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: Predictive Modeling Basics 

  • Data Preparation 

  • Data Cleansing  

  • Integrating Data from Multiple Sources 

  • Common Issues 

 

Module 2: Linear Regression 

  • Predictive vs. Explanatory Modeling Using Regression  

  • Overfitting vs. Underfitting 

  • Splitting Data into Training/Validation subsets 

  • Multicollinearity 

  • Feature Subset Selection Models  

 

Module 3: Classification Models 

  • K- Nearest Neighbor  

  • Distance Function 

  • Similarity Functions  

  • Combination Function 

  • Choosing K 

  • Advantages/Disadvantages  

 

Module 4: Segmentation Modeling/ Cluster Analysis  

  • Clustering 

  • Clustering vs. Classification 

  • K-Means Clustering 

  • Clusters Interpretation 

  • Hierarchical Clustering  

  • Segmentation  

 

Module 5: Spreadsheet Models / Optimization 

  • Linear Optimization Models 

  • Maximizing Profit/ Minimizing Cost  

 

Module 6: Data Analysis Using R  

  • Introduction to R 

  • Data Analysis using R 

  • Reading Data 

  • Data Type in R 

  • Clustering in R 

  • Regression in R  

 

Exercise 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. 

  • Students are required to have Microsoft Excel (and R for Level 2) installed on their laptops. 

Objectives

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

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

To build statistical models replacing the real life situation as closely as possible and to formulate appropriate hypothesis given the content.

to help stsdents acquire effective modeling skills in designing and implementing readable and reliable spreadsheet models. 

To trach students how to to interpret the results of statisitical tests and decision models, and to use them in making decsions. 

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

Register for Class

Date Location
04/21/20 - 04/23/20, 3 days, 8:30AM – 4:30PM Tysons Corner, VA Register
04/21/20 - 04/23/20, 3 days, 8:00AM – 5:00PM Columbia, MD Register
04/21/20 - 04/23/20, 3 days, 8:30AM – 4:30PM Online Register
10/13/20 - 10/15/20, 3 days, 8:00AM – 5:00PM Columbia, MD Register
10/13/20 - 10/15/20, 3 days, 8:00AM – 5:00PM Tysons Corner, VA Register
10/13/20 - 10/15/20, 3 days, 8:00AM – 5:00PM Miramar, CA Register