Enrolment options

30E03000 - Data Science for Business I D, Lecture, 11.1.2022-22.2.2022

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

After completing the course, students will understand the basic principles of predictive modeling and gain experience in using data analytic tools that are widely used in companies.

Credits: 6

Schedule: 11.01.2022 - 22.02.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Pekka Malo, Lina Siltala-Li, Antti Suominen

Contact information for the course (applies in this implementation):

Please check more in syllabus.

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    Fundamental concepts in predictive analytics, classification and association mining, model evaluation, use of programming (e.g., python or R), visiting lectures, project work.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Course project 40%, Assignments 30%, Exam 30%.

  • applies in this implementation

    Please check more in syllabus.

Workload

  • valid for whole curriculum period:

    Contact teaching 50 h, Independent work 107 h, Exam 3 h.

  • applies in this implementation

    Please check more in syllabus.

DETAILS

Study Material

  • valid for whole curriculum period:

    To be defined in the course syllabus.

  • applies in this implementation

    Please check more in syllabus.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Maximum 120 students will be admitted to the course. Priority is given 1. Aalto ISM MSc students 2. Aalto Analytics and Data Science minor students 3. Students in Master's Programme in ICT Innovation (EIT digital) 4. Bachelor’s students in ISM with 150 credits completed 5. Other Aalto students


    Teaching Period:

    2020-2021 Spring III

    2021-2022 Spring III


    Course Homepage: https://mycourses.aalto.fi/course/search.php?search=30E03000


    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

  • applies in this implementation

    Please check more in syllabus.

Details on the schedule

  • applies in this implementation

    Please check more in syllabus.

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

Guests cannot access this workspace. Please log in.