Data Analytics - 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.

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

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.

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.

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.

Juha Nurmonen

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.

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.

Pasila Campus

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 the Internet. It is therefore not possible to conduct the test as an Exam.

English

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

20.01.2025 - 16.05.2025

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.

07.01.2025 - 17.01.2025

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.

MAAIBUME
CONTACT

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.

Juha Nurmonen

31 - 50

The determination of the implementation grade will be announced at the beginning of the implementation.

AIBUM Degree Programme in AI for Business Transformation

0.00 cr

H-5