Study Guide

Learning outcomes

The student is able to
- utilise the fundamental concepts and principles of AI, machine learning and data science tools
- utilise technology to effectively visualise data, create interactive dashboards and communicate insights to stakeholders
- develop skills in programming for data analysis, manipulation, visualisation and implementation of machine learning models
- apply advanced AI and machine learning techniques and methodologies to analyse complex data sets and develop predictive models

Teaching methods

•Lecturing in TEAMS
•Group Discussion
•Individual Assignment
•Individual Project Task for data analysis using analytic tool or Python Programming Language
•A multiple choice question examination at the end of the study unit based on the concepts studied during the study unit.

Location and time

The location of lessons will be in Teams.
IF ANY CHANGES, STUDENTS WILL BE INFORMED IN ADVANCE
** You can see the schedule in Pakki and in Canvas **

Learning materials and recommended literature

All the materials for learning will be uploaded in the Canvas.
https://canvas.laurea.fi/courses/9837

Alternative completion methods of implementation

In this course, we’ll look at how data impacts business decisions. You’ll learn how data-analysis can be performed to help businesses grow, using tools like Power BI for analysis or Python Programming, while ensuring data privacy and following ethical guidelines.

You can complete the study unit virtually:

• Follow the instructions in CANVAS: The course learning environment is Canvas.
• Lectures will be recorded and uploaded in CANVAS for future reference.
• Submit the assignments on time.
• A mandatory multiple-choice examination.
• Attend the project task Evaluation.
• You can complete the study unit successfully.

Content and scheduling

Learning Outcomes:

Understand the fundamental concepts and principles of AI, machine learning, Power BI, and Python programming.
Utilize Power BI to effectively visualize data, create interactive dashboards and communicate insights to stakeholders.
Develop skills in Python programming for data analysis, manipulation, visualization and implementation of machine learning models.
Apply advanced AI and machine learning techniques and methodologies to analyse complex data sets and develop predictive models.

Further information for students

** According to the degree regulations (section 18) “students must be present in the first contact session or notify the teacher in charge if they cannot attend. If they fail to notify the teacher of their absence in the first contact session, their enrolment will be rejected. Another student in the queue may be enrolled in the study unit in the place of the absent student.”**

** Please note that the medium of instruction for this course will be English.**

Grading scale

H-5

Evaluation methods and criteria

The evaluation is based on the following criteria:
Assignment-30 ( 2 assignments 10 points each)
Project Evaluation (40)
Examination (30)
Total -100

Evaluation criteria, fail (0)

If the points are less than 40, the student will fail for the study unit

Evaluation criteria, satisfactory (1-2)

If the points between 40-60, the grade will be 1 and 2
ie; 40-49 will get 1
50-59 will get 2

Evaluation criteria, good (3-4)

If the points between 60-80, the grade will be 3 and 4
ie; 60-69 will get 3
70-79 will get 4

Evaluation criteria, excellent (5)

If the points between 80-100, the grade will be 4 and 5
ie; 80-89 will get 4
90-100 will get 5

Qualifications

To participate in the study unit, you must have completed the study unit Data-driven IT or have equivalent knowledge.

Further information

The learning outcomes of the study unit/project are evaluated based on the requirement level 2 of Laurea's common evaluation criteria.