• Data Analysis (TZLM7300-3007),
         23.10.2023 – 08.12.2023,  3 cr  (TLE23SHYIT, ...) — Face-to-face +-
    Learning outcomes of the course
    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.
    Prerequisites and co-requisites
    You master basic statistics and related Excel functions.
    Course contents
    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
    Assessment criteria
    Assessment criteria - grade 1 and 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.
    Assessment criteria - grade 3 and 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.
    Assessment criteria - grade 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.
    Language of instruction

    English

    Planned learning activities, teaching methods and guidance

    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.

    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

    Lecturer(s)

    Pasi Lehtola

    Campus

    Main Campus

    Exam dates and re-exam possibilities

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

    Timing

    23.10.2023 - 08.12.2023

    Learning assignments and student workload

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

    Enrollment

    01.08.2023 - 24.08.2023

    Groups
    • TLE23SHYIT
    • TLP21S1
    • TLP23VS
    Alternative learning methods

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

    Seats

    0 - 20

    Assessment methods

    The assessment is based on learning tasks and exams.

    An equivalent course in Finnish TZLM7300-3006 Datan analysointi.

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

    Degree Programme

    Bachelor's Degree Programme in Purchasing and Logistics Engineering

    Mode of delivery

    Face-to-face

    Credits
    • 3 cr
    Unit

    School of Technology