Economic Data Management and Analytics Course

Course Overview

In today's world, good decision making relies on data and data analysis. This course helps participants develop the understanding that they will need to make informed decisions using data, and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for participants in business, economics and similar disciplines. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require participants to write effectively and communicate their ideas with clarity. The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers. By the end of this course, participants should understand and know how to use statistics. Participants will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics. Participants will work in small groups in this course. Software like SPSS, STATA,SAS,POWER BI,EXCEL,R AND PYTHON will be used as per the participants preferences

Course Outline

MODULE 1: THE BASICS

• Basics of economic analysis
• Sources of economic data
• Microeconomic data
• Macroeconomic data
• Economic forecasting methods
• Regression analysis in economics

MODULE 2: ECONOMIC CYCLES

• Trend analysis in forecasting
• Case study – real estate
• Coefficients
• Significance
• Standard errors
• Serial correlation in data
• Analysing results

MODULE 3: FORECASTING ECONOMIC TRENDS

• Fixed effects regressions
• Omitted variables bias
• Binary outcome
• Binary regressions
• Logit models
• Probit models
• Advanced regression applications
• Federal Reserve Economic Database (FRED)
• Difference-in-differences analysis
• Difference-in-differences estimator

MODULE 4: USE ECONOMIC FORECASTS

• Understanding economic output
• Long-term capital gains rate
• Forecast accuracy
• Scenario analysis
• Using macro and microeconomic data in forecasts

MODULE 5: MICROECONOMIC ANALYSIS

• Understanding microeconomic analysis
• Corporate strategic decisions
• Market and industrial organisation
• Game theory
• Econometrics

MODULE 6: CORPORATE FINANCE

• Understanding the role of corporate finance in economic analysis
• Analysis of a firm’s financial decisions
• Use of financial models in economics
• Quantitative case studies

MODULE 7: DATA ANALYTICS

• Data Analysis in Context
• Data Analysis for Business
• Data Analysis for Education
• Data Analysis for Healthcare
• Data Analysis for Government

MODULE 8: FORECASTING METHODS

• Forecasting demand and regression
• Causal methods
• Time-series methods
• Qualitative methods
• Predicting values with regressions

MODULE 9: DATA AND ANALYSIS IN THE REAL WORLD

• Thinking about Analytical Problems
• Conceptual Business Models
• The information-Action Value Chain
• The information-Action Value Chain
• Real-World Events and Characteristics
• Data Capture by Source Systems

MODULE 10: ANALYTICAL TOOLS

• Data Storage and Databases
• Big Data & the Cloud
• Virtualisation, Federation, and In-Memory Computing
• The Relational Database
• Data Tools Landscape
• The Tools of the Data Analyst

MODULE 11: PERFORM PREDICTIVE ANALYTICS TASKS

• Cross-Validation and Confusion Matrix
• Assessing Predictive Accuracy Using Cross-Validation
• Building Logistic Regression Models using XLMiner
• How to Build a Model using XLMiner

MODULE 12: DECISION ANALYTICS

• Business Problems with Yes/No Decisions
• Formulation and Solution of Binary Optimisation Problems
• Metaheuristic Optimisation
• Chance Constraints and Value at Risk
• Simulation Optimization