- 3 Months
- Online Electrical Engineering
- Professional Certificate
- Electrical Engineering
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 around the globe.
- Flexibility of attending anytime from anywhere, even when you are working full-time.
- Interact with industry experts during the webinars and get the latest updates/announcements on the subject.
- Experience a global learning with students from various backgrounds and experience which is a great networking opportunity.
- Learn the latest applications of Big Data & Data Analytics in electricity grids using applied industry focused case studies.
- Study skills and knowledge about the foundations of data and the use of powerful machine learning models such as Artificial Neural Networks and decision trees.
- Learn about problem solving techniques using data analysis and machine learning and develop solutions to solve problems.
The 12-week part-time course, led by an industry expert will provide you with a practical, in-depth view of the use of data analytics and machine learning to solve problems in electricity production and distribution.
55% of companies have already adopted Big Data analytics to reduce overall cost and increase profit. With this in mind, this program has been tailored to equip you with the skills and knowledge to stand out from the crowd and harness the power of big data, working at the forefront of a fast-growing, dynamic, and future proof field.
The course will cover the foundations of data analytics including data acquisition, pre-processing, and visualization. You will learn how to make use of powerful machine learning models such as Artificial Neural Networks and decision trees.
By the end of the program, you will be able to identify problems that could be solved using data analysis and machine learning, and you will be able to develop solutions to such problems.
Module 1: Data analytics and machine learning basics
1. Data analytics
2. Machine Learning and Artificial Intelligence
3. Supervised, Unsupervised, Reinforcement Learning
4. Building and deployment
5. Evaluation of a system
Module 2: Data flow and feature engineering
1. Data sources: sensors, behaviors, social networks, text, images, videos, sounds
2. Data preprocessing
3. Features and feature vectors
4. Data visualization
5. Data mining
6. Big data
Module 3: Mathematical background
1. Statistics and probabilities
4. Similarity estimation
5. Game theory
Module 4: Algorithms (I)
1. K-means algorithm
2. A-priori algorithm
3. Genetic algorithms
Module 5: Algorithms (II)
1. K-Nearest Neighbors
2. Naïve Bayes
3. Decision trees
4. Linear regression
Module 6: Algorithms (III)
1. Feedforward Neural Networks
2. Convolutional Neural Networks
3. Recurrent Neural Networks
Module 7: Applications (I)
1. Dimensionality reduction
2. Finding correlations/correlation analysis
5. Time series analysis/forecasting
7. Model Predictive Control
Module 8: Applications (II)
1. Natural language processing
2. Knowledge representation: databases, ontologies, rules, natural language and chatbots
Module 9: Tools (I)
2. Pandas, Numpy, Matplotlib
Module 10: Tools (II)
5. Cloud-based solutions
Module 11: Case studies (I)
1. SCADA data analytics for Intelligent Alarm processing
2. SCADA data analytics for Predictive maintenance
3. Electricity demand forecasting (short- and long-term)
4. Short-term wind and solar power forecasting
5. Sentiment analysis on social media
6. Data visualization using clustering
7. Statistical Process Control for event/anomaly detection
Module 12: Case studies (II)
1. Fraud detection
2. Online and offline smart metering data analytics
3. Predictive outage management
4. Consumer modeling and segmentation
5. Sensor data for failure/fault predictions
6. Condition monitoring (generators, transformers, converters, breakers)
7. Energy management systems
8. Recommender systems
9. 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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Learn more about payment methods, including payment terms & conditions and additional non-tuition fees.
You are expected to spend approximately 5-8 hours per week learning the course content. This includes attending a weekly webinar that runs for about 90 minutes to facilitate class discussion and allow you to ask questions. This professional development program is delivered online and has been designed to fit around full-time work. It will take three months to complete.
Registrations are open for our upcoming intakes. Please ensure you book your place at least one week before the start date of the program.
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