Course Overview:
This professional online course is designed for academics, PhD students, and professors seeking to enhance their data analysis skills in economics and social sciences. The course covers theoretical foundations, practical applications, and advanced techniques in data analysis using contemporary tools and software.
Course Objectives:
Understand the principles of data analysis in economics and social sciences.
Develop skills in handling and analysing various types of data.
Apply statistical and econometric methods to real-world datasets.
Use software tools such as R, Python, Stata, and SPSS for data analysis.
Target Audience:
Academics in economics, sociology, political science, and related fields.
PhD students conducting research that involves data analysis.
Professors and researchers looking to update their data analysis techniques.
Course Structure: The course is divided into six modules, each containing lectures, readings, assignments, and practical exercises. Participants will engage with video lectures, interactive tutorials, and peer discussions.
Module 1: Introduction to Data Analysis
Week 1: Fundamentals of Data Analysis
Overview of data analysis in economics and social sciences.
Types of data: cross-sectional, time series, and panel data.
Week 2: Data Collection and Cleaning
Methods of data collection: surveys, experiments, and observational studies.
Data cleaning and preparation techniques.
Readings:
"Data Analysis for Social Science: A Friendly and Practical Introduction" by Elena Llaudet and Kosuke Imai.
"Principles of Econometrics" by R. Carter Hill, William E. Griffiths, and Guay C. Lim.
Module 2: Descriptive Statistics and Data Visualization
Week 3: Descriptive Statistics
Measures of central tendency and variability.
Summary statistics and their interpretation.
Week 4: Data Visualization
Principles of effective data visualization.
Creating charts and graphs using R and Python.
Readings:
"The Visual Display of Quantitative Information" by Edward R. Tufte.
"R for Data Science" by Hadley Wickham and Garrett Grolemund.
Module 3: Inferential Statistics
Week 5: Probability and Distributions
Basic probability concepts and common probability distributions.
Sampling distributions and the Central Limit Theorem.
Week 6: Hypothesis Testing and Confidence Intervals
Formulating and testing hypotheses.
Constructing and interpreting confidence intervals.
Readings:
"An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
"Discovering Statistics Using IBM SPSS Statistics" by Andy Field.
Module 4: Econometric Methods
Week 7: Regression Analysis
Simple and multiple linear regression.
Assumptions of the regression model and diagnostic tests.
Week 8: Advanced Econometric Techniques
Instrumental variables, panel data models, and time series analysis.
Dealing with endogeneity and autocorrelation.
Readings:
"Econometric Analysis" by William H. Greene.
"Introductory Econometrics: A Modern Approach" by Jeffrey M. Wooldridge.
Module 5: Practical Data Analysis
Week 9: Data Analysis with R
Introduction to R programming for data analysis.
Data manipulation, visualization, and regression analysis in R.
Week 10: Data Analysis with Python
Introduction to Python for data analysis.
Data manipulation, visualization, and machine learning with libraries like pandas and scikit-learn.
Readings and Software Tutorials:
"Python for Data Analysis" by Wes McKinney.
R and Python official documentation and tutorials.
Module 6: Applications and Case Studies
Week 11: Applications in Economics and Social Sciences
Applying data analysis techniques to real-world datasets.
Case studies on labor economics, health economics, and social behavior.
Week 12: Student Projects and Presentations
Participants develop their own data analysis projects on chosen topics.
Presentation of projects and peer feedback.
Readings:
"Mostly Harmless Econometrics: An Empiricist's Companion" by Joshua D. Angrist and Jörn-Steffen Pischke.
Selected articles from the American Economic Review and the Journal of Political Economy.
Assessment and Certification:
Assignments:
Weekly problem sets and practical exercises.
Mid-term project involving data analysis and interpretation.
Final Project:
Comprehensive data analysis project on a topic of choice.
Presentation and peer review.
Certification:
Participants who complete all modules, assignments, and the final project will receive a certificate of completion.
Course Delivery:
The course will be delivered through a combination of pre-recorded video lectures, live Q&A sessions, interactive tutorials, and discussion forums.
All course materials, including readings, software guides, and lecture slides, will be available online.
Instructor:
The course will be led by a team of experienced economists and social scientists with extensive expertise in data analysis.
Enrollment:
Participants can enroll through the university’s online learning platform.
Enrollment will be open to individuals with a foundational knowledge in statistics and econometrics.
By the end of this course, participants will have a robust understanding of data analysis in economics and social sciences, equipped with the skills to conduct rigorous research and analysis in their respective fields.