HI-PP8 Course Data Science for Reliability Engineers

Demystifying data science for reliability and maintenance specialists building on its strengths.

Data Science for Reliability Engineers - opening image

With this course you’ll learn:

  • What value Big Data and machine learning may bring for a reliability engineer. 
  • A more data-analytic workflow, incorporating efficient data handling and effective model building. 
  • Understanding data science, machine learning and AI, their application in CRISP-DM projects, and how they are applied in the smart industry. 
  • Essential machine learning techniques for predictive algorithms (such as OLS regression, decision trees) and unsupervised learning techniques (clustering, text mining).

Teaching professionals.

Ir. Dorien Lutgendorf

Ir. Dorien Lutgendorf

Senior Reliability Specialist
Trained as a mechanical engineering and passionate about bridging reliability engineering and data science. Dorien is an experienced reliability expert having completed many projects in high-tech, automotive, energy & agro.
Bright Cape

Bright Cape

At Bright Cape, we breathe data. We have a talented and knowledgeable pool of colleagues with expertise in analytics & applied data science, data-driven experience design, process mining, and innovative products. We take our clients on a journey of discovery through their data and guide them in their digitization journey by building and implementing scalable, sustainable data science solutions.

Course information

  • Hybrid
  • 2 modules of 1 day

Investment

The investment is € 1.495 (excl. VAT) per participant.

Included course materials, lunch and refreshments. 

About the course Data Science for Reliability Engineers.

The focus on data science applications in reliability and maintenance engineering is unique. For a successful project, expertise in both data science and domain expertise are required, as well as tech-savvy and programming skills.

The vision underlying the course is that reliability engineers bring domain knowledge to the table, and by training them in the possibilities and way of working of data science they can successfully collaborate with experts in machine learning and data engineering.

Course: Data Science for Reliability Engineers.

Outcome

Given the availability of cheap sensors and data storage, and modern data infrastructures such as the Internet of Things, more and more information about a product’s lifetime, degradation and performance in the field are available. Also, new types of data, such as online product reviews and weather data, are now easily accessible.

RFs - outcome

These massive streams of data are a rich source of information for understanding and improving the reliability of products and for optimizing a maintenance strategy. They complement reliability testing, and may sometimes even be a cheaper alternative. They also create challenges for reliability engineers, in handling larger and less structured data sources, and in combining multiple data sources, possibly of various types, in a meaningful way.

Data Science for Reliability Engineers

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What you’ll learn.

Block 1 - day 1

What you'll learn on day 1

Day 1

  • Introduction into Data Science
  • CRISP-DM process
  • Text mining and natural language processing (NLP)
  • Data maturity & data quality

Block 2 - day 2

What you'll learn on day 2

Day 2

  • Data preparation & Scripting
  • Unsupervised learning technique Clustering
  • Supervised learning and model evaluation
  • Deployment possibilities
  • Organization maturity
  • Reliability and data science

Practical information.

For whom

The Data Science for Reliability Engineers course is suited for reliability and maintenance engineers, reliability team-leads/managers eager to enrich their knowledge with a basic understanding of the data science work field.

Certificate

After completing the full training, you receive proof of participation.

Location & Dates

Locatie
Eindhoven – High Tech Campus 29
 
Data 2022 Eindhoven
Dag 1: 7 september
Dag 2: 8 september

Groepsgrootte

Maximum 12 participanten 

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