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 summarizing 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 through the Participants as preferences.
Basics of economic analysis
Sources of economic data
Microeconomic data
Macroeconomic data
Economic forecasting methods
Regression analysis in economics
Trend analysis in forecasting
Case study – real estate
Coefficients
Significance
Standard errors
Serial correlation in data
Analyzing results
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
Understanding economic output
Long-term capital gains rate
Forecast accuracy
Scenario analysis
Using macro and microeconomic data in forecasts
Understanding microeconomic analysis
Corporate strategic decisions
Market and industrial organization
Game theory
Econometrics
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
Data Analysis in Context
Data Analysis for Business
Data Analysis for Education
Data Analysis for Healthcare
Data Analysis for Government
Forecasting demand and regression
Causal methods
Time-series methods
Qualitative methods
Predicting values with regressions
Thinking about Analytical Problems
Conceptual Business Models
The Information-Action Value Chain
Real-World Events and Characteristics
Data Capture by Source Systems
Data Storage and Databases
Big Data & the Cloud
Virtualization, Federation, and In-Memory Computing
The Relational Database
Data Tools Landscape
The Tools of the Data Analyst
Cross-Validation and Confusion Matrix
Assessing Predictive Accuracy Using Cross-Validation
Building Logistic Regression Models using XL Miner
How to Build a Model using XL Miner?
Business Problems with Yes/No Decisions
Formulation and Solution of Binary Optimization Problems
Metaheuristic Optimization
Chance Constraints and Value at Risk
Simulation Optimization
Apply economic models to business problems
Identify contexts and applications of data in specific industries and in organizational settings
Implement conventional data analysis techniques and customizing them for exceptional circumstances
How to utilize different types of data in different scenarios
Analyze data with advanced statistical and econometric techniques
Learn and employ techniques of data analysis to form business strategies
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