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Predictive Data Analytics

  • Credits 2.5 credits
  • Study location Independent of location
  • $stringTranslations.StartDate 2020-11-30 - 2021-01-17 (part time 25%)
  • Education ordinance Second cycle
  • Course code DVA478
  • Main area Computer Science

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 this 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.

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 etc.

Course title in Swedish

Prediktiv dataanalys

Language

English

Teacher

Professor

Shahina Begum

+4621107370

shahina.begum@mdh.se

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

After submitting your electronic application, the next step is to submit documentation to demonstrate your eligibility for the course you have applied for. In order to document your eligibility, you must provide your high school diploma and university transcript and proof of your English language proficiency.

Entry requirements

To meet the entry requirements for this course you need to have previous academic qualifications (university studies). You will find the specific entry requirements above.

No academic qualifications?

If you do not have the formal academic qualifications needed for a specific course, you can apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc. This is also known as a validation of prior learning.

Recognition of prior learning means the mapping out and assessment of an individual's competence and qualifications, regardless how, where or when they were acquired – in the formal education system or in some other way in Sweden or abroad, just recently or a long time ago.

If you think your knowledge and competences will qualify you for this course, you will need to upload th following with your application:

  • CV with description of your educational and professional background. Your CV must describe your knowledge and competences in relation to the entry requirements.
  • If you refer to work experience, you need to upload an Employers certificate.

If we need more information, we will contact you.

FutureE

The courses are part of the FutureE project where MDH offers online courses in the areas of AI, Environmental and Energy Engineering, Software and Computer Systems Engineering.

For companies that want to collaborate on competence development
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