Professional Certificate in
Energy and Resource Analytics Course
- Get Trained by Trainers from ISB, IIT & IIM
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The Modern world is advancing rapidly, thanks to energy and resources. And as it has a vast impact on modern life and produces a vast amount of data on how energy is used and the different resources needed for the optimum performance of businesses and our everyday life. Data Analytics is an incredible solution to unburden data management and deliver meaningful insights that will help in the profits of the organizations.
Overview of Energy & Resources Analytics Course
With Data Analytics, energy and resource companies can analyze data and process it to make profitable insights. Data is a valuable asset and processing it properly can help increase output, reduce downtime along with improving customer service. With efficient data analytics tools, companies can gain insights across geographies, services, sectors, and products to maximize profits for the company along with minimizing upstreaming costs. Companies can also retain and increase client, partner, and investor retention with interactive online dashboards. 360DigiTMG Energy and Resource Analytics course will introduce you to all the latest technological advancements that will help you strategize and provide insights for the profit of the organization.
Energy & Resources Analytics Course Training Learning Outcomes
Machine Learning and Big Data Analytics have become game-changer in the Energy and Resource Analytics domain. This Certification Program is a sui generis attempt to blend machine learning solutions for traditional customer retention problems and loyalty. Specifically designed to suit energy and resource professionals and data professionals who wish to understand the application of Big Data Analytics , Machine Learning, Neural Networks, and Deep Learning to Oil and Gas industry data. This Energy and Resouce Analytics course is meant for professionals from the energy and resource industry as it provides a comprehensive picture of how Data Science and Artificial Intelligence can be leveraged to increase productivity and profits in decisions. Understanding the applications of Data Science, Machine Learning to the energy and resource industry will be the prime objective of this content-rich program.
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Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Operations managers
- Energy traders
- Data analysts
- Energy and resource planners
- Sustainability managers
- Environmental scientists
- Policy analysts
- Researchers and students in energy and natural resources-related fields
Energy and Resources Analytics Course Modules
- Introduction to energy and resources management systems
- Energy demand forecasting and optimization
- Renewable energy sources and optimization
- Smart grid systems and energy analytics
- Energy efficiency analysis in buildings and industrial processes
- Carbon footprint analysis and reduction using analytics
- Natural resource management using analytics
- Supply chain optimization in the energy and resources sector
- Energy trading and risk management using analytics
- Emerging technologies and future directions in energy and resources analytics
- Introduction to the various stages of analytics and using an historical example of the various stages can help understand the steps involved in analytics
- Discussion on CRISP-ML (Q)
- Explanation on the importance CRISP-ML (Q) in various Data Science related projects
- Explanation on unsupervised and supervised learning
- Explanation on the training, validation, and testing stages of supervised learning
- Understanding right fit, underfit and overfit scenarios in supervised learning
- Understanding Hyperparameter tuning in the context of overfit
- This module covers the various steps of EDA
- Discussion on the 4 business moment decisions in EDA
- Discussion on univariate, bivariate, and multivariate EDA steps
- Explanation of the various pre-processing steps involved in any Machine learning or Artificial intelligence project
- Brief introduction to feature engineering
- Re-cap of unsupervised learning in ML
- Understanding of clustering in layman’s terminology
- Understanding K-means clustering from a technical perspective
- Understanding the usage of elbow curve and silhouette scoring to decide on the ideal number of clusters
- Use Case: Clustering Electric usage profiles.
- Re-cap on supervised learning
- Understanding of nearest neighbor in ML
- Understanding KNN algorithm and distance metrics
- Understanding of early stopping point in training phase
- Explanation of the need for odd number of neighbors when classifying
- Use Case: Customer Churn Prediction for the Utility Industry.
- Re-cap on Decision Trees
- Understanding the various components of a decision tree
- Understanding the idea behind root node and how it affects the overall tree
- Understanding entropy and information gain concepts of decision tree to make logical choices on root node
- Usage of hyperparameter tuning to optimize the tree
- Use Case: Predicting solar energy.
- Introduction to the concept of line equation
- Correlation of line equation in ML terms
- Understanding of correlation and its importance in Linear regression
- Differentiating between the equations of SLR and MLR
- Explanation on the OLS concept
- Use Case Predicting Wind energy.
- Re-cap on the topics covered before
- Understanding the importance of AI
- Understanding the reasoning behind the concept of neural networks
- Explanation on the perceptron algorithm
- Introduction to multilayer perceptron
- Understanding the concept of weight calculations
- Brief intro in gradient descent
- Use Case: Assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.
Trends in Energy & Resources Analytics Course
The coming decade will witness major and exciting changes in the energy and resources industry. The pandemic has changed the way businesses are done and has accelerated digitization and the growing need to integrate environmental, social, and governance (ESG) commitments with central business functions. The green energy transition is the major objective that needs to be achieved by energy and resource companies. Data Analytics is a major change that will help companies not only profit from whatever the future might bring but leave a positive social impact as their legacy.
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