Big Data and Cloud Computing for Industrial Applications
The manufacturing industry collects increasingly large volumes of big data, that is, data at high speed, generated from a wide range of sources in different formats and quality levels. But what is data without insight? This course will help you master the fundamental concepts of big data, cloud computing and smart decision-making for industrial analytics.
Designed specifically for manufacturing sector professionals, this Master’s course provides knowledge and insights in handling and processing data, using machine learning and data analytics in the cloud environment. You will learn machine learning-based solutions for industrial applications, such as smart decision-making and predictive maintenance, using state of the art cloud platform tools. The course is held in a combination of online lectures and physical lectures at the MDH campus in Eskilstuna.
- Cloud computing principles and cloud services
- Big data management
- Data analytics principles
- Machine learning principles and techniques for big data
What you will learn
- Demonstrate the ability to practically and theoretically translate knowledge within cloud services for applications in production, logistics and product development.
- Demonstrate the ability to use machine learning systems in a manufacturing or production environment.
- The advantages and disadvantages of using cloud computing and the most important driving forces and obstacles for using cloud computing in the manufacturing sector. You will also be able to describe and explain the different services that can be offered by different cloud platforms.
- The basic principles of machine learning and big data. You will also be able to describe and understand the most important prerequisites and challenges for usingbig data and machine learning within the manufacturing sector.
- Be able to describe the high-level design decisions and machine learning pipelines for building production machine learning systems, and concepts of scalable machine learning models.
- Understand and use suitable tools for analysis of manufacturing data and explain results obtained from them.
40 credits in Engineering/Technology and at least 2 years’ experience in full-time employment in a relevant area within industry. 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.
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.
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.
The course is part of the Premium project in which MDH offers courses aimed at giving the industry the competence needed for sustainable and smart production.For companies that want to collaborate on competence development