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Regulatory Analytics Course

With our Regulatory Analytics course, you will emerge as a professional ready to grab innumerable opportunities in the current Industry that is focusing heavily to leverage on Data.
  • 16 Hours of Intensive 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
  • Regulatory Analytics course certification with Microsoft
  • Regulatory Analytics course certification certification with NASSCOM certificate
  • Regulatory Analytics course certification with INNODATATICS certificate
  • Regulatory Analytics course certification with TUV
  • Regulatory Analytics course certification with SUNY
  • Regulatory Analytics course certification with NEF

The application of Data Analytics to regulatory compliance has massive benefits to the domain and Regulatory Analytics combines technology with subject expertise to provide insight and guidance on trade, global tax, and business-related regulations for a company that is operating internationally.

Regulatory Analytics

Program Cost

INR 12,000/-

Overview of Regulatory Analytics Course

360DigiTMG Regulatory Analytics training program introduces the participants to all the necessary tools reliable for compliance with considerable regulatory burdens. They dramatically reduce the research time and allow the user to model alternative transactions to provide the user with regulatory and compliance data. With the help of analytics, regulatory analysts are well prepared to regulate company policies and taxes for companies that are operating internationally. 360DigiTMG course graduates can easily develop optimized trading solutions and help agencies to make informed decisions.

Regulatory Analytics Course Training Learning Outcomes

Machine Learning and Big Data Analytics have become game-changer in the Regulatory analytics domain. The Certification Program in Regulatory Analytics is a sui generis attempt to blend machine learning solutions for traditional trading problems. Specifically designed to suit the Regulatory professionals and data professionals who wish to understand the application of Big Data Analytics, Machine Learning, Neural Networks, and Deep Learning to financial industry data. This Regulatory Analytics course is meant for professionals from the trading domain as it provides a comprehensive picture of how Data Science and Artificial Intelligence can be leveraged to increase the speed and accuracy of regulatory decisions. Understanding the applications of Data Science, Machine Learning to regulatory compliance 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 regulatory compliance functions such as risk assessment.
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

Regulatory Analytics course - 360digitmg

16 hours

Classroom Sessions

Regulatory Analytics course - 360digitmg

20 hours

Programming Videos

Who Should Sign Up?

  • Compliance Officers
  • Risk Managers
  • Data Scientists
  • Policy Analysts
  • Legal Analysts
  • Regulatory Affairs Specialists
  • Business Professionals (in regulated industries)
  • Government Regulators
  • Data Analysts

Regulatory Analytics Course Modules

  • This module introduces the topic of regulations
  • Discussion on the importance of regulations
  • Basic information on AI and ML
  • Introduction to the various stages of analytics and using a historical example of the various stages can help understand the steps involved in analytics
  • Introduction to the various stages of analytics and using a historical example of the various stages can help understand the steps involved in analytics
  • Discussion on CRISP-ML (Q)
  • Explanation of the importance of CRISP-ML (Q) in various Data Science related projects
  • Explanation of unsupervised and supervised learning
  • Explanation of 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 overfitting
  • 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
  • A 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
  • Re-cap on supervised learning
  • Understanding of nearest neighbor in ML
  • Understanding the KNN algorithm and distance metrics
  • Understanding of early stopping point in the training phase
  • Explanation of the need for an odd number of neighbors when classifying
  • Understanding the decision-making process for humans and correlating to the topic of Decision Trees
  • Understanding the various components of a decision tree
  • Understanding the idea behind the root node and how it affects the overall tree
  • Understanding entropy and information gain concepts of decision tree to make logical choices on the root node
  • Usage of hyperparameter tuning to optimize the tree
  • 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 of the OLS concept
  • Re-cap on the topics covered before
  • Understanding the importance of AI
  • Understanding the reasoning behind the concept of neural networks
  • Explanation of the perceptron algorithm
  • Introduction to multilayer perceptron
  • Understanding the concept of weight calculations
  • Brief intro to gradient descent
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