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Data AnalysisLaajuus (3 cr)

Code: TZLM7300

Credits

3 op

Teaching language

  • Finnish
  • English

Responsible person

  • Ida Arhosalo
  • Kalle Niemi

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.

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

Qualifications

You master basic statistics and related Excel functions.

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.

Enrollment

18.11.2024 - 09.01.2025

Timing

13.01.2025 - 19.05.2025

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

20 - 45

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Kalle Niemi
Groups
  • UTIVERKKO
    Institute of New Industry, online learning (mechanical, logistics and civil engineering)

Objectives

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.

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

Time and location

Autumn semester 2024

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
Shmueli, G., Bruce, P., Deokar, A. & Patel, N. 2023. Machine learning for business analytics. Hoboken, NJ: Wiley

Other material accessible in Moodle

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

If a student enrolled in the course does not show activity within three weeks of the start of the course, the enrollment will be rejected.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2024 - 22.08.2024

Timing

21.10.2024 - 15.12.2024

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

20 - 57

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Kalle Niemi
Scheduling groups
  • TLS22SA (Capacity: 35. Open UAS: 0.)
  • TLS22SB (Capacity: 35. Open UAS: 0.)
Groups
  • TLS22S1
    Logistiikka - tutkinto-ohjelma (AMK)
Small groups
  • TLS22SA
  • TLS22SB

Objectives

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.

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

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

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.

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

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

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course in English TZLM7300-3012 Data Analysis.

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

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2024 - 22.08.2024

Timing

21.10.2024 - 15.12.2024

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • English
Seats

20 - 36

Degree programmes
  • Bachelor's Degree Programme in Purchasing and Logistics Engineering
Teachers
  • Kalle Niemi
Groups
  • TLP22S1
    Bachelor's Degree Programme in Purchasing and Logistics Engineering
  • TLP24VS
    Bachelor's Degree Programme in Purchasing and Logistics Engineering (AMK) vaihto-opiskelu/Exchange studies

Objectives

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.

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

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

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.

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

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

Further information for students

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.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2024 - 22.08.2024

Timing

26.08.2024 - 18.12.2024

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

20 - 45

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Kalle Niemi
Groups
  • UTIVERKKO
    Institute of New Industry, online learning (mechanical, logistics and civil engineering)

Objectives

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.

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

Time and location

Autumn semester 2024

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
Shmueli, G., Bruce, P., Deokar, A. & Patel, N. 2023. Machine learning for business analytics. Hoboken, NJ: Wiley

Other material accessible in Moodle

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course with contact lessons in English TZLM7300-3007 Data Analysis.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

20.11.2023 - 04.01.2024

Timing

08.01.2024 - 19.05.2024

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 30

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Kalle Niemi
Groups
  • UTIVERKKO
    Institute of New Industry, online learning (mechanical, logistics and civil engineering)

Objectives

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.

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

Time and location

Spring semester 2024

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
Shmueli, G., Bruce, P., Deokar, A. & Patel, N. 2023. Machine learning for business analytics. Hoboken, NJ: Wiley

Other material accessible in Moodle

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2023 - 24.08.2023

Timing

23.10.2023 - 08.12.2023

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 50

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Pasi Lehtola
Scheduling groups
  • TLS21SA (Capacity: 30. Open UAS: 0.)
  • TLS21SB (Capacity: 30. Open UAS: 0.)
Groups
  • TLS21S1
    Logistiikan tutkinto-ohjelma (AMK)
Small groups
  • TLS21SA
  • TLS21SB

Objectives

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.

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

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

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.

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

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

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course in English TZLM7300-3007 Data Analysis.

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

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2023 - 24.08.2023

Timing

23.10.2023 - 08.12.2023

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • English
Seats

0 - 20

Degree programmes
  • Bachelor's Degree Programme in Purchasing and Logistics Engineering
Teachers
  • Pasi Lehtola
Groups
  • TLE23SHYIT
    Bachelor's Degree Programme in International Logistics, HYIT
  • TLP21S1
    Bachelor's Degree Programme in Purchasing and Logistics Engineering
  • TLP23VS
    Bachelor's Degree Programme in Purchasing and Logistics Engineering (AMK) vaihto-opiskelu/Exchange studies

Objectives

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.

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

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

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.

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

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

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course in Finnish TZLM7300-3006 Datan analysointi.

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

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2023 - 24.08.2023

Timing

28.08.2023 - 19.12.2023

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 15

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Pasi Lehtola
Groups
  • UTIVERKKO
    Institute of New Industry, online learning (mechanical, logistics and civil engineering)

Objectives

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.

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

Time and location

Autumn semester 2023

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
Shmueli, G., Bruce, P., Deokar, A. & Patel, N. 2023. Machine learning for business analytics. Hoboken, NJ: Wiley

Other material accessible in Moodle

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course with contact lessons in English TZLM7300-3007 Data Analysis.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.04.2023 - 30.04.2023

Timing

01.05.2023 - 31.08.2023

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 20

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Ida Arhosalo
Teacher in charge

Ida Arhosalo

Groups
  • LOGRAKVERKKO
    Logistiikan ja rakentamisen verkko-opetus

Objectives

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.

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

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

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course with contact lessons in English TZLM7300-3007 Data Analysis.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.11.2022 - 05.01.2023

Timing

09.01.2023 - 19.05.2023

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 20

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Ida Arhosalo
Teacher in charge

Ida Arhosalo

Groups
  • LOGRAKVERKKO
    Logistiikan ja rakentamisen verkko-opetus

Objectives

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.

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

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

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

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

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2022 - 25.08.2022

Timing

30.08.2022 - 21.12.2022

Number of ECTS credits allocated

3 op

Virtual portion

3 op

Mode of delivery

Online learning

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 20

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Ida Arhosalo
Teacher in charge

Ida Arhosalo

Groups
  • LOGRAKVERKKO
    Logistiikan ja rakentamisen verkko-opetus

Objectives

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.

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

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

Teaching methods

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

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

Independent study about 20 hours
Learning tasks about 40 hours

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course with contact lessons in English TZLM7300-3007 Data Analysis.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2022 - 25.08.2022

Timing

29.08.2022 - 14.10.2022

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • Finnish
Seats

0 - 50

Degree programmes
  • Bachelor's Degree Programme in Logistics
Teachers
  • Ida Arhosalo
Teacher in charge

Ida Arhosalo

Scheduling groups
  • TLS20SA (Capacity: 30. Open UAS: 0.)
  • TLS20SB (Capacity: 30. Open UAS: 0.)
Groups
  • TLS20S1
    Bachelor's Degree Programme in Logistics
Small groups
  • TLS20SA
  • TLS20SB

Objectives

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.

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

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

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.

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

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

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course in English TZLM7300-3002 Data Analysis.

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

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.

Enrollment

01.08.2022 - 25.08.2022

Timing

29.08.2022 - 14.10.2022

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages
  • English
Seats

0 - 25

Degree programmes
  • Bachelor's Degree Programme in Purchasing and Logistics Engineering
Teachers
  • Pasi Lehtola
Teacher in charge

Pasi Lehtola

Groups
  • TLP20S1
    Bachelor's Degree Programme in Purchasing and Logistics Engineering
  • TLP22VS
    Bachelor's Degree Programme in Purchasing and Logistics Engineering (AMK) vaihto-opiskelu/Exchange studies

Objectives

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.

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

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

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.

Exam dates and retake possibilities

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

Alternative completion methods

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

Student workload

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

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course in English TZLM7300-3002 Data Analysis.

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

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

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.

Evaluation criteria, good (3-4)

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.

Evaluation 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.

Prerequisites

You master basic statistics and related Excel functions.