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Professional Certificate in

Energy and Resource Analytics Course

With our Energy and Resources Analytics course, you will emerge as an industry professional equipped with the skills to unlock valuable insights from complex data sets, optimize resource allocation, and drive sustainable business practices in the energy and resources sector.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 16 Hours of Intensive Classroom & Online Sessions
  • 20 Hours of Free Python Programming Videos
  • Get Quiz Questions and Use Cases
HR Analytics course - 360digitmg
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HR Analytics course reviews - 360digitmg
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Academic Partners & International Accreditations
  • Energy and Resource Analytics course certification with Microsoft
  • Energy and Resource Analytics course certification certification with NASSCOM certificate
  • Energy and Resource Analytics course certification with INNODATATICS certificate
  • Energy and Resource Analytics course certification with TUV
  • Energy and Resource Analytics course certification with SUNY
  • Energy and Resource Analytics course certification with NEF

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.

Energy & Resources Analytics

Program Cost

INR 12,000/-

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.

Work with various information sources
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of descriptive and predictive analytics
Apply data-driven, machine learning approaches for HR functions such as attrition prediction
Understanding how surveys work and perform analytics on them
Use ML techniques to create a personalized experience for employees
Use data visualisation concepts to represent data for easy understanding
Block Your Time
hr analytics course - 360digitmg

16 hours

Classroom Sessions

hr analytics course - 360digitmg

20 hours

Programming Videos

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.
How we prepare you
  • Energy and Resources analytics training with placements
    Get Quiz Questions and Use Cases
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    Live Free Webinars
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    Resume and LinkedIn Review Sessions
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    Lifetime LMS Access
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    24/7 Support
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    Job Placements in Energy and Resources analytics Fields
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    Complimentary Courses
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    Unlimited Mock Interview and Quiz Session
  • Energy and Resources analytics training with placements
    Hands-on Experience in Live Projects
  • Energy and Resources analytics course
    Offline Hiring Events

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