During this workshop, the Alstom data science team will provide their experiences on applying data science methods in the transportation industry, for different vital and non-vital applications. First, they will talk about a variety of topics such as data collection, cleaning, and mining, manual and automatic feature engineering and selection, and model building, evaluation, and tuning. Then, the Alstom data science team will share their thoughts on the pre-production approval of models and results with systems engineering, validation, safety, and software teams. Finally, model extensions for real industry-grade applications will be presented. Basic and advanced statistical and machine learning techniques, applicable for data mining, anomaly detection, inference, and prediction, will be demonstrated using python.
What you will learn?
- Exploratory data analysis
- Statistical data analysis
- Machine Learning techniques
- Feature engineering
- Basics of python language, including pandas and scikit-learn
- Models evaluation
- Model results presentation
- Models industrialization
Who is this workshop for?
- Technical managers to understand how statistical and machine learning techniques can help with challenging tasks in the transportation industry.
- Software developers to learn statistical and machine learning methods that should be industrialized on the edge and cloud platforms.
- A strong desire to learn about data science
- A moderate desire to go deep into math and stats
- Some programing knowledge (preferably python)
|(1) Why use data science in transportation?
|(2) Four key tasks for data scientists: describe, diagnose, predict, and prescribe
|(3) Example applications in transportation
|(4) Hands-on: Data science in action
|(5) Final remarks
Dr. Nenad Mijatovic: With over 20 years of industry and academia experience, Nenad Mijatovic has gained valuable knowledge in data analytics, optimization, machine learning, and system engineering. Dr. Mijatovic has held multiple leading positions in the engineering and R&D departments of bluechip and start-up companies. His special interests include data mining, classification, inference, and the prediction of time-series data. Furthermore, he researches change detection approaches of streaming data. He is the inventor on several patents applied the mobility sector. Currently, Dr. Mijatovic is leading the data analytics team of Alstom NAM.
Dr. Emilio Barcelos: An experienced engineer, leader, and innovator with appointments in both industry and academia, Emilio Barcelos holds a doctoral degree in engineering and a professional MBA in business. His areas of interest include Image Analysis, Artificial Intelligence, Infrared Thermography, Smart Mobility, and Data Science. Dr. Barcelos has contributed to the creation of many real-life applications and serves as a frequent reviewer for high-impact scientific journals. He often mentors and consults on emerging and disruptive technologies. At Alstom, Emilio Barcelos leverages the research and development of strategic, data-driven, and AI-empowered applications for the rail industry.