Data Analysis

Introduction

This course offers a practical introduction to key techniques in business-focused work-life-relevant data analysis and econometrics. Students learn to use and process information and data in a critical manner. Students gain hands-on experience with data handling, descriptive statistics, visualization, and learn how to use empirical methods to inform decision-making in businesses. The course will cover a number of data types, including cross-sectional data, time-series data, panel data, and text data. The course also provides a gentle introduction to programming and how to use generative AI tools in data analysis. Students get training in group work, oral presentations, and critical and ethical thinking related to quantitative analysis. 

Course content

1. Data Gathering and Organization:
   1. Students will learn about both quantitative and qualitative surveys, representativity, response rates, and visualizing survey results.
   2. Key statistical concepts such as means, medians, standard deviations, and interquartile ranges will be covered, alongside covariances and correlations. Practical work will include scatter plots, box plots, histograms, and kernel density plots.
   3. A guest lecturer from the business sector, or similar, will discuss data gathering from customers and transactions, current uses, and ambitions for data analysis (e.g., CRM, decision support) as well as challenges related to GDPR. A supporting session with a legal expert will provide guidance on GDPR considerations for students starting their master’s thesis or gathering data in other contexts.
2. Multiple Regression:
   1. Students will learn about multiple regression models, OLS estimation, omitted variable bias, and regression anatomy.
   2. Practical sessions will involve modeling trends in business-relevant datasets, visualizing multiple linear regression models including interaction effects between dummy variables and continuous variables.
   3. Both continuous and binary response variables will be covered.
3. Causality and Experimental Methods:
   1. Students will be introduced to potential outcomes, randomized controlled trials (AB-tests), and the decomposition of sample means into selection effects and causal effects.
   2. Practical exercises will include analyzing data from marketing AB-tests and performing standard t-tests for comparing two population means.
   3. The course will cover the difference-in-differences design as both a graphical and regression-based method, with practical applications from business, where this method is widely used.
4. Forecasting and Time-Series:
   1. Key concepts such as stationarity, trend, cycle and seasonality will be discussed.
   2. Students will learn how to use linear regression for forecasting and also explore machine learning and AI algorithms for predictive analytics.
   3. Typical out-of-sample or cross-validation schemes will be discussed alongside standard loss functions.

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.