Data Analytics with Python (5 cr)
Code: ANA001AS2AE-3001
Basic information of implementation
- Enrollment
- 02.01.2025 - 10.01.2025
- Enrolment for the implementation has ended.
- Timing
- 13.01.2025 - 14.03.2025
- Implementation has ended.
- ECTS Credits
- 5 cr
- Campus
- Pasila Campus
- Teaching languages
- English
- Seats
- 15 - 45
- Degree programmes
- INTBBA International Business
- ITBBA Business Information Technology
- Teachers
- Toni Ernvall
- Groups
-
IBE4PAACCINTBBA, 4. semester, Pasila, Accounting and Finance
-
CONTACTContact implementation
-
ITE4PAICB1Business Information Technology, 4th semester, ICT and Business, Pasila, group 1
-
IBE4PASCMINTBBA, 4. semester, Pasila, Supply Chain Management
-
IBE4PAMARINTBBA, 4. semester, Pasila, Marketing and Sales
-
IBE4PACOMINTBBA, 4. semester, Pasila, International Business Communication
-
EXCHEXCH Exchange students
- Course
- ANA001AS2AE
Evaluation scale
H-5
Implementation methods, demonstration and Work&Study
Depending on the implementation, learning takes place in contact lessons, as independent studies, teamwork and online-studies. Implementations can include literature, assignments, R&D co-operation and company projects. The course includes the assessment of one’s own learning.
Recognition of prior learning (RPL)
If students have acquired the required competence in previous work tasks, recreational activities or on another course, they can show their competence via a demonstration. The demonstration must be agreed with the course teacher. More information and instructions for recognising and validating prior learning (RPL) are available at https://www.haaga-helia.fi/en/recognition-learning Look at "Instructions to students (master)"
Intro
In modern organizations, information is a crucial tool for management. Data analytics is a means of refining information for business needs. The objective of this course is to understand the process and methods of data analytics and to be able to apply them through practical examples. This course does not require prior programming skills.
Materials
Course material will be available in Moodle.
Teaching methods and instruction
Using Python in data analytics is learned under the guidance of a teacher.
Previous coding experience is not required.
Exam dates and re-exam possibilities
No exam.
Learning assignments
There are three exercises. Their topics cover descriptive analytics, diagnostic analytics, time series and machine learning.
Assessment methods
Final grade is the average of assignment grades rounded to integer. All assignments must be passed.