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IoT Big Data and AnalyticsLaajuus (5 cr)

Code: 5000BL72

Credits

5 op

Objective

Student knows Basic tools for data analysis
Student can implement data analytics in edge computing
Student knows Basic solutions for big data analysis in cloud

Content

Data at rest and data in motion
Process for data analysis
Data preparation
Basics of descriptive statistics
Data visualization
Machine learning basics
Big data architectures

Qualifications

Basic skills in using both Windows and Linux systems
Basic networking skills (Cisco CCNA1 or similar)
Basic programming skills with some high level programming language (for example Python, Java, C# or similar)
Basic programming skills include (but are not limited to): output formatting, conditional execution, loops, functions/procedures, function parameters and return values, arrays, error handling, testing and good programming policies
Sufficient logical-mathematical thinking skills
Sufficient skills in English language (lectures and all materials are in English)

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Enrollment

01.06.2023 - 26.09.2023

Timing

25.09.2023 - 31.12.2023

Number of ECTS credits allocated

5 op

Mode of delivery

Contact teaching

Unit

Engineering and Business

Campus

Kupittaa Campus

Teaching languages
  • English
Seats

20 - 60

Degree programmes
  • Degree Programme in Information and Communication Technology
  • Degree Programme in Information and Communications Technology
Teachers
  • Juha Saarinen
Teacher in charge

Juha Saarinen

Scheduling groups
  • Laboratory Group 1 (Size: 30. Open UAS: 0.)
  • Laboratory Group 2 (Size: 30. Open UAS: 0.)
Groups
  • ICTMODembeddedSem
  • PTIVIS22S
    Embedded Software and IoT
Small groups
  • Laboratory Group 1
  • Laboratory Group 2

Objective

Student knows Basic tools for data analysis
Student can implement data analytics in edge computing
Student knows Basic solutions for big data analysis in cloud

Content

Data at rest and data in motion
Process for data analysis
Data preparation
Basics of descriptive statistics
Data visualization
Machine learning basics
Big data architectures

Materials

Cisco network academy material www.netacad.com

Teaching methods

Self-study network material
Lectures
7 laboratory sessions

International connections

Lab works
Lectures
Self study

Completion alternatives

-

Student workload

lab works 7x4h = 28h
lectures 6x1h = 6h
exam = 2h
self study = 74h
exam preparation 25h
TOTAL 135h

Content scheduling

Chapter 1 Data and the Internet of Things
Chapter 2 Fundamentals of Data Analysis
Chapter 3 Data Analysis
Chapter 4 Advanced Data Analytics and Machine Learning
Chapter 5 Storytelling with Data
Chapter 6 Architecture for Big Data and Data Engineering

Further information

-

Evaluation scale

H-5

Assessment methods and criteria

Must pass Final Exam:
60% -> 1
68% -> 2
76% -> 3
84% -> 4
92% -> 5
Mandatory lab works: +/- 2 grades from individual Lab performance
Mandatory lectures, must attend 70%

Assessment criteria, fail (0)

Failed Final Exam <60%
or
Weak Final exam < 76% + poor lab performance (missing labs, nonprofessional attitude or lack of active problem-solving, missed lectures)

Assessment criteria, excellent (5)

Excellent Final Exam >92%
and
expected lab performance (all labs done with average performance)
or
Good Final Exam >76%
and
superb lab performance (all labs done, actively learns new skills outside of the lab scope, is able to help fellow students, attended all lectures)

Qualifications

Basic skills in using both Windows and Linux systems
Basic networking skills (Cisco CCNA1 or similar)
Basic programming skills with some high level programming language (for example Python, Java, C# or similar)
Basic programming skills include (but are not limited to): output formatting, conditional execution, loops, functions/procedures, function parameters and return values, arrays, error handling, testing and good programming policies
Sufficient logical-mathematical thinking skills
Sufficient skills in English language (lectures and all materials are in English)

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Enrollment

01.06.2022 - 11.09.2022

Timing

19.09.2022 - 09.12.2022

Number of ECTS credits allocated

5 op

Mode of delivery

Contact teaching

Unit

Engineering and Business

Campus

Kupittaa Campus

Teaching languages
  • English
Degree programmes
  • Degree Programme in Information and Communications Technology
Teachers
  • Juha Saarinen
Teacher in charge

Juha Saarinen

Groups
  • ICTMODembeddedSem
  • PTIVIS21S
    Embedded Software and IoT

Objective

Student knows Basic tools for data analysis
Student can implement data analytics in edge computing
Student knows Basic solutions for big data analysis in cloud

Content

Data at rest and data in motion
Process for data analysis
Data preparation
Basics of descriptive statistics
Data visualization
Machine learning basics
Big data architectures

Materials

Cisco network academy material www.netacad.com

Teaching methods

Self-study network material
Lectures
7 laboratory sessions

International connections

Lab works
Lectures
Self study

Completion alternatives

-

Student workload

lab works 7x3h = 21h
lectures 6x2h = 12h
exam = 2h
self study = 74h
exam preparation 26h
TOTAL 135h

Content scheduling

Chapter 1 Data and the Internet of Things
Chapter 2 Fundamentals of Data Analysis
Chapter 3 Data Analysis
Chapter 4 Advanced Data Analytics and Machine Learning
Chapter 5 Storytelling with Data
Chapter 6 Architecture for Big Data and Data Engineering

Further information

-

Evaluation scale

H-5

Assessment methods and criteria

Must pass Final Exam:
60% -> 1
68% -> 2
76% -> 3
84% -> 4
92% -> 5
Mandatory lab works: +/- 2 grades from individual Lab performance
Mandatory lectures, must attend 70%

Assessment criteria, fail (0)

Failed Final Exam <60%
or
Weak Final exam < 76% + poor lab performance (missing labs, nonprofessional attitude or lack of active problem-solving, missed lectures)

Assessment criteria, excellent (5)

Excellent Final Exam >92%
and
expected lab performance (all labs done with average performance)
or
Good Final Exam >76%
and
superb lab performance (all labs done, actively learns new skills outside of the lab scope, is able to help fellow students, attended all lectures)

Qualifications

Basic skills in using both Windows and Linux systems
Basic networking skills (Cisco CCNA1 or similar)
Basic programming skills with some high level programming language (for example Python, Java, C# or similar)
Basic programming skills include (but are not limited to): output formatting, conditional execution, loops, functions/procedures, function parameters and return values, arrays, error handling, testing and good programming policies
Sufficient logical-mathematical thinking skills
Sufficient skills in English language (lectures and all materials are in English)

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Enrollment

01.12.2021 - 19.01.2022

Timing

10.01.2022 - 30.04.2022

Number of ECTS credits allocated

5 op

Mode of delivery

Contact teaching

Unit

Engineering and Business

Campus

Kupittaa Campus

Teaching languages
  • English
Degree programmes
  • Degree Programme in Information and Communications Technology
Teachers
  • Juha Saarinen
Groups
  • PIOTK21
    Degree Programme in Information Technology, Cyber Security and IoT

Objective

Student knows Basic tools for data analysis
Student can implement data analytics in edge computing
Student knows Basic solutions for big data analysis in cloud

Content

Data at rest and data in motion
Process for data analysis
Data preparation
Basics of descriptive statistics
Data visualization
Machine learning basics
Big data architectures

Materials

Cisco network academy material www.netacad.com

Teaching methods

Self-study network material
Lectures
7 laboratory sessions

International connections

Lab works
Lectures
Self study

Completion alternatives

-

Student workload

lab works 7x3h = 21h
lectures 6x2h = 12h
exam = 2h
self study = 74h
exam preparation 26h
TOTAL 135h

Content scheduling

Chapter 1 Data and the Internet of Things
Chapter 2 Fundamentals of Data Analysis
Chapter 3 Data Analysis
Chapter 4 Advanced Data Analytics and Machine Learning
Chapter 5 Storytelling with Data
Chapter 6 Architecture for Big Data and Data Engineering

Further information

-

Evaluation scale

H-5

Assessment methods and criteria

Must pass Final Exam:
60% -> 1
68% -> 2
76% -> 3
84% -> 4
92% -> 5
Mandatory lab works: +/- 2 grades from individual Lab performance
Mandatory lectures, must attend 70%

Assessment criteria, fail (0)

Failed Final Exam <60%
or
Weak Final exam < 76% + poor lab performance (missing labs, nonprofessional attitude or lack of active problem-solving, missed lectures)

Assessment criteria, excellent (5)

Excellent Final Exam >92%
and
expected lab performance (all labs done with average performance)
or
Good Final Exam >76%
and
superb lab performance (all labs done, actively learns new skills outside of the lab scope, is able to help fellow students, attended all lectures)

Qualifications

Basic skills in using both Windows and Linux systems
Basic networking skills (Cisco CCNA1 or similar)
Basic programming skills with some high level programming language (for example Python, Java, C# or similar)
Basic programming skills include (but are not limited to): output formatting, conditional execution, loops, functions/procedures, function parameters and return values, arrays, error handling, testing and good programming policies
Sufficient logical-mathematical thinking skills
Sufficient skills in English language (lectures and all materials are in English)