•   IoT Big Data and Analytics 5000BL72-3005 19.09.2022-09.12.2022  5 credits  (ICTMODembeddedSem, ...) +-
    Competence objectives of study unit
    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
    Prerequisites
    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)
    Content of study unit
    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
    Teacher(s) in charge

    Juha Saarinen

    Learning material

    Cisco network academy material www.netacad.com

    Learning methods

    Self-study network material
    Lectures
    7 laboratory sessions

    Objects, timing and methods of assessment

    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%

    Teaching language

    English

    Timing

    19.09.2022 - 09.12.2022

    Enrollment date range

    01.06.2022 - 11.09.2022

    Group(s)
    • ICTMODembeddedSem
    • PTIVIS21S
    Responsible unit

    Engineering and Business

    Additional information

    -

    Degree Programme(s)

    Degree Programme in Information and Communications Technology

    Campus

    Kupittaa Campus

    Assessment scale

    H-5

    Alternative methods of attainment for implementation

    -

    Pedagogic approaches

    Lab works
    Lectures
    Self study

    Student's schedule and 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

    Assessment criteria
    Failed (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)