Preface

Example of a modern data architecture (MS Azure): https://blog.mashfords.com/2018/06/28/the-emerging-big-data-architectural-pattern/

Figure 0.1: Example of a modern data architecture (MS Azure): https://blog.mashfords.com/2018/06/28/the-emerging-big-data-architectural-pattern/

Prerequisites

Before starting this module make sure you have:

  • access to the book Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.
  • installed R and RStudio
  • a Github account

Purpose of this course

The general learning outcome of this course is:

The student is able to perform a well-defined task independently in a relatively clearly arranged situation, or is able to perform in a complex and unpredictable situation under supervision.

The course will provide you with a non-technical overview of data science, and types of techniques. The focus will lie on critical thinking and the full DS process (based on CRISP).

After a successful completion of the course, a student:

  • is able to translate a business problem into an appropriate setup of the data mining process
  • is able to list commonly applied data mining methods
  • can describe drivers of success for creating a data driven business

Structure of the course

Table 0.1: Course overview
Week nr. Module name Readings
1 Onboarding and Introduction to the Course
2-3 Data-Analytic Thinking Provost / Fawcett Ch.1
4-5 Business Problems and Data Science Solutions Provost / Fawcett Ch.2
6-7 Data Science and Business Strategy Provost / Fawcett Ch.13

The program has been divided into three blocks, each covering two weeks. During each block you’ll be working individually on a case study. Through the whole of the program you’ll be cooperating within a team where you will combine and compare the results of the different case studies. At the end of the course you will present with your team what you have learned from analyzing and comparing the different case studies.