Professional Certificate of Competency in Big Data and Analytics in Electricity Grids

Module 1: Data analytics and machine learning basics

• Data analytics
• Machine Learning and Artificial Intelligence
• Supervised, Unsupervised, Reinforcement Learning
• Building and deployment
• Evaluation of a system

Module 2: Data flow and feature engineering

• Data sources: sensors, behaviors, social networks, text, images, videos,
  sounds
• Data preprocessing
• Features and feature vectors
• Data visualization
• Data mining
• Big data

Module 3: Mathematical background

• Statistics and probabilities
• Derivatives
• Optimization
• Similarity estimation
• Game theory

Module 4: Algorithms (I)

• K-means algorithm
• A-priori algorithm
• Genetic algorithms

Module 5: Algorithms (II)

• K-Nearest Neighbors
• Naïve Bayes
• Decision trees
• Linear regression

Module 6: Algorithms (III)

• Feedforward Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks

Module 7: Applications (I)

• Dimensionality reduction
• Finding correlations/correlation analysis
• Clustering
• Classification
• Time series analysis/forecasting
• Predictions
• Model Predictive Control

Module 8: Applications (II)

• Natural language processing
• Knowledge representation: databases, ontologies, rules, natural language and
  chatbots

Module 9: Tools (I)

• Python
• Pandas, Numpy, Matplotlib
• Scikit-learn
• Statsmodel
• Tensorflow
• NLTK

Module 10: Tools (II)

• MATLAB
• R
• WEKA
• Cloud-based solutions

Module 11: Case studies (I)

• SCADA data analytics for Intelligent Alarm processing
• SCADA data analytics for Predictive maintenance
• Electricity demand forecasting (short- and long-term)
• Short-term wind and solar power forecasting
• Sentiment analysis on social media
• Data visualization using clustering
• Statistical Process Control for event/anomaly detection

Module 12: Case studies (II)

• Fraud detection
• Online and offline smart metering data analytics
• Predictive outage management
• Consumer modeling and segmentation
• Sensor data for failure/fault predictions
• Condition monitoring (generators, transformers, converters, breakers)
• Energy management systems
• Recommender systems
• Resilient operation of power grid

To obtain a certificate of completion for EIT’s Professional Certificate of Competency course, students must achieve a 65% attendance rate at the live, online weekly webinar.  Detailed summaries or notes can be submitted in lieu of attendance.  In addition, students must obtain a mark of 60% in the set assignments which could take the form of written assignments and practical assignments. Students must also obtain a mark of 100% in quizzes.  If a student does not achieve the required score, they will be given an opportunity to resubmit the assignment to obtain the required score.

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You are expected to spend approximately 5-8 hours per week learning the course content. This includes attending fortnightly webinars that run for about 90 minutes to facilitate class discussion and allow you to ask questions. This program has a 65% attendance requirement in the live webinars in order to graduate from the program.  If you are unable to attend the live webinars, you have the option of watching the recording of completed webinars and sending a summary of what you have learnt from the webinar to the Learning Support officer.  The summaries go towards your attendance requirement for the program.

This program is run online on an intensive part-time basis and has been designed to fit around full-time work. It will take three months to complete.

We understand that sometimes work commitments and personal circumstances can get in the way of your studies, so if at any point you feel that you are struggling with the pace of the course or finding a particular module challenging, you are encouraged to contact your designated Learning Support Officer for assistance.

Registrations are open for our upcoming intakes. Please ensure you book your place at least one week before the start date of the program.