Data Analysis (3 cr)

Materials

Morrison, S. J. (2009) Introduction to engineering statistics. Hoboken, NJ: Wiley
Hoerl, R & Snee, R. (2012) Statistical Thinking: Improving Business Performance. Hoboken, NJ: Wiley
Kelleher, Mac Namee & D'Arcy (2020) Fundamentals of Machine Learning for Predictive Data Analytics. Cambridge, MA: MIT Press
Other material accessible in Moodle

Evaluation scale

0-5

Completion alternatives

The admission procedures are described in the degree rule and the study guide.

Further information

The assessment is based on learning tasks and exams.

An equivalent course in Finnish TZLM7300-3011 Datan analysointi.

Open AMK: at most 5 students if there are seats in the classroom.

Student workload

Contact lessons about 20 hours
Independent study about 20 hours
Learning tasks about 20 hours

Assessment criteria, satisfactory (1)

Adequate 1
You have achieved the desired goals. You know a few of the concepts and methods and how to apply them in familiar situations but your reasoning is often deficient and you make mistakes in calculations.

Satisfactory 2
You have achieved the desired goals. You know many of the concepts and methods and how to apply them in familiar situations but your reasoning is sometimes deficient or you make mistakes in calculations.

Assessment criteria, good (3)

Good 3
You have achieved the desired goals. You know most of the concepts and methods and how to apply them in familiar situations showing often the ability to reason completely and calculate flawlessly

Very good 4
You have achieved the desired goals. You know most of the concepts and methods and how to apply them in new situations showing in most cases the ability to reason completely and calculate flawlessly.

Assessment criteria, excellent (5)

Excellent 5
You have achieved the desired goals. You know all the concepts and methods and how to apply them in new situations showing always the ability to combine things, reason completely and calculate flawlessly.

Exam schedules

The date and execution of the exam will be announced in the beginning of the course and in Moodle.

Teaching language

en

Teaching methods

The contact lessons are in a computer class and involve use of computers. The theory should be independently acquired before class exercises. The learning is accomplished by assignments where theory is put into practice.

Number of ECTS credits allocated

3

Qualifications

You master basic statistics and related Excel functions.

Content

Descriptive, exploratory, and prescriptive statistics
Confidence interval estimation and hypotheses testing
Multi-variable regression models
Time series analysis, smoothing and forecasting methods
Big data analysis using a computer
Use of Excel and some machine learning software

Objective

Purpose
After the course you will understand how analysis of statistical data can help an engineer to make better business decisions.

Learning outcomes
You can analyze, visualize and interpret small and big data to draw conclusions and make forecasts using statistical methods.