Data Analysis (TZLM7300)

Data Analysis, 3 cr - TZLM7300

Data Analysis (TZLM7300-3004), 09.01.2023 – 19.05.2023,
3 cr (LOGRAKVERKKO) — Online learning + -
Language of instruction

Finnish

Timing

09.01.2023 - 19.05.2023

Lecturer(s)

Ida Arhosalo

Planned learning activities, teaching methods and guidance

In online learning the student works independently by familiarizing with the theory and putting the theory into practice by solving assignments using a computer.

Learning materials and recommended literature

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

Campus

Main Campus

Exam dates and re-exam possibilities

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

Learning assignments and student workload

Independent study about 20 hours
Learning tasks about 40 hours

Enrollment

01.11.2022 - 05.01.2023
Groups
- LOGRAKVERKKO
Alternative learning methods

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

Seats

0 - 20

Assessment methods

The assessment is based on learning tasks and exams.

An equivalent course with contact lessons in English TZLM7300-3002 Data Analysis.

Degree Programme

Bachelor's Degree Programme in Logistics

Mode of delivery

Online learning

Share of virtual studies

3 cr

Unit

School of Technology
Data Analysis (TZLM7300-3005), 01.05.2023 – 31.08.2023,
3 cr (LOGRAKVERKKO) — Online learning + -
Language of instruction

Finnish

Timing

01.05.2023 - 31.08.2023

Lecturer(s)

Ida Arhosalo

Planned learning activities, teaching methods and guidance

In online learning the student works independently by familiarizing with the theory and putting the theory into practice by solving assignments using a computer.

Learning materials and recommended literature

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

Campus

Main Campus

Exam dates and re-exam possibilities

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

Learning assignments and student workload

Independent study about 20 hours
Learning tasks about 40 hours

Enrollment

01.04.2023 - 30.04.2023
Groups
- LOGRAKVERKKO
Alternative learning methods

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

Seats

0 - 5

Assessment methods

The assessment is based on learning tasks and exams.

An equivalent course with contact lessons in English TZLM7300-3002 Data Analysis.

Degree Programme

Bachelor's Degree Programme in Logistics

Mode of delivery

Online learning

Share of virtual studies

3 cr

Unit

School of Technology

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.

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

You master basic statistics and related Excel functions.

0-5

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.

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.

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.

Data Analysis, 3 cr - TZLM7300

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