Data AnalyticsLaajuus (5 cr)
Code: R504TL128
Credits
5 op
Teaching language
- English
- Finnish
Objective
The student knows the main content of the selected data analytics libraries and is able to utilize them for data preparation and statistical processing for utilization in machine learning.
Content
- Data preparation: filtering, extraction, aggregation and classification
- Data visualization, research and analysis
- Use of suitable data analytics libraries
Assessment criteria, satisfactory (1)
The student is able to prepare and modify the data of a simple example case in a way that it can be utilized in machine learning algorithms or cloud services.
Assessment criteria, good (3)
The student is able to choose case-specific methods for data preparation and to modify the data in such a way that it can be utilized in machine learning and cloud services.
Assessment criteria, excellent (5)
The student is able to select the best case-specific methods for data preparation and to modify the data in a way that they can be utilized further in machine learning algorithms and cloud services.
Enrollment
01.10.2024 - 31.12.2024
Timing
27.01.2025 - 31.05.2025
Credits
5 op
Virtual proportion (cr)
5 op
Mode of delivery
Distance learning
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
- Finnish
Seats
0 - 60
Teachers
- Mikko Pajula
Responsible person
Mikko Pajula
Student groups
-
RA54T22SBachelor of Engineering, Information Technology (online studies), autumn 2022
Objective
The student knows the main content of the selected data analytics libraries and is able to utilize them for data preparation and statistical processing for utilization in machine learning.
Content
- Data preparation: filtering, extraction, aggregation and classification
- Data visualization, research and analysis
- Use of suitable data analytics libraries
Materials
All the necessary course materials will be compiled and made available via the Moodle workspace.
Self-study prerequisites, if not familiar: Basics of Python: Familiarity with the basic concepts and syntax of the Python programming language. Basics of Data Management: Understanding of fundamental aspects of data handling, including JSON and databases. Basics of Information Technology: Knowledge of key IT concepts, including understanding what a CPU and GPU are.
Teaching methods
Online material. Practical exercise support offered in workshops
Content scheduling
- Analytics tools and programs. Introduction to data analytics. Overview and implementation
- Data preparation. Filtering, retrieval, merging, and classification
- Use of appropriate data analytics libraries. Pandas, NumPy, scikit-learn, and others
- Data visualization and analysis. Visualization tools and principles. Exploring and analyzing data
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student is able to prepare and modify the data of a simple example case in a way that it can be utilized in machine learning algorithms or cloud services.
Assessment criteria, good (3)
The student is able to choose case-specific methods for data preparation and to modify the data in such a way that it can be utilized in machine learning and cloud services.
Assessment criteria, excellent (5)
The student is able to select the best case-specific methods for data preparation and to modify the data in a way that they can be utilized further in machine learning algorithms and cloud services.
Assessment methods and criteria
Grades are based on the quality, quantity, and comprehensiveness of the exercises.
Enrollment
18.03.2024 - 31.07.2024
Timing
01.08.2024 - 31.12.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- Finnish
Seats
0 - 50
Teachers
- Mikko Pajula
Responsible person
Mikko Pajula
Student groups
-
R54T21SBachelor of Engineering, Information Technology (full time day studies), autumn 2021
Objective
The student knows the main content of the selected data analytics libraries and is able to utilize them for data preparation and statistical processing for utilization in machine learning.
Content
- Data preparation: filtering, extraction, aggregation and classification
- Data visualization, research and analysis
- Use of suitable data analytics libraries
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student is able to prepare and modify the data of a simple example case in a way that it can be utilized in machine learning algorithms or cloud services.
Assessment criteria, good (3)
The student is able to choose case-specific methods for data preparation and to modify the data in such a way that it can be utilized in machine learning and cloud services.
Assessment criteria, excellent (5)
The student is able to select the best case-specific methods for data preparation and to modify the data in a way that they can be utilized further in machine learning algorithms and cloud services.
Enrollment
02.10.2023 - 09.01.2024
Timing
10.01.2024 - 31.05.2024
Credits
5 op
Virtual proportion (cr)
5 op
Mode of delivery
Distance learning
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
- Finnish
Seats
0 - 50
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
RA54T21SBachelor of Engineering, Information Technology (online studies), autumn 2021
Objective
The student knows the main content of the selected data analytics libraries and is able to utilize them for data preparation and statistical processing for utilization in machine learning.
Content
- Data preparation: filtering, extraction, aggregation and classification
- Data visualization, research and analysis
- Use of suitable data analytics libraries
Materials
Lecture materials and exercises will be placed in the Moodle workspace.
Teaching methods
Lectures, workshops, examples, exercises and self-supervised work.
Exam schedules
The course will be graded based on personal work and exercises.
Content scheduling
Topics include, but are not limited to:
- Quick Python recap
- NumPy
- pandas
- seaborn and matplotlib
- Data formats and management
- EDA - Exploratory Data Analysis
+ other relevant topics
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student is able to prepare and modify the data of a simple example case in a way that it can be utilized in machine learning algorithms or cloud services.
Assessment criteria, good (3)
The student is able to choose case-specific methods for data preparation and to modify the data in such a way that it can be utilized in machine learning and cloud services.
Assessment criteria, excellent (5)
The student is able to select the best case-specific methods for data preparation and to modify the data in a way that they can be utilized further in machine learning algorithms and cloud services.
Assessment methods and criteria
The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.