Data Analytics: Tools for Big Data

Data analytics is a set of techniques that enterprises use to gain insight from their data and make better decisions. Many firms in a variety of industries use these techniques: Google, Amazon, Target, Coca-Cola, WalMart, Capital One. These techniques are also
applicable to the many functional areas of business, such as operations, marketing, accounting, finance, etc. Furthermore, the modern abundance of data, so-called “Big Data,” underscores the value that analytics can provide a firm, be it non-profit, for-profit, or government.

This course introduces data analytic techniques via quantitative tools and sophisticated software (R, Radiant and Tableau). These techniques are drawn from machine learning, data mining, and optimization. Note that this is not a technical or theoretical course. This
course does not aim to produce experts in statistical analysis; rather, the aim is to provide students competency to interact with and manage a team of analytics professionals. Furthermore, this is not a technical or theoretical course; we will instead focus on the application of analytics techniques to real business situations, with the aim of creating insight and value.

The course is divided into the following six main modules under three topic areas of Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics:
1. Introduction and Visualization (Descriptive and Inferential Analytics)
2. Supervised Learning: Prediction (Predictive Analytics)
3. Supervised Learning: Classification (Predictive Analytics)
4. Unsupervised Learning: Clustering (Descriptive and Predictive Analytics)

Course leader

Mamani Hamed, University of Washington, Seattle, United States

Target group

Master students as well as recent graduates and young professionals who wish to acquire new knowledge in specific areas

Course aim

Our course goals are the following:
1. Students should be able to think critically about data analysis, which includes selecting the right type of analysis for a given task.
2. Students should be able to identify opportunities of applying data analytics, in real business settings.
3. Students should be well equipped to become data-savvy managers.

To achieve the above goals, lectures will cover the major concepts and analytical tools. Cases and practice problems will allow you to analyze different industry settings, analyze different company strategic problems, and identify key issues related to data and modeling.

Credits info

7 EC
Certificate of Attendance: awarded at the end of the summer school to all students who complete the 3-week programme.
Transcript of records (with credits and grades): awarded only to students who complete all course obligations and pass the final examination.

Fee info

EUR 650: You can find more information on our website.

Scholarships

None.

Register for this course
on course website