Project Work: Data Analytics (4 cr)

Code: TTOW1600-3001

General information

Enrollment: 02.11.2020 - 30.11.2020
Registration for the implementation has ended.

Timing: 08.03.2021 - 30.04.2021
Implementation has ended.

Number of ECTS credits allocated: 4 cr

Local portion: 0 cr

Virtual portion: 4 cr

Unit: School of Technology

Teaching languages: Finnish

Seats: 0 - 60

Degree programmes: Bachelor's Degree Programme in Information and Communications Technology

Teachers: Jesse Raitapuro; Lassi Partamies

Teacher in charge: Mika Rantonen

Groups: TTV18S1
Tieto- ja viestintätekniikka; TTV19SM
Tieto- ja viestintätekniikka; TTV18SM
Tieto- ja viestintätekniikka; TTV18S5
Tieto- ja viestintätekniikka; TTV18S2
Tieto- ja viestintätekniikka; ZJA20STIDA
Avoin amk, tekniikka, ICT, Data-analytiikka ja tekoäly; TTV18S3
Tieto- ja viestintätekniikka

Course: TTOW1600

No reservations found for realization TTOW1600-3001!

Evaluation scale

0-5

Objective

The student understands and masters the various phases of Data Analytics project. The student is able to select the applicable methods for the problem to be solved and apply them to the problem to be solved. The student is able to interpret the obtained results and draw conclusions based on them.

Content

• Analysis of elective data in Python programming environment
• Includes all stages of data analysis:
• Data preprocessing
• Data description and descriptors
• Selection of a suitable predictive model and its implementation (at least two alternative models)
• Assessment of the accuracy of the predictive models
• Detailed reporting (GitLab)

Assessment criteria, satisfactory (1)

Excellent 5: The student knows the various phases of a data analytics project and is able to proceed systematically step by step. The student is able to select the correct techniques regardless of the problem to be solved and is able to apply his/her technical know-how to practice. Additionally, the student is able to critically assess his/her implementation and validate the conclusions.

Very good 4: The student knows the various phases of a data analytics project and is able to proceed systematically step by step. The student is able to select the correct techniques regardless of the problem to be solved and is able to apply his/her technical know-how to practice. In addition, the student is able to assess his/her implementation and validate the conclusions.

Good 3: The student knows the various phases of a data analytics project and is able to proceed step by step. The student is able to select the most common techniques to the problem to be solved and is able to apply his/her technical know-how to practive. Additionally, the student is able to assess his/her implementation and validate the conclusions.

Satisfactory 2: The student knows the various phases of a data analytics project. The student is able to select the most common techniques to the problem to be solved and is able to apply his/her technical know-how to practice. In addition, the student is able to assess his implementation and validate the conclusions.

Sufficient 1: The student knows the various phases of a data analytics project. The student knows the most common techniques and is able to apply them to practice. Additionally, the student is able to assess briefly his/her implementation and validate the conclusions.

Fail 0: The student does not meet the minimum criteria set for the course.

Qualifications

Basics in computing, programming, knowledge and know-how of Python programming language.

Additionally, courses in Data preprocessing and Data Analysis.