Kaisa_2012_3_photo by Veikko Somerpuro

The students will acquire familiarity with the basic concepts of data science. The students will be able to distinguish between different kinds of data (e.g., statistical, structured, unstructured), and identify challenges related to big data (e.g., volume, velocity, veracity) and data governance (privacy, ethics, legal issues). The various goals of statistical modelling and machine learning, from exploration and data mining to validation and decision-making, and approaches suitable for them will be introduced. The students will be able to choose an appropriate machine learning setting (unsupervised, semi-supervised, supervised) and to evaluate the pros and cons of different approaches (for example, linear regression, deep learning, or decision trees). The students will be able to present the results of a data science project by means of reports and visualisations so that they can be used as a basis of operationalisation.

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Timetable

Here is the course’s teaching schedule. Check the course overview for possible other schedules.

DateTimeLocation
Mon 4.9.2017
10:15 - 12:00
Tue 5.9.2017
16:15 - 18:00
Mon 11.9.2017
10:15 - 12:00
Tue 12.9.2017
16:15 - 18:00
Mon 18.9.2017
10:15 - 12:00
Tue 19.9.2017
16:15 - 18:00
Mon 25.9.2017
10:15 - 12:00
Tue 26.9.2017
16:15 - 18:00
Mon 2.10.2017
10:15 - 12:00
Tue 3.10.2017
16:15 - 18:00
Mon 9.10.2017
10:15 - 12:00
Tue 10.10.2017
16:15 - 18:00
Mon 16.10.2017
10:15 - 12:00
Tue 17.10.2017
16:15 - 18:00

Course overview

Master's Programme in Data Science is responsible for the course.

The course belongs to the Data Science methods / Basic Studies in Data Science module.

The course is available to students from other degree programmes.

  • programming skills in one or more languages (python recommended; R will probably be helpful)
  • basic familiarity with command-line interfaces (such as Linux/Unix shell)

The students will acquire familiarity with the basic concepts of data science. They will be able to describe the importance of data science in science, in the society, and in business. Students will also be able to characterise the different roles of a data scientist, and understand the skills required by them.

The students will be able to distinguish between different kinds of data (e.g., statistical, structured, unstructured), and identify challenges related to big data (e.g., volume, velocity, veracity) and data governance (privacy, ethics, legal issues).

The students will learn to identify the problems and tasks involved in the life-cycle of a Data Science project, including data collection, data preprocessing, data management, data analysis, presentation, and operationalisation (end-user point of view). They will be able to store and access different kinds of data using suitable database and data management tools, implement conversions between different data formats, and to control the accessibility of privacy-sensitive data.

The various goals of statistical modelling and machine learning, from exploration and data mining to validation and decision-making, and approaches suitable for them will be introduced. The students will be able to choose an appropriate machine learning setting (unsupervised, semi-supervised, supervised) and to evaluate the pros and cons of different approaches (for example, linear regression, deep learning, or decision trees).

The students will be able to present the results of a data science project by means of reports and visualisations so that they can be used as a basis of operationalisation.

Recommended time/stage of studies for completion: 1st semester

Term/teaching period when the course will be offered: may be offered in the autumn or spring term or both. Typically in the autumn in period I

Basic concepts of Data Science, including:

  • data science in science, society, business
  • different kinds of data (statistical, structured, unstructured, big data, ...),
  • jobs of a data scientist

The life-cycle of a Data Science project:

  • data collection
  • data preprocessing, "wrangling" (data formats, XML, statistical data, ...)
  • data management (databases, accessibility, sharing, governance, ethics, privacy)
  • exploratory data analysis: summary statistics
  • presentation, visualisation
  • operationalisation (end-user point of view)

The literature and other materials (both required and recommended) will be specified each year.

Students are required to complete exercises and projects.

The assessment and grading of the course is based on completed exercises, one or more exams, and/or projects.

  • The course will be offered in the form of contact teaching
  • Possible attendance requirements will be decided each year
  • The completion of the course is based on exercises, one or more exams, and/or projects