Data Analysis with Python

• Programming skills and basic knowledge of probability calculus and linear algebra.
• The compulsory basic level courses in Bachelor's Programme in Science form a sufficient background.

After the course you:

• Can confidently write basic level Python programs without constantly consulting language/library documentation.
• Can apply efficient and elegant Pythonic idioms to solve problems
• Know the different phases of data analysis pipeline
• Know the fundamental data types array, Series and DataFrame
• Can clean data to form consistent Series and DataFrames without anomalies
• Can select subsets, transform, reshape and combine data
• Can extract summary statistics from data (min, max, mean, median, standard deviation)
• Know the main types of machine learning (supervised learning: regression and classification, unsupervised learning: clustering, dimensionality reduction, (density estimation))
• Know the estimator API of Scikit-Learn (choose model class, choose hyperparameters, form feature matrix and target vector, fit model, transform data or predict labels or responses)
• Can form feature matrix and target vector suitable for Scikit-Learn's model fitting algorithms
• Can visualize data as simple plots or histograms
• Can apply basic data analysis skills to a simple project on an application field

What kind of literature and other materials are read during the course (reading list)?

• Material is integrated to the MOOC instructions

Which works are set reading and which are recommended as supplementary reading?

• Jake VanderPlas, Python data science handbook, O'Reilly (2016)
  The book is freely available in electronic form from https://jakevdp.github.io/PythonDataScienceHandbook/(Avautuu uuteen välilehteen)

Course is offered as MOOC. After passing 80 % of the course material through online exercises, one can enter to the project work and the exam. The project work is peer-reviewed, and then evaluated. The exam consists of multiple choice questions.

The exact allowed completion methods will be announced when the course starts.

The final project, the peer-review work related to it and the exam are assessed.

The course is graded on a numerical scale from 0 to 5.

The MOOC learning environment includes automatic assessment of programming exercises.

The course is suitable to those who want an overview of the different phases of the data analysis pipeline using Python and its data analysis ecosystem. Prerequisites include programming skills and basic knowledge of probability calculus and linear algebra.

• Optional course in the new Bachelor's Programme in Science
• Optional course in other studies module of Master's Programme in Computer Science
• The course is available to students from other degree programmes.

• The last possibility to complete the course is to take the exam on 20.8.2025.

End of Bachelor's or start of Master's studies

Intermediate studies in Computer and Data Science

English