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Course Outline
Introduction
- Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing
Overview of Big Data concepts
Capturing data from disparate sources
What are data-driven predictive models?
Overview of statistical and machine learning techniques
Case study: predictive maintenance and resource planning
Applying algorithms to large data sets with Hadoop and Spark
Predictive Analytics Workflow
Accessing and exploring data
Preprocessing the data
Developing a predictive model
Training, testing and validating a data set
Applying different machine learning approaches (time-series regression, linear regression, etc.)
Integrating the model into existing web applications, mobile devices, embedded systems, etc.
Matlab and Simulink integration with embedded systems and enterprise IT workflows
Creating portable C and C++ code from MATLAB code
Deploying predictive applications to large-scale production systems, clusters, and clouds
Acting on the results of your analysis
Next steps: Automatically responding to findings using Prescriptive Analytics
Closing remarks
Requirements
- Experience with Matlab
- No previous experience with data science is required
Testimonials
Trainer took the initiative to cover additional content outside our course materials to improve our learning.
Chia Wu Tan - SMRT Trains Ltd
Exercises were most beneficent thing in the sessions
- Halcon Systems
Students interact to solve problems
- 东风康明斯
Interaction
chengyang cai - 东风康明斯
Alternation theory / practice effective!
- CIRAD
Progressive presentation and application of methods
Aurélien Briffaz - CIRAD
Availability and adaptability, answers to questions
Jean-Michel MEOT - CIRAD
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
- Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
His deep knowledge about the subject
Teaching style and ability of the trainer to overcome unforeseen obstacles and adopt to circumstances. Broad knowledge and experience of the trainer
ASML
Overall good intro to Python. The format of using Jupyter notebook and live examples on the projector was good for following along with the exercises.
ASML
Hands on experience.
Matevz Nolimal - European Investment Bank
Many useful exercises, well explained
Helene Meadows - European Investment Bank
His deep knowledge about the subject
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