Introduction to Data Analytics (5 cr)
Code: R504D119-3001
General information
- Enrollment
- 03.10.2022 - 15.01.2023
- Registration for the implementation has ended.
- Timing
- 16.01.2023 - 24.05.2023
- Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Bachelor of Engineering, Information Technology
- Teaching languages
- English
- Seats
- 0 - 30
- Degree programmes
- Machine Learning and Data Engineering
Evaluation scale
H-5
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
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.
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.