Excerpt from course description

Big Data

Introduction

The modern world generates data at an incredible rate.  This has profound impact on how businesses are run, investors make their decisions, and how we apply financial theory to navigate the world. This course provides a first introduction to essential quantitative tools to take advantage of data available and to analyze data through the lenses of financial theory.  

To be ready for today’s competitive labor market and being able to quickly and precisely analyze and visualize data, students should master quantitative tools. Everyone uses software in the modern world, but we would be kidding yourself if we think that this only means spreadsheets such as Excel, OpenOffice or Google Sheets. Students give themselves a huge advantage if they learn to write programs -- to code.

In this course, students will therefore learn both to use spreadsheets (such as Excel, OpenOffice or Google Sheets), and elementary programming using R. R is today widely used in business, in general, and in the financial industry, in particular. It is a general programming language that also allows statistical analysis and handling of large datasets. R is open source, which has, at least, three advantages: it is a dynamic and evolving project with a huge amount of contributors around the world, there are extensive, freely available online resources, and it is free.

Students will learn to use spreadsheets and elementary coding through analyzing a set of topical questions in finance using data and visualization techniques.  This course will be "hands-on" and prepare students to provide quantitative answers to question facing the organization they are working for. This course is intended to be both fun and interesting. Students will learn to work together. Several teaching methods will be utilized in the course. 

Students will develop basic skills that will be useful in their studies of finance, and later, in professional life. In most entry-level positions, ability to efficiently gather and process data is a crucial skill. Spreadsheets are widely used and ability to use spreadsheets fast and efficiently are expected by almost all employers. Programming skills are also expected by more and more employers. Combining programming skills with financial insight is important in the financial industry, in business in general, and in public administration. Below you find examples where programming is useful:

  • Analysis of companies’ investment and finance decisions (this also applies to non-profit organizations)
  • Data based analysis for portfolio and investment strategies in financial markets
  • Analysis of big data sets in order to provide insight before making important decisions
  • Automating processes relevant for buying and selling stocks, foreign exchange, and other securities
  • Financial advice

R and spreadsheets (Excel, OpenOffice and Goggle sheets) are used in this course. R is a market leading programming language, and is used by e.g. NBIM (“Oljefondet”, Norges Bank Investment Management) and many financial institutions. 

Course content

  1. Introduce interesting issues that are highlighted in finance
  2. Understand the financial system and how different participants must relate to finance
  3. The Oil Fund and the Norwegian economy
  4. Introduction to R and spreadsheets (Excel/OpenOffice/Google sheets)
  5. Obtain relevant data for macro and financial markets, for example from:
    1. FRED database: Macro aggregates for USA
    2. OECD and IMF: Macro aggregates for the rest of the world
    3. Statistics Norway and Norges Bank: Macro aggregates for Norway
    4. World Federation of Exchanges: Data for a number of exchanges globally
    5. Penn World Tables: Economic and demographic variables for a number of countries over time. See also Gapminder (https://www.gapminder.org/)
    6. Google Trends: What’s hot and what’s not on internet
    7. Economic Policy Uncertainty Index: http://www.policyuncertainty.com/
    8. Financial News Index: https://www.retriever-info.com/fni/
    9. Process and analyze data
    10. Visualize data and discuss results

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.