Data Science for Business Course is based on text and video learning materials by Academy Europe.

Data Science for Business is intended for several sorts of readers:

• Business people who will be working with data scientists, managing data science–oriented projects, or investing in data science ventures,
• Developers who will be implementing data science solutions, and
• Aspiring data scientists.

This is not a course about algorithms, nor is it a replacement for a course about algorithms. We deliberately avoided an algorithm-centered approach. We believe there is a relatively small set of fundamental concepts or principles that underlie techniques for extracting useful knowledge from data. These concepts serve as the foundation for many well known algorithms of data mining. Moreover, these concepts underlie the analysis of data-centered business problems, the creation and evaluation of data science solutions, and the evaluation of general data science strategies and proposals. Accordingly, we organized the exposition around these general principles rather than around specific algorithms. Where necessary to describe procedural details, we use a combination of text and diagrams, which we think are more accessible than a listing of detailed algorithmic steps.

The course does not presume a sophisticated mathematical background. However, by its very nature the material is somewhat technical—the goal is to impart a significant understanding of data science, not just to give a high-level overview. In general, we have tried to minimize the mathematics and make the exposition as “conceptual” as possible.

In this course we introduce a collection of the most important fundamental concepts of data science. Some of these concepts are “headliners” for chapters, and others are introduced more naturally through the discussions (and thus they are not necessarily labeled as fundamental concepts). The concepts span the process from envisioning the problem, to applying data science techniques, to deploying the results to improve decision-making. The concepts also undergird a large array of business analytics methods and techniques.

The concepts fit into three general types:

1. Concepts about how data science fits in the organization and the competitive landscape, including ways to attract, structure, and nurture data science teams; ways for thinking about how data science leads to competitive advantage; and tactical concepts for doing well with data science projects.
2. General ways of thinking data-analytically. These help in identifying appropriate data and consider appropriate methods. The concepts include the data mining process as well as the collection of different high-level data mining tasks.
3. General concepts for actually extracting knowledge from data, which undergird the vast array of data science tasks and their algorithms.

Dear Students,

The text and video based courses by Academy Europe are targeted at providing the students with the most important aspects of the theoretical and methodical materials. In addition, these courses will also focus on the philosophical background for the development and use of the theoretical background and are thus to be understood as partly complementary to the material of the course notes. It is assumed and strongly suggested that the students study and become familiar with the course notes.

Digital Support Resources

All of our Higher Education textbooks are accompanied by a range of digital support resources.
Each title’s resources are carefully tailored to the specific needs of the particular course’s readers.
Examples of the kind of resources provided include:
– A password protected area for instructors with, for example, PowerPoint slides, an instructor’s solutions manual and teaching notes for case studies included in the course.
– An area for students including, for example, useful spreadsheets to accompany case studies in the course, multiple choice questions, discussion questions spreadsheets and useful weblinks.

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Certification

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