By their efforts in this course, students should improve in the following course learning outcomes from each chapter:
Chapter 1: Define and describe the following: data mining, machine learning, data visualization, data quality, clustering, classification, predictive analytics.
Chapter 2: Perform key data-mining and machine-learning operations. Compare and contrast supervised and unsupervised learning. Compare and contrast training and testing data sets. Define and describe dimensionality reduction. Define and describe primary-component analysis. Know when and how to apply data-set standard scaling.
Chapter 3: Define database and describe the role of databases in data analytics. Create an entity relationship diagram (ERD) that represents entities and their relationships. Compare and contrast the conceptual, logical, and physical data models. Compare and contrast databases, data warehouses, data marts, and data lakes. Explain the purpose of data normalization and understand the processing required to achieve third-normal form (3NF). Compare and contrast relational, NoSQL, object-oriented, and graph databases.
Chapter 4: Define and describe data visualization. Compare and contrast chart types and the appropriate use of each. Create a variety of charts using Excel. Create HTML-based charts on the web. Use best practices when creating charts.
Chapter 5: Sort and filter data using Excel. Create charts to visualize data using Excel.
Apply conditional formatting to highlight key values. Compare and contrast spreadsheet file formats. Use pivot tables to analyze data and to produce reports. Perform “what if” processing within Excel.
Chapter 6: Define and describe the components of a relational database. Compare and contrast DCL, DDL, and DML queries. Perform complex SQL queries. Compare and contrast SQL JOIN operations. Use SQL aggregation functions and query techniques to group data for reporting.
Chapter 7: Compare and contrast relational and NoSQL databases. Compare and contrast NoSQL database management systems. Perform NoSQL query operations. Understand the role of JSON within NoSQL solutions. Define and describe managed database services.
Chapter 8: Use Python to perform common machine-learning and data-mining operations. Use R to perform common machine-learning and data-mining operations. Compare and contrast Python and R solutions.
Chapter 9: Use Python to perform common machine-learning and data-mining operations. Use R to perform common machine-learning and data-mining operations. Compare and contrast Python and R solutions.
Chapter 10: Define and describe data clustering. Compare and contrast hard and soft clustering. Compare and contrast different clustering algorithms. Describe the purpose of a dendrogram. Visually represent cluster assignments.
Chapter 11: Define and describe data classification. Compare and contrast binary and multiclass classification. Compare and contrast classification algorithms. Define and describe the role of training and testing data sets. Describe the steps to perform the classification process.
Chapter 12: Define and describe predictive analysis. Compare and contrast predictive and prescriptive analysis. Define and describe the regression process. Define and describe regression techniques. Compare and contrast regression algorithms.
Chapter 13: Define and describe data association. Define and describe market-basket analysis. Define and describe support, confidence, conviction, and lift. Use visual programming to implement machine-learning and data-mining solutions.
Chapter 14: Perform text sentiment analysis and categorization. Perform facial recognition. Perform image classification. Understand that text and image mining use the same data-mining techniques you have used throughout this text.
Chapter 15: Perform text sentiment analysis and categorization. Perform facial recognition. Perform image classification. Understand that text and image mining use the same data-mining techniques you have used throughout this text.
Chapter 16: Define and describe data governance. Describe and calculate a return on investment (ROI). Describe and perform a SWOT analysis. Define and describe the PDCA process.