- 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
- Experience with Matlab
- No previous experience with data science is required
Many useful exercises, well explained
Helene Meadows - European Investment Bank
Hands on experience.
Matevz Nolimal - European Investment Bank
Exercises were most beneficent thing in the sessions
- Halcon Systems
Trainer took the initiative to cover additional content outside our course materials to improve our learning.
Chia Wu Tan - SMRT Trains Ltd
Following along with the coded examples as they were being written was effective. The trainer answered any queries that could not be immediately answered at the time soon after, having found the solution/answer during the break times.
It was a good mix of theory and examples to reinforce learning.
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.
Teaching style and ability of the trainer to overcome unforeseen obstacles and adapt to circumstances. Broad knowledge and experience of the trainer
His deep knowledge about the subject