•   Data Analysis and Visualization 5G00FT11-3001 14.01.2022-29.04.2022  7 cr  (20I260E) +-
    Learning outcomes of the course unit
    The student understands basic concepts of Statistics and Classical Data Analysis. The student can collect and preprocess data for analysis and visualization. The student is able make appropriate data analyses and visualizations for the problem. The student can evaluate both the quality and the applicability of the results.
    Course contents
    Course content is:
    - Data collection and Data Preprocessing
    - Visual analytics process
    - Basic methods of Statistics and Classical Data Analysis
    - Data analysis with Analytics Tools and Python
    - Visualization models
    - Critical evaluation of results
    Assessment criteria
    Satisfactory

    Student can sufficiently implement data collection and data preprocessing for a given task. Student knows how to implement visual analytics processes. Student knows some basic methods of statistics and classical data analysis. The student can solve some given data analysis problems with analytics tools or python. Student can use given visualization models. Student understands the meaning of the results.

    Good

    Student can implement data collection and data preprocessing for a given task. Student can implement visual analytics processes. Student knows and understands basic methods of statistics and classical data analysis. The student can solve given data analysis problems with analytics tools and python. Student knows and can exploit given visualization models. Student can evaluate the meaning of the results.

    Excellent

    Student can implement data collection and data preprocessing with the appropriate methods. Student can implement various visual analytics processes. Student knows and understands in depth basic methods of statistics and classical data analysis. The student can solve versatile data analysis problems with analytics tools and python. Student knows and can exploit visualization models as appropriate. Student can critically evaluate and interpret the meaning of the results.

    Further information
    Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.

    Name of lecturer(s)

    Ossi Nykänen

    Recommended or required reading

    Moodle course with links to additional material.

    Planned learning activities and teaching methods

    Contact teaching
    Assignments (the primary learning method)
    Group work and presentation

    Language of instruction

    English

    Timing

    14.01.2022 - 29.04.2022

    Registration

    15.11.2021 - 09.01.2022

    Credits

    7 cr

    Group(s)

    20I260E

    Seats

    0 - 62

    Teacher(s)

    Ossi Nykänen

    Further information for students

    NOTE: In accordance with the current Covid-19 guidelines, teaching in the Industrial Technology Unit shall be arranged (at least) UNTIL January 16, 2022 ONLY ONLINE.I .e. the course at least starts remotely via MS Teams.

    Old info (prior to the aforementioned covid instructions): By default, the course would be organized f2f at TAMK main campus (partial remote or hybrid participation via MS Teams might be available). See the Moodle course for instructions how to attend the contact teaching hours: https://moodle.tuni.fi/course/view.php?id=24456 . Please note that any additional covid restrictions might imply organizational changes to the course.

    Unit, in charge

    ICT Engineering

    Degree programme(s)

    Bachelor's Degree Programme in Software Engineering

    Office

    TAMK Main Campus

    Evaluation scale

    0-5

    Exam schedule

    No exam.

    Students use of time and load

    See the period timetable. See the Moodle course for instructions when and how to attend the contact teaching hours.

    Assessment criteria
    Not approved

    Less than 30% of the exercises completed.

    Satisfactory

    The student is familiar with the essential concepts and can implement simple applications with specific instructions. At least 30% of the exercises completed.

    Good

    The student is familiar with the basic concepts and can implement simple applications autonomously. At least 60% of the exercises completed.

    Excellent

    The student is familiar with the main concepts, is able to critically evaluate application requirements, and can implement realistic applications autonomously. At least 90% of the exercises completed. A good group work completed and presented.