Predictive Data Analytics
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
Below you find the entry requirements for the course. If you do not fulfill the requirements, you can get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etcetera. Read more in Application information below.
Occasions for this course
Autumn semester 2023
2023-09-11 - 2023-10-29 (part time 25%)
Independent of location
Number of mandatory occasions including examination: 0
Number of other physical occasions: 0
Course syllabus & literatureSee course plan and literature list (DVA478)
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, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.
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 can validate work experience to determine whether you have the qualifications 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, 45.5 kB, opens in new window.
• Download a template for Employers certificate Word, 38 kB, opens in new window.
If you have any questions regarding eligibility or application please send an e-mail to firstname.lastname@example.org