:: Instructor-led Classes

Data Analytics: Understanding Customers

Course Details

Students work as a member of the eCommerce Team at an electronics retailer that recently launched an eCommerce website. Using data mining tools and methods, students evaluate online customer sales to provide insight into customer buying patterns and preferences using machine learning methods. Their evaluation of this data will help the sales team make data-driven decisions about how to offer its products. In this course you will be working under Blackwell's Chief Technology Officer Danielle Sherman, as a member of the Blackwell Electronics eCommerce Team. Blackwell Electronics has been a successful consumer electronics retailer in the southeastern United States for over 40 years. Last year, the company launched an eCommerce website. Your job is to use data mining and machine-learning techniques to investigate the patterns in customer sales data and provide insight into customer buying trends and preferences. The inferences you draw from the patterns in the data will help the business make data-driven decisions about sales and marketing activities. First you will install the open source WEKA machine learning package and use it to understand the relationship between customer demographics and purchasing behavior. Next you will use feature selection techniques in WEKA to determine which add-on product a customer will be likely to buy. Finally, you will present to management, explaining your insights and suggestions for data mining process improvements.

Return to the Learn By Doing page.

What you will learn

Nearly all businesses collect data about their operations and examine this data for insight into how to improve their operations. As the amount of data that businesses collect becomes increasingly large, insights from the data can no longer be effectively derived manually. There is a growing trend among companies, organizations, and individuals to exploit data mining's potential to help them discover and act on the most important patterns contained within the data they collect. Data mining has a myriad of business applications and is used increasingly to drive decisions about all aspects of business including spotting sales trends, developing smarter marketing campaigns, accurately predicting customer loyalty, and predicting and protecting against fraud. In fact, data mining can be applied anywhere in a business or organization where a company is interested in identifying and exploiting predictable outcomes. Even if you are not directly responsible for data mining, its ever-increasing prevalence in the business world means that you will likely be working with others who are involved in its use. This requires that you be able to speak with them knowledgeably about data mining--from theory to practical use and strategy. The skills practiced in this course will help you interact with data mining professionals effectively to collect inputs to make business decisions.

What you will do

  • Use data mining tools to investigate patterns in complex data sets
  • Preprocess data for data mining (e.g., transform numeric values to nominal values, discretize data)
  • Use decision tree classifiers to investigate classification and regression problems
  • Apply cross-validation methods
  • Interpret and draw inferences from the results of data mining
  • Assess the predictive performance of classifiers by examining key error metrics
  • Identify where learning methods fail and gain insight into why with error analysis
  • Draw relationships between learner performance and measured features to help understand model performance
  • Conduct feature selection to investigate the correlation between different features in a dataset
  • Present data mining results to management


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