Study Guide

Objective

The student knows the features of selected data analysis libraries and knows how to apply them in data preparation and statistical analysis. The student knows how to prepare data for machine learning algorithms.

Content

- Data preparation
- Data visualization
- Data analysis
- Data management
- Data analytics libraries and modules

Location and time

Lapland University of Applied Sciences, Rantavitikka Campus, 10.1.2022 - 30.4.2022.

Materials

Lecture materials and exercises are available on OneDrive/Git or other cloud service. Links to the materials can be found in the Moodle workspace.

Recommended reading:
Deitel P & Deitel H. 2019. Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud. 1st edition. Pearson Education
McKinney W. 2017. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd edition. O'Reilly
Nelli F. 2018. Python Data Analytics: With Pandas, NumPy, and Matplotlib. 2nd Edition. Apress

Teaching methods

Lectures, examples, exercises and self-supervised work.

Exam schedules

No preset dates for re-examinations. Re-examinations can be agreed on with the teacher case by case.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student knows how to prepare and modify example data for machine learning algorithms.

Assessment criteria, good (3)

The student can select pertinent data preparation methods for given data and modify the data so that it can be used by machine learning algorithms.

Assessment criteria, excellent (5)

The student can select the most pertinent data preparation methods for given data and modify the data so that it can be used by machine learning algorithms.

Assessment methods and criteria

The course will be graded on the scale from 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.