Foundations of Descriptive Analytics- Part 2
-
- 0 Rating
- 0 Reviews
- 2 Students Enrolled
Foundations of Descriptive Analytics- Part 2
In this section, you'll dive into the essentials of descriptive analytics, focusing on its scope, flavors, and business applications. You will also explore cluster analysis methods, including goal formalization, similarity measures, and the k-means algorithm. This foundational knowledge will prepare you for more advanced topics in business analytics and data science. The next recommended section will cover predictive analytics. No strict prerequisites are required, but continuous engagement with the provided materials is essential for a comprehensive understanding.
-
- 0 Rating
- 0 Reviews
- 2 Students Enrolled
Requirements
- 1. Foundations of Descriptive Analytics
General Overview
Description
Welcome to the "2.Foundations of Descriptive Analytics" section of our Business Analytics and Data Science course. This part is designed to ground you in the fundamental principles and practices of descriptive analytics, setting the stage for more complex analytical techniques. Here’s what you can expect:
-
Descriptive Analytics in a Nutshell
- Scope and Flavors: Gain a clear understanding of the various aspects of descriptive analytics, including its scope and different approaches.
- Business Applications: Learn about the practical applications of descriptive analytics in business, helping you see how these techniques can be applied to real-world scenarios.
-
Cluster Analysis Methods
- Goal Formalization: Understand the goals of cluster analysis and how to formalize them in a structured way.
- Similarity Measures: Explore different similarity measures used in cluster analysis to determine how closely related data points are.
- The k-means Algorithm: Dive deep into the k-means algorithm, a fundamental method in cluster analysis, and learn how to apply it to segment data into meaningful clusters.
Learning Objectives
By the end of this section, you will be able to:
- Define and explain the core concepts and scope of descriptive analytics.
- Identify and describe various business applications of descriptive analytics.
- Understand and formalize the goals of cluster analysis.
- Apply different similarity measures to assess the relationships between data points.
- Implement the k-means algorithm to perform cluster analysis.
Next Steps
Upon completing this section, you are recommended to proceed to the section on predictive analytics. There are no strict prerequisites for this section, but for the best learning experience, ensure continuous engagement with the videos and accompanying PDFs.
Recommended for you
Meet the instructors !
I'm Saloni, a student assistant at HU Berlin. Currently, I'm immersed in managing video content and crafting courselets for business analytics.
Stefan received a PhD from the University of Hamburg in 2007, where he also completed his habilitation on decision analysis and support using ensemble forecasting models in 2012. He then joined the Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics. He serves as an associate editor for the International Journal of Business Analytics, Digital Finance, and the International Journal of Forecasting, and as department editor of Business and Information System Engineering (BISE). Stefan has secured substantial amounts of research funding and published several papers in leading international journals and conferences. His research concerns the support of managerial decision-making using quantitative empirical methods. He specializes in applications of (deep) machine learning techniques in the broad scope of marketing and risk analytics. Stefan actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations.