Course Proposal: Introduction to Data Analysis using Excel (Beginner Level)
1. Motivation
Microsoft Excel is one of the most widely used tools in data analysis, finance, research, and business operations.
Despite its accessibility, many students and early professionals underutilize its analytical potential.
This course is designed to provide students with a practical introduction to data analysis using Excel, preparing them
for the demands of both academic tasks and professional environments. Whether students are planning careers in finance,
business, economics, social sciences, or administration, proficiency in Excel will give them a competitive edge.
By making this course inclusive and beginner-friendly, it allows learners from all disciplines to explore how Excel
can serve as a stepping stone toward advanced data skills and professional preparedness.
2. Objectives
Introduce students to Excel as a tool for organizing, analyzing, and visualizing data.
Teach practical skills in creating charts, summaries, and basic statistical analysis in Excel.
Demonstrate Excel’s usefulness in real-world scenarios including finance, project tracking, and academic data.
Provide a foundation for further learning in data science, economics, business analytics, and research.
Equip students from all academic backgrounds with universally applicable data handling skills.
3. Outcomes
Understand the Excel interface and essential functions.
Visualize data using various types of charts and graphs.
Apply basic statistical techniques such as measures of central tendency, t-tests, and ANOVA.
Perform data analysis using PivotTables and linear forecasting.
Manage and organize data for academic or professional tasks.
Use Excel for financial applications including budgeting and financial projections.
4. Target Audience
Students from any discipline looking to build data literacy.
Recent graduates seeking technical skills relevant for the job market.
Professionals aiming to strengthen their Excel capabilities for work-related tasks.
Aspiring analysts, economists, or researchers needing a foundation in Excel.
Weekly Program Outline
Week
Class
Topics & Details
Week 0
Intro Session
Overview of Excel interface
Introduction to spreadsheets, formulas, and cell referencing
Importance of Excel across industries
Basic navigation and file handling
Week 1
Class 1
Scatter plot
Scatter plot with confidence intervals
Line chart
Week 1
Class 2
Pie chart
Histogram
Week 2
Class 3
Measures of Central Tendency (Mean, Median, Mode)
Linkage with chart types
Week 2
Class 4
Data analysis using PivotTables
Sorting and filtering
Week 3
Class 5
Data management using Excel
Application: Result Card creation and analysis
Week 3
Class 6
Forecasting using linear trends
Using trendlines and projection tools
Week 4
Class 7
Comparison of means:
One-sample t-test
Two-sample t-test
Paired t-test
ANOVA
Week 4
Class 8
Simple Linear Regression
Week 4
Special
Excel for Finance:
Budgeting templates
Financial projections
Loan and interest calculations
Application of Excel Across Fields
Field
Use of Excel
Finance
Budgeting, forecasting, cash flow management, and financial modeling
Data visualization, regression, and statistical analysis of indicators
Social Sciences
Survey data analysis, demographic reporting, project planning
Research
Data cleaning, tabulation, hypothesis testing, and visual reporting
Administration
Record keeping, scheduling, payroll, and database management
Course Proposal: Applied Economics Using STATA (Summer Program)
1. Motivation
In an increasingly data-driven world, the ability to analyze and interpret economic data is essential. However, many students and recent graduates often lack exposure to practical tools like STATA, which are vital in both academic research and professional analysis. This course is designed to fill that gap by providing hands-on training in applied economics using STATA. It enables participants to translate theoretical economic concepts into real-world analysis, thus bridging the divide between academia and the practical demands of research, policymaking, and industry.
2. Objectives
Introduce STATA as a versatile software tool used for statistical analysis in economics, policy research, health, finance, and social sciences.
Build foundational skills in data management, cleaning, and visualization using STATA.
Train participants in essential econometric techniques through practical application on real-world datasets.
Develop analytical thinking by encouraging the interpretation of statistical output and drawing policy-relevant conclusions.
Enable career exploration in data analytics, research, policy evaluation, and economics by building technical competency and confidence.
Promote independent research skills through practical labs and reproducible work using Do-files and STATA projects.
3. Outcomes
Be proficient in the basic operations of STATA and capable of independently analyzing datasets.
Understand and apply key econometric methods across different data types (cross-sectional, time-series, and panel).
Create meaningful data visualizations to communicate results effectively.
Build a foundation for entry-level roles in research, analytics, and policy institutions.
Be equipped to pursue further study in economics, data science, and related fields with stronger technical readiness.
4. Target Audience
Undergraduate or graduate students in economics, political science, public policy, business, or related disciplines.
Recent graduates exploring data-intensive career paths.
Individuals aiming to enhance their applied data analysis skills for research or professional development.
Weekly Program Outline
Week
Topics
Details
Week 0
Introduction to STATA
What is STATA and its applications across fields
Setting up and navigating STATA
Importing/exporting data
Data cleaning and management
Basic commands and descriptive statistics
Writing and saving Do-files/log files
Week 1
Data Visualization
Creating scatter plots
Scatter plots with confidence intervals
Line graphs
Histograms
Labeling and customizing graphs for presentations and papers
Week 2
Regression – Cross-Sectional Data
Simple linear regression
Multiple regression
Dummy variables and interpretation
Assumptions and diagnostics
Week 3
Regression – Time Series Data
Understanding time series structure in STATA
Unit root testing (ADF)
Co-integration
Forecasting techniques and ARIMA models
Week 4
Regression – Panel Data
Introduction to panel structure
Pooled OLS
One-way and two-way fixed effects
First difference estimation
Auto-regressive models in panel data
Application of STATA across Fields
Field
Use of STATA
Public Policy
Evaluating impact of government programs and social interventions
Healthcare Economics
Analyzing treatment outcomes, costs, and public health interventions
Development Economics
Studying poverty, inequality, and development indicators over time
Finance & Banking
Modeling financial time series, returns, and risk
Labor Economics
Exploring employment trends, wage determinants, and labor force participation
Education & Research
Conducting empirical research and thesis work with large survey or panel data