Current Topics in Data Science for Business

Course Director: Rickard Sandberg

Content
In this course, the students advance their understanding of Machine Learning (ML), using R and Python, learned in the sequence of DA I-IV courses. The students will have a hands-on approach (using real data sets) to problems of multi-variables, non-linear regressions, classification and prediction models using Supervised Learning. This will also serve as the foundations to a brief introduction to Deep Learning (DL) with Artificial Neural Networks (ANN) simplified and brought to practice. Moreover, the topics of Unsupervised Learning, such as various clustering techniques and principal component analysis, and Reinforcement Learning, such as designing product recommendation systems, are also covered.

Applications and practical implications are addressed in this course, and examples of fraud detection, computer vision, campaign targeting, and linguistical understanding for the analysis of e.g. social media (basic text analysis) are discussed. In addition, the students will get inspiration by guest lectures from the Swedish business world.

Format & Structure
Lectures, computer labs, tutoring, guest lecture(s) and exercises, with the use of R and Python. Explanations and applications relevant to business and economics. Solving real-world business problems, including current topics in Data Science, using R and Python.

Relevance to the Students
The Data Analytics V course will help foster an understanding of the relevance of data in a business setting, allowing the students to better grasp various business cultures and perspectives. The material covered will be extra valuable for students planning to analyze data and write a more qunatitative BSc-thesis. Moroever, data analytics is relevant to all academic fields that examine the real world through quantitative data, from numeric and text financial and economic data to e.g. survey data used within marketing and management. The material prepares the student for various career paths, including investment banking, financial analysis, economics and marketing analyst, 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.

Course Administrator: Anna.Burell@hhs.se

Intended Learning Outcomes After successfully completing this course students should be able to:

1. Solve problems that require knowledge about more advanced concepts of Econometrics and ML as well as basic understanding of DL and text analysis.
2. Recognize the importance of data science to businesses.
3. Perform more advanced Econometric and ML analysis, and basic DL and text analysis, on real-world data sets.

General entry requirements

• Introduction to Data Science - Data Analysis and Prediction Algorithms with R. (This textbook is also core literature in the Data Analytics I-III courses).
• Tibshirani, Robert., An Introduction to Statistical Learning - with Applications in R (Available as a free PDF), 2nd Ed.
• Silge, Julia and Robinson, David., Text Mining with R - a tidy Approach. (Available for free online)

The only grade granted is Pass

All intended learning outcomes will be assessed through extensive assignments (Mandatory). 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).