Studies in Statistics

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You can study Statistics as a minor subject in Helsinki and Vaasa irrespective of your major. At a Master level, you can choose Statistics as a specialization within the subject of Finance in Helsinki, and likewise, at a PhD level.

Studying Statistics at Hanken will enable you to:

  • use mathematics to describe relationships in the economy
  • model and test those relationships using statistical and econometric models
  • use programming languages such as R and Python to fit statistical models
  • analyse economic data, including big data
  • produce dynamic reports with text and code using Markdown and LaTeX

Statistics as a minor is an excellent combination with majors such as Finance and Economics - it will help you to master your major subject! For these and other subjects, it will equip you with hand-on experience in performing data analyses, which forms an essential part of Bachelor and Master theses, and makes you competitive on the job market.

Examples of analyses you will be carrying out during your studies in Statistics

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sompca2022
SOM-based and PCA-based (SOM=Self-Organizing Map, PCA=Principal Component Analysis) results obtained in software R. Input data: 40x9 matrix containing 40 sets of estimates of alpha, beta, s, h, r, c, IVOL, Sharpe Ratio, Treynor Ratio from the fit of Fama-French-5-factor model to daily returns of 20 randomly-selected S&P500 stocks between 2017-2022. From Agnieszka Jach's course "Mathematical and Quantitative Finance".
cluster2
Clustering results obtained in software R for the states of the US based on the arrests per 100,000 residents for assault, murder, and rape and percent of the population living in urban areas, using hierarchical clustering (correlation-based distance and complete linkage). From Agnieszka Jach's course "Multivariate Data Analysis".

 

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Log-likelihood function drawn in Python (run within Jupyter Notebook) for a sample of five iid observations from Poisson distribution with unknown parameter lambda (estimate of lambda is the value at which log-likelihood is maximized, ie, at lambda=10.2). From Agnieszka Jach's course "Mathematics for Economists".