Applied Data Science

BAN426 Applied Data Science

Spring 2019

  • Topics

    Data science is the discipline of making data useful. It is a multidisciplinary field that lies at the intersection of mathematics/statistics, computer science and subject matter expertise. While technology companies were among the first to apply the techniques of data science to extract actionable knowledge from the large amount of data they collect, industries other than tech (medicine, finance, retail, automotive, etc.) are also making great strides in how they extract value from data.

    We will focus on some of the important components of data science, such as:

    • Business Intelligence - which is turning the company's data into the actionable form of dashboards and reports
    • Machine Learning - which is, at its root, an algorithmic technique to devise predictive models, where the data generating process is treated as unknown
    • Ethics - which includes the social responsibility around deploying predictive models that have been "trained" on potentially biased data

    As this is an applied seminar, the focus will be on practical applications of the material in a business setting. Academic theory will be discussed only to the extent that it is used in practice.

  • Learning outcome

    Knowledge - the student will know

    • what the various components of data science are and how they fit into the broader field
    • how data science is used across various industries
    • how to interpret the output from exploratory data analysis

    Skills - the student will be able to

    • build a dashboard using a commercial business intelligence system
    • consider possible sources of bias in a dataset
    • visually analyze data to draw initial conclusions

    General competence - the student will be able to

    • visually communicate findings to decision makers
    • convincingly argue the case for their conclusions
    • present their work for an audience

  • Teaching

    One-week intensive course with standard lectures and practical sessions with business intelligence software.

  • Requirements for course approval

    Mandatory attendance.

  • Assessment

    A group presentation (2-5 students in a group) on a given topic.

  • Grading Scale

    Pass / Fail.

  • Computer tools

    MS Office suite, Tableau Desktop (which students can license for free).

  • Literature

    Wexler et al. (2017), The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios (Wiley).

    Other reading will be assigned as necessary.  

Overview

ECTS Credits
2.5
Teaching language
English.
Semester

Spring. Last week of the spring semester 2019.

Course responsible

Adjunct Associate Professor Nikhil Atreya, Department of Business and Management Science.