Data Analytics with Python - ANA001AS2AE - Opinto-opas

Data Analytics with Python, 5 cr - ANA001AS2AE

Upcoming implementations

Data Analytics with Python ANA001AS2AE-3001 13.01.2025-14.03.2025 5 op (IBE4PAACC, ...) +-
Teaching methods and instruction

Using Python in data analytics is learned under the guidance of a teacher.

Previous coding experience is not required.

Learning material and recommended literature

Course material will be available in Moodle.

Campus

Pasila Campus

Exam dates and re-exam possibilities

No exam.

Teaching language

English

Timing

13.01.2025 - 14.03.2025

Learning assignments

There are three exercises. Their topics cover descriptive analytics, diagnostic analytics, time series and machine learning.

Enrollment

02.01.2025 - 10.01.2025
Groups
- IBE4PAACC
- CONTACT
- ITE4PAICB1
- IBE4PASCM
- IBE4PAMAR
- IBE4PACOM
- EXCH
Teachers

Toni Ernvall

Seats

15 - 45

Further information

Final grade is the average of assignment grades rounded to integer. All assignments must be passed.

Degree Programme

ITBBA Business Information Technology, INTBBA International Business

R&D proportion

0.00 cr

Virtual proportion

0.00 cr

Evaluation scale

H-5

Current implementations

No ongoing implementations yet.

Past implementations

Data Analytics with Python ANA001AS2AE-3002 14.10.2024-13.12.2024 5 op (IBE5PASCM, ...) +-
Teaching methods and instruction

In the classroom or online classes, one will learn the basics of data analytics using Python coding under the guidance of a teacher. Students can also study independently with the help of ready-made examples.

No previous coding experience is required.

Learning material and recommended literature

https://1u.fi/hhjuhanurmonen (available via vdi)

Campus

Pasila Campus

Teaching language

English

Timing

14.10.2024 - 13.12.2024

Learning assignments

The implementation includes individual tasks where the skills learned are applied. The topics of the tasks include Descriptive analytics Diagnostic analytics Time series description and analysis Time series forecasting Predictive analytics and machine learning models

Enrollment

03.06.2024 - 11.10.2024

Content scheduling

Period 2, Autumn 2024
Groups
- IBE5PASCM
- IBE4PAACC
- IBE5PAMAR
- CONTACT
- DBE5PCDBI
- IBE4PASCM
- IBE4PAMAR
- BLENDED
- IBE5PACOM
- IBE5PAACC
Alternative learning methods

The implementation allows one to participate in common teaching according to your own skills. The course material supports indivudual learning.

Teachers

Juha Nurmonen, Veijo Vänttinen

Seats

15 - 45

Further information

Individual assignments are assessed and each contributes its own weight to the final grade. Each individual assignment must be passed according to the criteria given in the implementation during the same course implementation.

Degree Programme

INTBBA International Business

R&D proportion

0.00 cr

Virtual proportion

0.00 cr

Evaluation scale

H-5

Upon completion of the course, the student will:
• Identify data sources and assess their suitability for business needs.
• Understand the stages of data preparation, modeling, and forecasting.
• Understand the fundamental concepts of machine learning and artificial intelligence.
• Master methods of descriptive and explanatory analytics.
• Be able to utilize various visualization and reporting techniques.
• Understand the concept of information design.

Content
• Data analytics based on process thinking
• Methods for descriptive and explanatory analytics.
• Analysis and forecasting of time series data.
• Models for predictive analytics and machine learning.
• Applied examples using Python.
• Tools for visualization and reporting.

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)"

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.

When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

The student understands the data analytics process and can apply it, instructed by the teacher, to a business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions, guided by the teacher.

The student is able to derive and visualize dashboards, scorecards and publish those using related digital tools. The students is able to apply one or some forecasting algorithms to a business problem, instructed by the teacher. The students is able to assess the reliability and relevance of business reports.

The student understands the data analytics process and can apply it independently to a simple business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions independently.

The students is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently one or some forecasting algorithms to a business problem. The students is able to assess the reliability and relevance of business reports.

The student understands the data analytics process and can apply it to a slightly complicated business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve demanding business questions.

The student is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently several forecasting algorithms to different business problems. The student is able to assess the reliability and relevance of business reports.