Syllabus

LEARNING OUTCOMES

After the course, you can work as a data scientist in a team. You understand the structure and technical and non-technical challenges of data science projects. Furthermore, you learn to apply data analysis tools in a real-world data analysis project. Finally, you learn to document your project work and its outcomes, and to present its results and conclusions thereof both in writing and verbally.

Credits: 5

Schedule: 04.09.2023 - 30.11.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jorma Laaksonen

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

Jorma Laaksonen <jorma.laaksonen@aalto.fi> +358 50 3058719 TG:jormalaaksonen

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:

  The course consists of a data science project which is done in a small group under supervision of a data science expert from industry or academia. The activities include project management, requirement specification, design, coding, data collection and curating, experimenting, documenting, and presenting.

• applies in this implementation

  
  

  
  

Assessment Methods and Criteria

• valid for whole curriculum period:

  Project work, its documentation and presentation, peer opinions.

Workload

• valid for whole curriculum period:

  lectures, supervised group meetings, group and individual work, excursions to companies, final presentations in companies

• applies in this implementation

  •   6 x 2-hour lectures (1 by Jorma Laaksonen, 4 by visitors, 1 by groups)

  • 10 x 2-hour supervised group meetings

  •   3 x 2-hour final presentations for other groups at the companies

  • participation in all above is mandatory, but few absences allowed

  • rest: group and individual work, to be reported in MyCourses

  • 135 hours total

  
  

DETAILS

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

SDG: Sustainable Development Goals

1 No Poverty

2 Zero Hunger

3 Good Health and Well-being

4 Quality Education

5 Gender Equality

6 Clean Water and Sanitation

7 Affordable and Clean Energy

8 Decent Work and Economic Growth

9 Industry, Innovation and Infrastructure

10 Reduced Inequality

11 Sustainable Cities and Communities

12 Responsible Production and Consumption

13 Climate Action

14 Life Below Water

15 Life on Land

16 Peace and Justice Strong Institutions

17 Partnerships for the Goals

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Language : English

  Teaching Period : 2022-2023 Autumn I - II
  2023-2024 Autumn I - II

  Enrollment :

  The course is primarily for the students of the Data Science major in Aalto Bachelor's Programme in Science and Technology. Other B.Sc. students can be accepted depending on the circumstances.

Details on the schedule

• applies in this implementation

  Lectures on Mondays at 12:15–14:00 in T5:

  • 4.9. Jorma Laaksonen: Introduction
  • 11.9. N.N.: Python tools and libraries
  • 18.9. Saku Suuriniemi: data Sceince cases & observations
  • 25.9. Seth Peters: Real-life data science
  • 2.10. Andreas helenius: Privace & ethics
  • 9.10. Groups' status presentations

  Group meetings on Thursdays at 12:15–14:00 in T4 / U409 / U360b

  • 7.9. Kick-off with company representatives
  • 14.9. Progress monitoring with TAs
  • 21.9. Progress monitoring with TAs
  • 28.9. 1st check-up with company representatives
  • 5. 10. Progress monitoring with TAs
  • 12.10. Progress monitoring with TAs
  • 26.10. 2nd check-up with company representatives
  • 2.11. Progress monitoring with TAs
  • 9.11. Progress monitoring with TAs
  • 16.11. Progress monitoring with TAs

  Site visits to companies and group presentations:

  • Mon 20.11. Futurice & Group 1
  • Thu 23.11. Reaktor & Group 2
  • Mon 27.11. OP & Group 3
  • ??? ??.??. Droppe & Group 4

  Submission deadlines:

  • Reading task #1 xx.xx.
  • Reading task #2 xx.xx.
  • Final report xx.xx. (draft version)
  • Final report xx.xx. (final version)

  
  

  
  

Description