Training on Economic Data Management and Analysis Course

Data-Driven Decision Making: A 10-Day Intensive Program

This course empowers you to make informed decisions using data and communicate your findings effectively. We focus on practical application and insightful interpretation, rather than complex formulas. You'll master core statistical concepts and apply them to real-world business problems using various analytical tools.

Course Objectives

Upon successful completion, participants will be able to:

• Apply economic models to business problems.
• Identify contexts and applications of data in various industries and organizational settings.
• Implement and customize data analysis techniques.
• Utilize diverse data types effectively.
• Analyze data with advanced statistical and econometric methods.
• Develop data-driven business strategies.
• Apply software tools (SPSS, STATA, SAS, Power BI, Excel, R, Python) for data analysis.
• Understand the limitations and ethics of statistical inference.

Who Should Attend?

This program benefits professionals in diverse fields including:

• Management
• Economics
• Consumer Studies
• International Development
• Environmental Sciences
• Data Analytics
• C-level executives
• Government employees
• Decision-makers

Course Structure:

The course is divided into modules focusing on both economic analysis and data analytics, with hands-on application using various software tools. The emphasis is on practical application and clear communication of results.

Module 1: Fundamentals of Economic Analysis and Data

• Introduction to economic analysis and its applications.
• Sources of economic data (micro and macro).
• Basic descriptive statistics.
• Introduction to regression analysis.

Module 2: Economic Forecasting and Time Series Analysis

• Trend analysis and forecasting methods.
• Regression analysis: interpretation of coefficients, significance, standard errors, and serial correlation.
• Case study: Real estate market analysis.

Module 3: Advanced Econometric Techniques

• Fixed effects regressions.
• Addressing omitted variable bias.
• Binary outcome models (logit, probit).
• Difference-in-differences analysis.
• Introduction to Federal Reserve Economic Database (FRED).

Module 4: Applying Economic Forecasts

• Understanding economic output and long-term capital gains.
• Assessing forecast accuracy and scenario analysis.
• Using macro and micro data in forecasting.

Module 5: Microeconomic Analysis and Applications

• Microeconomic principles and their applications to business decisions.
• Market and industrial organization analysis.
• Introduction to game theory.
• Basic econometrics.

Module 6: Corporate Finance and Economic Analysis

• The role of corporate finance in economic analysis.
• Analyzing a firm's financial decisions.
• Using financial models in economic analysis.
• Quantitative case studies.

Module 7: Data Analytics in Diverse Contexts

• Data analysis applications in various sectors (business, education, healthcare, government).

Module 8: Forecasting Methods

• Forecasting demand using regression.
• Causal, time-series, and qualitative forecasting methods.
• Predicting values with regressions.

Module 9: Real-World Data Analysis

• Framing analytical problems.
• Creating conceptual business models.
• Understanding the information-action value chain.
• Data capture by source systems.

Module 10: Data Analytics Tools and Technologies

• Data storage and databases.
• Big data and cloud computing.
• Virtualization, federation, and in-memory computing.
• Relational databases and data tools landscape.

Module 11: Predictive Analytics

• Cross-validation and confusion matrices.
• Assessing predictive accuracy.
• Building logistic regression models (using XL Miner or similar software).

Module 12: Decision Analytics

• Business problems involving yes/no decisions.
• Binary optimization problems.
• Metaheuristic optimization.
• Chance constraints and Value at Risk.
• Simulation optimization.

Software: SPSS, STATA, SAS, Power BI, Excel, R, and Python will be used based on participant preference.