Study Guide

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.

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.