Course Overview

The Professional Certificate of Competency in Big Data and Analytics in Electricity Grids from the Engineering Institute of Technology is designed for professionals aiming to harness the power of data within the energy sector. You will explore how big data and advanced analytics are transforming electricity grids, enabling smarter decision-making, enhanced customer experiences, and more resilient infrastructure. This online certificate equips you with essential skills in data analysis, machine learning, and data visualization, preparing you to address critical challenges in areas like predictive maintenance, demand forecasting, and grid optimisation.

This online, part-time certificate is studied over 3 Months, allowing you to develop your expertise without interrupting your career. Through applied industry-focused case studies, you will gain practical experience with tools such as Python (Pandas, Numpy, Scikit-learn) and MATLAB, and learn to implement algorithms like decision trees and neural networks. You will work alongside experienced faculty and global peers, developing solutions for real-world problems in electricity grids. You will graduate with a recognised certificate and the advanced analytical capabilities sought by the modern energy industry.

Course Curriculum & Details

Detailed information for serious evaluators

Program Overview

This course provides a comprehensive introduction to big data and analytics within the context of electricity grids. It covers foundational concepts in data analytics and machine learning, including supervised, unsupervised, and reinforcement learning techniques. The curriculum delves into data flow, feature engineering, and essential mathematical background, alongside various algorithms and their applications. Practical case studies and industry-standard tools like Python, MATLAB, and R are explored to equip students with the skills needed for real-world grid analytics challenges.

1 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

2 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

3 Module 3: Mathematical background

Statistics and probabilities

Derivatives

Optimization

Similarity estimation

Game theory

4 Module 4: Algorithms (I)

K-means algorithm

A-priori algorithm

Genetic algorithms

5 Module 5: Algorithms (II)

K-nearest neighbors

Naïve Bayes

Decision trees

Linear regression

6 Module 6: Algorithms (III)

Feedforward neural networks

Convolutional neural networks

Recurrent neural networks

7 Module 7: Applications (I)

Dimensionality reduction

Finding correlations/correlation analysis

Clustering

Classification

Time series analysis/forecasting

Predictions

Model predictive control

8 Module 8: Applications (II)

Natural language processing

Knowledge representation: databases, ontologies, rules, natural language, and chatbots

9 Module 9: Tools (I)

Python

Pandas, Numpy, Matplotlib

Scikit-learn

Statsmodel

Tensorflow

NLTK

10 Module 10: Tools (II)

MATLAB

WEKA

Cloud-based solutions

11 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

12 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

Cost & Affordability

A clear picture of what this course will cost you

Program Costs

First Year Fee Tuition fee

USD 979.00

Total Program Cost Estimated total tuition

USD 979.00

Currency note: Program costs shown in USD based on the available tuition breakdown.

Living Costs

Accommodation

USD 641.00 - USD 1,522.00

Food & Groceries USD 6,400.00 - USD 8,000.00

Transport USD 1,061.00 - USD 1,350.00

Utilities & Bills USD 1,206.00 - USD 1,359.00

Estimated Total USD 8,667.00 - USD 9,109.00

Living cost estimates are based on Melbourne Campus.

Outcomes & Employability

What happens after you graduate

79%

Graduate Employment Rate Within 6 to 12 months of graduation

Common Graduate Roles

Artificial Intelligence & Machine LearningEnergy Storage & TechnologyMining Technology & InnovationNetworking & Internet ServicesOil & Gas Exploration & ProductionQuality Control & AssuranceResearch & Product DevelopmentSatellite CommunicationsSupply Chain & Freight ManagementTransport Engineering & Infrastructure

Top Employers Hiring for this course

Acwa Operations

Air Liquide

Australian Energy Market Operator (AEMO)

BHP

Botswana Power Corporation

Chevron

Cognetiks Consulting

Energy Queensland

Frequently Asked Questions

Common questions about Professional Certificate of Competency in Big Data and Analytics in Electricity Grids at Engineering Institute of Technology

Can I work while studying?

International students can work up to 48 hours every fortnight during term time and unlimited hours during breaks without needing a separate work permit.

What English language score do I need for this course?

To enroll, you need an IELTS listening score of 5.0, an overall score of 5.5, and scores of 5.0 in reading, speaking, and writing. Ensure you meet these requirements before applying.

What jobs can I get after studying this course?

Graduates can pursue careers as Data Analysts, Big Data Engineers, Machine Learning Specialists, Data Scientists, or Business Intelligence Analysts.

What will I actually study in this course?

Energy and utilities are taking advantage of the technology boom! They are turning knowledge into power by using big data & analytics in informing their decision making and customer journey. This course explores the use of big data & data analytics in electricity grids using applied industry focused case studies.

Big Data Analytics Course Benefits

Receive a Certificate of Completion from EIT.
Learn from well-known faculty and industry experts from

When can I start — what are the intake months?

The course has two intakes each year, in March and July. You can apply for either of these months to start your journey in Big Data and Analytics in Electricity Grids.

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