Statistics with Python Specialization Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
This specialization provides a comprehensive introduction to statistical analysis using Python, designed for beginners with little to no prior experience. Over approximately 12 weeks, learners will progress through three core modules that build foundational skills in data visualization, inferential statistics, and statistical modeling. Each module combines theory with hands-on coding exercises using real-world datasets in Jupyter notebooks. The course emphasizes practical application with Python libraries such as Pandas, Statsmodels, and Seaborn, enabling learners to analyze data, conduct hypothesis tests, and build regression models. With a balanced approach to classical and Bayesian methods, this course prepares learners for data-driven roles across industries.
Module 1: Understanding and Visualizing Data with Python
Estimated time: 16 hours
- Data types and variable classification
- Exploratory data visualization using histograms and box plots
- Summary statistics and distribution interpretation
- Sampling methods and study design considerations
Module 2: Inferential Statistical Analysis with Python
Estimated time: 16 hours
- Constructing confidence intervals
- Performing hypothesis tests (one-sample and two-sample)
- Distinguishing between statistical significance and practical relevance
- Applying inference procedures using Pandas and Statsmodels
Module 3: Fitting Statistical Models to Data with Python
Estimated time: 16 hours
- Linear regression modeling and interpretation
- Logistic regression for binary outcomes
- Introduction to multilevel models
- Bayesian inference techniques and frameworks
Module 4: Applied Data Analysis in Python
Estimated time: 8 hours
- Integrating visualization, inference, and modeling workflows
- Using Seaborn for advanced data plotting
- Aligning statistical results with research questions
Module 5: Case Studies in Statistical Thinking
Estimated time: 12 hours
- Applying statistical methods to real-world datasets
- Interpreting results in context
- Comparing classical and Bayesian approaches
Module 6: Final Project
Estimated time: 20 hours
- Conduct a full analysis using Python on a provided dataset
- Produce visualizations, perform inference, and fit statistical models
- Submit a report interpreting findings and methodological choices
Prerequisites
- Familiarity with basic Python programming
- High school level mathematics
- No prior statistics or college-level math required
What You'll Be Able to Do After
- Identify data types and implement exploratory visualizations in Python
- Conduct statistical inference including confidence intervals and hypothesis tests
- Fit and interpret linear and logistic regression models
- Apply Bayesian and classical statistical frameworks to real data
- Produce reproducible data analyses using Python tools