Data Analytics with PythonLaajuus (5 cr)
Code: DT10074
Credits
5 op
Teaching language
- English
Responsible person
- Radu Mariescu-Istodor
- Jyri Kemppainen
Objective
You learn how to effectively analyse and visualize data with Python.
Content
- Python libraries for data science
- Necessary probability and statistics
Qualifications
- You master Python programming language, or
- You have completed Programming Essentials in Python
- You have a basic understanding of mathematics, statistics, and data science
Enrollment
01.08.2023 - 01.10.2023
Timing
05.10.2023 - 01.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Campus
Wärtsilä Campus Karjalankatu 3
Teaching languages
- English
Seats
5 - 30
Degree programmes
- Open University of Applied Sciences Studies
- Degree Programme in Business Information Technology
Teachers
- Radu Mariescu-Istodor
Teacher in charge
Radu Mariescu-Istodor
Scheduling groups
- Avoimen opiskelijat (Size: 10. Open UAS: 10.)
- Tutkinto-opiskelijat (Size: 0. Open UAS: 0.)
Groups
-
TOP23_24Other Complimentary Studies Group Semester 2023-2024
-
KAKS23Karelia, Open UAS, All, Fall, 2023
Small groups
- Open UAS students
- Degree students
Objective
You learn how to effectively analyse and visualize data with Python.
Content
- Python libraries for data science
- Necessary probability and statistics
Location and time
Online lectures and exercises:
05.10.2023 08.15 - 09.45
09.10.2023 10.45 - 12.15
11.10.2023 10.45 - 12.15
23.10.2023 13.15 - 14.45
25.10.2023 09.00 - 10.30
30.10.2023 14.15 - 15.45
01.11.2023 14.00 - 15.30
07.11.2023 10.15 - 11.45
09.11.2023 11.30 - 13.00
13.11.2023 10.15 - 11.45
Teaching methods
Course grading is based on continuous evaluation through weekly assignments
- Assignments are graded from 0 to 5, and the course grade is the average grade from these assignments.
The minimum grade for passing the course is 1
Student workload
- Lectures and exercises 20h
- Self-study 100h
Further information
- You master Python programming language, or
- You have completed Programming Essentials in Python
- You have a basic understanding of mathematics, statistics, and data science
Evaluation scale
H-5
Qualifications
- You master Python programming language, or
- You have completed Programming Essentials in Python
- You have a basic understanding of mathematics, statistics, and data science
Enrollment
01.10.2022 - 18.12.2022
Timing
04.01.2023 - 13.03.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
Information and Communication Technologies (DATA)
Campus
Wärtsilä Campus Karjalankatu 3
Teaching languages
- English
Seats
20 - 40
Degree programmes
- Degree Programme in Business Information Technology
Teachers
- Radu Mariescu-Istodor
- Jyri Kemppainen
Teacher in charge
Jyri Kemppainen
Scheduling groups
- Pienryhmä (schedulingGroup) 1 (Size: 20. Open UAS: 20.)
- Pienryhmä (schedulingGroup) 2 (Size: 10. Open UAS: 0.)
Groups
-
KAKK23Karelia, Open UAS, All, Spring, 2023
-
KAKS22Karelia, Open UAS, All, Fall, 2022
-
TOP22_23Other Complimentary Studies Group Semester 2022-2023
-
KADS22SO
Small groups
- Pienryhmä (schedulingGroup) 1
- Pienryhmä (schedulingGroup) 2
Objective
You learn how to effectively analyse and visualize data with Python.
Content
- Python libraries for data science
- Necessary probability and statistics
Further information
- Lectures and exercises 35h
- Self-study 100h
Starting level:
- You master Python programming language, or
- You have completed Programming Essentials in Python
- You have a basic understanding of mathematics, statistics, and data science
Evaluation scale
H-5
Assessment methods and criteria
Course grading is based on continuous evaluation through weekly assignments
- Assignments are graded from 0 to 5, and the course grade is the average grade from these assignments.
The minimum grade for passing the course is 1
Qualifications
- You master Python programming language, or
- You have completed Programming Essentials in Python
- You have a basic understanding of mathematics, statistics, and data science