Certificate Course in
EV Data Analytics & Cyber Security
Duration – 45 Hours Hybrid Learning
Course Overview
This course covers the basics of automotive cybersecurity and the potential risks and threats to connected cars. Students will learn about the current state of the automotive industry, automotive electrical and electronics, automotive software technology and the connected car. It introduces students to the fundamentals of data analytics and its application in the automotive industry. The course covers the data analytics pipeline, including the tools and techniques used to analyse data. Overall, this course aims to equip students with the necessary knowledge and skills to understand and analyse the cybersecurity risks in connected cars and apply data analytics techniques to automotive systems data for predictive maintenance and fault diagnosis.
Specialization Stack Objectives
To understand the automotive industry and its development process, including megatrends, electrical and electronics systems and software technology
To develop knowledge about ADAS and autonomous driving and their cybersecurity implications
To understand the risks associated with the connected car and how to mitigate them
To understand data collection and analysis for EVs and automotive systems, including sensors, data acquisition and pre-processing
To understand big data platforms and tools such as Hadoop, Spark and Kafka and how to use them for processing and analysing automotive data
Course Modules
Automotive industry
Automotive megatrends
Automotive development process
Automotive electrical and electronics
Automotive software technology
The connected car
Automotive cybersecurity
Mobile apps for connected car
Car hailing and ride sharing
Connected parking and automated valet parking
ADAS and autonomous driving
Introduction to Data Analytics and its application in the Automotive Industry.
Understanding of the data analytics pipeline
Overview of data analytics, its tools and techniques
EV data collection and analysis
Sensors and data collection in EV
Data acquisition and pre-processing
Statistical analysis of EV data
Automotive system data collection and analysis
Sensors and data collection in automotive systems (such as engines, transmissions, brakes, etc.)
Data acquisition and pre-processing
Statistical analysis of automotive system data
Regression, classification and clustering
Principal component analysis (PCA) and linear discriminant analysis (LDA)
Predictive maintenance techniques in automotive systems
Fault detection and diagnosis
Remaining useful life (RUL) prediction
Introduction to big data platforms and tools (such as Hadoop, Spark, and Kafka)
How to use big data platforms to process and analyze automotive data
Projects & Case Studies
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Vulnerability testing
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Secure communication protocols
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Analyzing data from electric vehicles to optimize their energy efficiency and performance
Target Industry Job Profiles
Automotive Cybersecurity Engineer
Electric Vehicle Data Analyst
Connected Car Systems Engineer
ADAS (Advanced Driver-Assistance Systems) Engineer
EV Charging Infrastructure Specialist
EV Battery Management Systems Engineer
Predictive Maintenance Engineer
Autonomous Vehicle Systems Engineer
EV Performance Optimization Specialist
Big Data Engineer for Automotive Applications