Advanced Data Science (5 cr)

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

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 **

Materials

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

Teaching methods

* Blended Learning with Emphasis on Self-Direction and Practical Application

This study unit combines scheduled online lectures, interactive group discussions, individual assignments, and a project task that requires practical application of data analysis skills using Power BI or Python programming. The selection of application program (Python or PowerBI) in the study unit is based on the individual interest of student.

The structure supports independent learning, guided by a strict schedule and assignment deadlines. While self-management is essential, a limited number of lectures will be conducted via Microsoft Teams to provide instructional support and facilitate group discussions.

To successfully complete this study unit, students must fulfill the following requirements:
* Progress the modules in CANVAS as per schedule
* Study the content provided in the modules
* Study module material as instructed
* Pass assignments in every module
* Submit the online multiple choice examination on time.
* Submit the project task report on time.

Learning Environment and Tools
The primary platform for course content and assignment submissions is Canvas, supported by activities and communications through Microsoft Teams.

Guidance and Support
Instructional support is available through Canvas, where the instructor may be contacted via messages, email, or discussion boards. Students are expected to follow all announcements closely and thoroughly review all guidance provided within the Canvas platform. Live sessions held in Teams will also be used to address questions, provide clarification, and encourage peer discussion.

Feedback and Assessment
This course does not include personalized feedback for each student. Instead, evaluation is based on scores earned from assignments, project task, quizzes, and tests. The individual project task will be assessed according to a rubric-based Evaluation Criteria Chart, which outlines the levels of proficiency in understanding and implementation.

Important Note
The medium of implementation of the study unit is in English. It requires a strong capacity for self-direction, time management, and independent learning. If you prefer a more structured or instructor-led format, you are encouraged to explore alternative course delivery options available for this unit.

Completion alternatives

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 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.

Evaluation scale

H-5

Qualifications

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

Further information

** 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.**