Data analytics 1 (5cr)
Code: C-02536-YD00AV52-3001
General information
- Enrollment
- 15.11.2025 - 15.01.2026
- Registration for introductions has not started yet.
- Timing
- 01.01.2026 - 31.05.2026
- The implementation has not yet started.
- Number of ECTS credits allocated
- 5 cr
- Institution
- Centria University of Applied Sciences, Microsoft Teams
- Teaching languages
- Finnish
- Seats
- 0 - 30
- Course
- C-02536-YD00AV52
Unfortunately, no reservations were found for the realization Data analytics 1 C-02536-YD00AV52-3001. It's possible that the reservations have not yet been published or that the realization is intended to be completed independently.
Evaluation scale
0-5
Objective
- The student understands the principles of data collection and preprocessing and can explain their significance in the analytics process. - The student identifies various data storage solutions and can evaluate their suitability for different needs. - The student can apply basic statistical analysis methods and can use analytics tools to perform foundational analyses. - The student is able to analyze the results of statistical analysis and can assess their impact on decision-making. The student can apply learned skills in practical exercises and can utilize analytics tools to support decision-making.
Content
The aim of the course is to provide students with basic knowledge of data collection, preprocessing, the use of various data storage solutions, as well as fundamental statistical analysis and its importance in decision-making. The course also includes practical exercises with common analytics tools, allowing students to apply their learning in practice.
Location and time
Microsoft Teams
Teaching methods
The goal of this course is to introduce students to the fundamental concepts, tools, and methods of data analytics. During the course, students will learn how to process, analyze, and visualize data using Excel and RStudio. Content: Lecture 1: Introduction to the course, requirements, and an overview of data analytics. Basic Excel analysis: mean, standard deviation, expected value, correlation. Lecture 2: Advanced Excel analytics: regression, forecasting, optimization through iteration, table filtering, pivot tables, importing and processing CSV data. Lecture 3: Installation and setup of the RStudio environment. Data import and basic commands for performing analyses. Lecture 4: Statistical analysis in RStudio: normal distribution, confidence interval, degrees of freedom, statistical testing, and Student's t-test. Lecture 5: Introduction to final project. Lectures will be recorded, and the recordings will be available on Itslearning after the lecture. Attendance at lectures is not mandatory!
Qualifications
Students taking this course should have basic skills in engineering mathematics. Programming skills are also beneficial, as the course involves applying analytics tools.