Statistics and Data Science (Time Series and Social Sciences Track) course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Probability and Statistical Foundations
Estimated time: 64 hours
- Random variables and probability distributions
- Hypothesis testing
- Confidence intervals
- Statistical inference
Module 2: Regression and Econometrics
Estimated time: 64 hours
- Linear regression models
- Logistic regression models
- Causal inference methods
- Econometric modeling techniques
Module 3: Time Series Analysis
Estimated time: 64 hours
- AR, MA, and ARIMA models
- Stationarity, seasonality, and autocorrelation
- Forecasting techniques
- Structural breaks and intervention analysis
Module 4: Stochastic Processes and Model Diagnostics
Estimated time: 40 hours
- Introduction to stochastic processes
- Model diagnostics
- Predictive analytics
Module 5: Policy Evaluation and Causal Inference
Estimated time: 40 hours
- Econometric methods for policy evaluation
- Causal inference in dynamic systems
- Intervention models for policy or market events
Module 6: Final Project
Estimated time: 20 hours
- Comprehensive analysis using time series models
- Application of causal inference techniques
- Policy impact evaluation report
Prerequisites
- Background in college-level statistics
- Familiarity with linear algebra and calculus
- Basic programming experience (preferably in R or Python)
What You'll Be Able to Do After
- Analyze time-dependent data using ARIMA and related models
- Conduct causal inference for policy evaluation
- Build and validate econometric models
- Forecast trends in economic and social data
- Evaluate the impact of interventions using statistical methods