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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
  • RA54T22S
    Bachelor 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
  • R54T21S
    Bachelor 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
  • RA54T21S
    Bachelor 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.