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
-
UTIVERKKOInstitute 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
-
TLS22S1Logistiikka - 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
-
TLP22S1Bachelor's Degree Programme in Purchasing and Logistics Engineering
-
TLP24VSBachelor'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
-
UTIVERKKOInstitute 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
-
UTIVERKKOInstitute 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
-
TLS21S1Logistiikan 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
-
TLE23SHYITBachelor's Degree Programme in International Logistics, HYIT
-
TLP21S1Bachelor's Degree Programme in Purchasing and Logistics Engineering
-
TLP23VSBachelor'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
-
UTIVERKKOInstitute 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
-
LOGRAKVERKKOLogistiikan 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
-
LOGRAKVERKKOLogistiikan 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
-
LOGRAKVERKKOLogistiikan 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
-
TLS20S1Bachelor'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
-
TLP20S1Bachelor's Degree Programme in Purchasing and Logistics Engineering
-
TLP22VSBachelor'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.