Study Guide

Objective

After completing the course the student has an understanding of the methods and Python packages for processing, visualising and analysing data for data science and machine learning applications. Student is able to use data coming from different sources in different formats, perform basic statistical analysis of data and visualise it. Basic tasks in creating models to make predictions based on data will also become familiar.

Content

• Python programming language and its use in processing data
• Tools for the analysis and visualisation of data, basic statistical methods
• Building models for making predictions

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

Student understands the methods and tools for data science and machine learning and is able to apply them in most typical settings.

Assessment criteria, good (3)

In addition to satisfactory criteria, student has demonstrated ability to solve some more demanding problems in the field. Student has fair understanding of the limitations of the methods and models.

Assessment criteria, excellent (5)

In addition to good criteria, student is able to apply new knowledge to data science and machine learning tasks. They understand the limitations of the methods and are able to critically assess the outcomes.

Prerequisites

No requirements