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.

Note: by the request of the training subscriber (degree program), starting in 2024, "Python for everyone" has been taken as the starting point instead of the previous "data analytics for everyone".
The course introduces basic features to analyze structured data and is supported by related material and assignments. Topics covered include:
- descriptive analytics
- diagnostic analytics
- time series analysis and forecasting
- introduction to machine learning.
All the necessary material will be found on Moodle including Q@A section whereby students are able to discuss matters concerning the course including assignments.

Heidi Rajamäki-Partanen

Material given in Moodle

Pasila Campus

Four graded assignments issued throughout the course

English

15.01.2024 - 15.03.2024

Four assignments: - descriptive analytics - diagnostic analytics - time series analysis and forecasting - machine learning.

02.01.2024 - 12.01.2024

15.1 - 15.3.2024

MALEAE
MAENTE
MAEXPE
VIRTUAL
INSTRUCTED

Veijo Vänttinen

31 - 100

Four assignments:
- descriptive analytics, (0 - 5 points)
- diagnostic analytics, (0 - 5 points)
- time series analysis and forecasting, (0 - 5 points)
- machine learning, (0 - 5 points)

BUTEM Degree Programme in Business Technologies, ATBUM Degree Programme in Aviation and Tourism Business, LEBUM Degree Programme in Leading Business Transformation, HOSBUM Degree Programme in Tourism and Hospitality Business

0.00 cr

5.00 cr

H-5