Class Details

Data and Business Analytics: what is it, how it is used, when it should be used, what it tells you and how it affects our decisions? We will focus on how to deal with analytics and uncertainty, using data, making optimal decisions or creating real life scenarios.  The course introduces the necessary core quantitative methods and the foundations for statistical methodologies used in data analytics. Statistical software and the use of spreadsheets are integrated throughout so that students better comprehend the importance of using technological tools for effective model building and decision-making. This course is data-oriented, exposing students to basic statistical methods, their conceptual underpinning, such as variability and uncertainty, and their use in the real world. Topics include data collection, descriptive statistics, elementary probability rules and distributions, statistical inferences, break-even analysis, regression analysis, and introduction to predictive modeling and optimization models.


Course Outline


Module 1: Introduction to The Course

·         Introduction to Analytics

·         Different Types of Analytics

·         Why are There So Many Different Methods?

·         Terminology and Notation

·         Core Ideas in Data Analytics

·         The Steps in Data Analytics Projects


Module 2: Data Exploration

·         Introduction to Statistics

·         Variable Types

·         Summarizing Data

·         Descriptive Statistics: Measures of Central Tendency

·         Descriptive Statistics: Measures of Variation

·         Statistical Displays: Histograms and Boxplots


Module 3: Excel for Data Analysis

·         Introduction to Excel

·         Sort/Filter/Conditional Formatting

·         Pivot Tables

·         Data Visualization


Module 4: Breakeven Analysis

         Linear Functions

         Revenue and Cost Models

         Exponential Functions

         Curve Fitting

         What-If Analysis / Goal Seek


Module 5: Time Value of Money

         Basics of lending and borrowing


         Present and future value of investments

         Simple Interest

         Compound Interest


Module 6: Probability Models

·         Basic Principles

·         Conditional Probability

·         Discrete Random Variables

·         Continuous Random Variables

·         Normal Distribution

·         Z-score

·         Outlier Detection Method


Module 7: Statistical Inferences

·         Sampling Types / Survey Errors

·         Confidence Intervals

·         t-Distribution

·         Introduction to Hypothesis Testing

·         Single Sample t-Test

·         Type I/II Errors


Module 8: Linear Regression – Part 1

·         Supervised vs. Unsupervised Methods

·         Explanatory vs. Predictive Modeling

·         Correlation

·         Simple Linear Regression

·         Dealing with Categorical Variable in Regression

·         Multiple Linear Regression

·         Fit Measures


Exercises and Software:

-        Within each module, students will be provided with lots of in-class hands on exercises to practice the materials on their own and/or with the guidance of the instructor.

-         Class materials, including lecture notes and exercises will be provided to students.



   To develop one’s ability and confidence in effectively communicate analytical, quantitative, and statistical concepts.

·         To improve analytical thinking and develop effective problem solving strategies and validation techniques for different problem situations.

·         To familiarize students with useful, efficient, and proper methodologies for summarizing and communicating quantitative and qualitative data in Excel.

·        To build statistical models replicating the real life situation as closely as possible and to formulate appropriate hypothesis given the context

·         To help students acquire effective modeling skills in designing and implementing readable and reliable spreadsheet models

·         To teach students how to interpret the results of statisticaltests and decision models, and to use them in making decisions


Register for Class

Date Location
02/03/20 - 02/05/20, 3 days, 8:30AM – 4:00PM Stuttgart, Germany Sold Out!
04/14/20 - 04/16/20, 3 days, 8:30AM – 4:30PM Tysons Corner, VA Register
04/14/20 - 04/16/20, 3 days, 8:30AM – 4:30PM Online Register
04/14/20 - 04/16/20, 3 days, 8:00AM – 5:00PM Columbia, MD Register
10/06/20 - 10/08/20, 3 days, 8:00AM – 5:00PM Tysons Corner, VA Register
10/06/20 - 10/08/20, 3 days, 8:00AM – 5:00PM Online Register
10/06/20 - 10/08/20, 3 days, 8:00AM – 5:00PM Columbia, MD Register