Introduction to Data AnalyticsLaajuus (5 cr)
Code: R504D119
Credits
5 op
Objective
The student understands basics of data analytics in data engineering and machine learning. The student acquires knowledge on, and experience with, selected environments and libraries for data analytics. The student is able to utilize them, in e.g., data preparation for machine learning algorithms.
Content
- Data preparation, pre-processing
- Data exploration, analysis; e.g., visual, numerical
- Use of data analytics environments and libraries
Assessment criteria, satisfactory (1)
Grade 1: The student knows basics of data analytics in data engineering and machine learning. The student is able apply basic data analytics techniques in data engineering and machine learning.
Assessment criteria, good (3)
Grade 3: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, suitably.
Assessment criteria, excellent (5)
Grade 5: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, most suitably.
Enrollment
02.10.2023 - 14.01.2024
Timing
15.01.2024 - 24.05.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
R54D23SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023
Objective
The student understands basics of data analytics in data engineering and machine learning. The student acquires knowledge on, and experience with, selected environments and libraries for data analytics. The student is able to utilize them, in e.g., data preparation for machine learning algorithms.
Content
- Data preparation, pre-processing
- Data exploration, analysis; e.g., visual, numerical
- Use of data analytics environments and libraries
Location and time
Lapland University of Applied Sciences, Rantavitikka Campus, 8.1.2024 - 15.5.2024.
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)
Grade 1: The student knows basics of data analytics in data engineering and machine learning. The student is able apply basic data analytics techniques in data engineering and machine learning.
Assessment criteria, good (3)
Grade 3: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, suitably.
Assessment criteria, excellent (5)
Grade 5: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, most suitably.
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.
Enrollment
03.10.2022 - 15.01.2023
Timing
16.01.2023 - 24.05.2023
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Degree programmes
- Machine Learning and Data Engineering
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
R54D22S
Objective
The student understands basics of data analytics in data engineering and machine learning. The student acquires knowledge on, and experience with, selected environments and libraries for data analytics. The student is able to utilize them, in e.g., data preparation for machine learning algorithms.
Content
- Data preparation, pre-processing
- Data exploration, analysis; e.g., visual, numerical
- Use of data analytics environments and libraries
Location and time
Lapland University of Applied Sciences, Rantavitikka Campus, 10.1.2023 - 15.5.2023.
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.
Completion alternatives
For other alternatives on how to pass the course, consult your instructor.
Content scheduling
Topics include, but are not limited to:
- Quick Python recap
- NumPy
- pandas
- seaborn and matplotlib
- Data formats and management
- EDA - Explortaive Data Analysis
+ other relevant topics
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: The student knows basics of data analytics in data engineering and machine learning. The student is able apply basic data analytics techniques in data engineering and machine learning.
Assessment criteria, good (3)
Grade 3: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, suitably.
Assessment criteria, excellent (5)
Grade 5: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, most suitably.
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.