Data Science for Business

Course Director: Joel Smedberg

This course aims to give a better understanding of modern data and computer science in a business context. The students will learn the fundamentals of programming in Python, simpler algorithms, complexity theory/big “O” notation, and the basic concepts surrounding relational databases. To aid this understanding, the students will learn how to use modern tools like Jyputer NoteBooks, Pandas and SQL, as well as Excel.

This course is practically oriented with a hands-on approach to deal with real data and real data challenges. For instance, the students will learn how data driven companies can solve the challenges of efficiently combine and analyze data from heterogeneous sources. Such challenges are further demonstrated in a context of automation and business intelligence.

The structuring and processing of data is today a bare necessity in most work places and is essential and lay the ground for further analysis. The need for people with strong analytical skills who can also harness the power of modern computing will remain in high demand. This course continues the work of previous DA courses and expands on analytical thinking but with a focus on computers. The course also lays the some of the groundwork for DA V and shows where machine learning fits in. Moreover, data analytics is relevant to all academic fields that examine the real world through quantitative data. The material in this course prepares the student for various career paths, including investment banking, financial analysis, economics and marketing analysts, data scientist, and research. By empowering the student to better recognize and understand trends in data it is also essential for entrepreneurs and those undertaking a new venture.

Format & Structure

Lectures, computer labs, help sessions, tutoring and exercises, with the use of Python, SQL and Excel. Explanations and applications relevant to business and economics. Solving real-world business problems, including current topics in Data Science.

Course Administrator: Anna.Burell@hhs.se

After successfully completing this course students should be able to:

1. Write basic scripts to solve automation tasks.
2. Write queries to retrieve and analyse data from a database.
3. Understand scaleability and problems that are scaleable.
4. Efficiently combine and analyze data from heterogeneous sources.
5. Understand the role of data science in a buisness context.

Knowledge corresponding to the courses Data Analytics I-II is required.

No literature is required. The course material is made available online via Canvas. Besides that almost all problems one can encounter in Excel have a solution that has been posted online, so access to google should be enough of a complement to the course material provided.

The only grade granted is Pass

Intended learning outcomes will be assessed through extensive assignments.

This is a pass/fail course, and a grade of pass requires a grade of pass for all assignments.

Presentation (and form for each presentation session): each student book a 20 min timeslot (Mandatory).

Find the course schedule in TimeEdit

Have you been granted educational support because of a documented disability? If so, please contact the course director as soon as possible so that adequate accommodations can be made.

Contact specialneeds@hhs.se if you have questions about being granted educational support or need a copy of your educational support certificate.

For more information, visit the Specific Educational Needs page on the Student Hub (login required).