Predictive Data Analytics

  • Credits 2.5 credits
  • Study location Independent of location
  • $stringTranslations.StartDate 2022-09-12 - 2022-10-30 (part time 25%)
  • $stringTranslations.StartDate 2022-09-12 - 2022-10-30 (part time 25%)
  • Education level Second cycle
  • Course code DVA478
  • Main area Computer Science
  • Application code: MDU-24546

The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling

About the course

The course aims to give insights in fundamental concepts of machine learning for predictive analytics to provide actionable, i.e., better and more informed decisions in, forecasting. It covers the key concepts to extract useful information and knowledge from data sets to construct predictive modeling. The course includes three modules: 

Introduction: overview of Predictive data analytics and Machine learning for predictive analytics.

Data exploration and visualization: presents case studies from industrial application domains and discusses key technical issues related to how we can gain insights enabling to see trends and patterns in industrial data.

Predictive modeling: consists of issues in construction of predictive modeling, i.e., model data and determine Machine learning algorithms for predicative analytics and techniques for model evaluation.

You will learn:

  • Select suitable machine learning algorithms to solve a given problem for predictive data analytics.
  • Explore data and produce datasets suitable for analytical modeling.
  • Basics of machine learning for predictive analytics

Entry requirements

90 credits of which at least 60 credits in Computer Science or equivalent, including 15 credits in programming as well as 2,5 credits in basic probability theory and 2,5 credits in linear algebra, or equivalent. In addition English course A/English course 6 is required.

You can also apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etcetera. Read more in Application information below.




Course syllabus

You can read in detail about the course, it's content and literature and so on in the course syllabus

See course syllabus

Apply to the course

Predictive Data Analytics

Go to application

Application information

You’ll find the entry requirements in the course description. After submitting your application, the next step is to submit documentation to demonstrate your eligibility for the course. Most academic credentials from Sweden are retrieved automatically. Wait a few days after submitting your application - if you still can’t see your academic credentials om My pages, please upload them.

If you have studied in another country, you must provide transcripts of your academic studies and of your English proficiency. Exactly what you need to submit and how, depends on several factors. You can read more on universityadmissions.se or antagning.se.

If the course requires work experience, you need to provide an employer’s certificate. You can download a template for employer’s certificate below.

No academic qualifications?

Many courses requires that you have previous academies studies, but we validate work experience to assess if you have the knowledge that is equal to the eligibility requirements for the course.

If you don’t have the formal qualifications required, please send in a certificate of employment (current or previous) and a CV/Description of competence that describes your educational and professional background. Please include a short description of your work experience, not only the work title.

Use the CV/ Description of competence template below and fill in the information requested.

You can also use our template for Employers certificate if you like.

Download a template for CV/Description of competence Word, 28 kB, opens in new window.

Download a template for Employers certificate Word, 19.5 kB, opens in new window.

If you have any questions regarding eligibility or application please send an e-mail to lifelonglearning@mdu.se