histograms for categorical data
- Construct box plots for quantitative data
- Calculate and interpret numerical summaries: mean, median, IQR
- Apply standard scores (z-scores) to compare data points
Module 3: Multivariate Relationships & Association
Estimated time: 4 hours
- Explore relationships between two quantitative variables
- Visualize associations using scatter plots
- Interpret correlation structures in real-world datasets
- Analyze relationships involving categorical and quantitative variables
Module 4: Sampling, Inference & Interpretation
Estimated time: 4 hours
- Differentiate probability vs non-probability sampling methods
- Understand sampling variability and its implications
- Apply reasoning to generalize sample findings to populations
- Evaluate sample design examples for bias and reliability
Prerequisites
- Basic computer literacy
- Familiarity with fundamental math concepts
- No prior programming experience required
What You'll Be Able to Do After
- Identify and classify different types of data
- Create clear and informative data visualizations using Python
- Summarize and interpret univariate and multivariate datasets
- Apply sampling concepts to assess statistical claims
- Use Pandas, Matplotlib, and Jupyter Notebooks for data analysis