Data Analytics with Python - Study guide

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.

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.

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

No previous coding experience is required.

Study material is delivered through Moodle. There is also a lot of online mataerial availeble.

The course content takes into account and builds on the needs of today's working life.

Pasila Campus

Detailed examination arrangements are agreed together within the implementation participants. Participants may use online material and modern coding environment in the examination, due to which a classroom examination, during which a participant's identity is checked, is necessary. Particularly, no Exam examination is possible.

All the examinations of methodological studies will be held under supervision at the premises of the Haaga-Helia UAS and the identity of the participants will be checked during the examination.

English

Analytical skills are international skills. Data analytics skills are international skills. Mathematical and methodological skills are international skills

18.08.2025 - 12.12.2025

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

14.05.2025 - 22.05.2025

Detailed schedule will obey the duration and the contents of the course. It will be published in the beginning of implementation in Moodle if the programme director decides to start this implementation.

IBE5PASCM
IBE4PAACC
IBE5PAMAR
CONTACT
IBE4PAMAR
IBE5PACOM
IBE5PAACC
EXCH

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

Juha Nurmonen

15 - 45

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.

INTBBA International Business

0.00 cr

H-5