Data Analytics (5cr)

Evaluation scale

H-5

Content scheduling

The course has weekly lessons according to the timetable. A detailed weekly timetable will be made available on the Moodle learning environment if the Director of Studies decides to start this implementation. The timetable for the returnable learning assignments will follow the lecture timetable.

Objective

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.

Methods of completion

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)"

Methods of completion

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.

Content

Content • Data analytics based on process thinking (CRISP-DM). • 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.

Materials

The learning material is mainly distributed through the Moodle learning environment. The course brings together the main techniques needed for the fundamentals of data analytics. It also provides tools for applied analytics research or thesis work. Such issues are constantly changing and the course will mainly make use of teachers’ and otherwise up-to-date material.

Teaching methods

The course has lessons every week. The lessons cover the basics of data analytics applications using Python coding. The course implementation welcomes independent study of topics with the help of ready-made examples and videos, within the guidelines of the Haaga-Helia University of Applied Sciences. No previous experience in coding is required.

Employer connections

The course implementations are designed for students who are about to enter work life or are already working there. The course content takes into account the content used in the field.

Exam schedules

The organisations of exam and retake will be agreed together with the course participants. The exam will be held at the end of the course on a date to be agreed with the participants in the implementation. The date of the re-examination will also be mutually agreed with the participants of the implementation. The exam and retakes are organised in Pasila and participants are expected to be present; particularly the identity of the examiner will be verified. In the tests, the author has extensive access to materials and to modern programming environment. It is therefore not possible to conduct the test as an Exam examination

International connections

Data analytics skills are international skills. Methodological expertise is international.

Completion alternatives

There are no shortcuts to learning, and doing the work is essential. Other ways of obtaining a grade in the Haaga-Helia University of Applied Sciences credit register should be requested from the teacher responsible for the course.

Student workload

The topics presented in the lectures are learned by doing related exercises. Assignments will be returned regularly during the course, every one or two weeks. Assignments will be given and returned via Moodle. Assignments will cover the following topics: descriptive analytics, explanatory analytics, time series and time series forecasting, predictive analytics and basics of machine learning models.

Qualifications

No prerequisites. This course unit is part of the master's degree's curriculum. Completion of the course requires master's study entitlement.