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Legal and Law Analytics Course

With our Legal and Law 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.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 16 Hours of Intensive Online Sessions
  • 20 Hours of Free Python Programming Videos
  • Get Quiz Questions and Use Cases
Legal and Law Analytics course - 360digitmg
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  • Legal and Law Analytics Course with Microsoft
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  • Legal and Law Analytics Course with INNODATATICS certificate
  • Legal and Law Analytics Course with TUV
  • Legal and Law Analytics Course with SUNY
  • Legal and Law Analytics Course with NEF

Data Analytics are becoming ubiquitous in many industries and the Legal and Law department is no exception. Legal analytics can be defined as the application of data analysis methods and technologies within the field of law to improve efficiency, gain insight, and aid in taking valuable decisions.

Legal and Law Analytics

Program Cost

INR 12,000/-

Overview of Legal and Law Analytics Course

Implementation of Legal and Law Analytics will give a competitive advantage to the law firms and drive corporate profits. Data Analytics applications can be used to mine data from previous cases and information and insights gathered can be used in taking better and informed actions. 360DigiTMG course graduates can easily able to process legal and law data and draw insights from it that can be used in taking better decisions.

Legal and Law Analytics Course Training Learning Outcomes

Legal and Law Analytics is a special branch of Data Analytics that involve using employee-related data with analytical tools to take better legal decisions. The Certification Program in Legal and Law Analytics is a sui generis attempt to blend analytics solutions for better legal practices and the development of new strategies for gaining efficient insights which can be used in making informed decisions. Specifically designed to suit the Legal and Law professionals and data professionals who wish to understand the application of Big Data Analytics, Machine Learning, Neural Networks, and Deep Learning to Legal and Law industry data. Our Legal and Law Analytics course is meant for professionals from the Legal and Law domain as it provides a comprehensive picture of how Data Science and Artificial Intelligence can be leveraged to increase the efficiency of legal practices and strategies. Understanding the applications of Data Science, Machine Learning to insurance 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 legal functions such as legal research
Understanding how surveys work and perform analytics on them
Use ML techniques for e-discovery, predictive analytics, marketing and drawing insights from law firms.
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

Python
Programming Videos

Who Should Sign Up?
  • Legal Intern
  • Legal Researchers
  • Lawyers
  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Math, Science Graduates

Legal and Law Analytics Course Modules

This document details the legal and law analytics program. Sessions are broken down in a module-wise manner to understand the topics in a better way. The focus of this analytics will be on the overall how to implement analytics in legal systems.

  • Introduction to automation in Legal systems
  • Importance of analytics in document automation
  • Use of analytics for contract review.
  • Analytics in Litigation prediction.
  • Legal analytics and Legal research.
  • Information about Intellectual property.
  • 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
  • Use Case: Segmentation of US crime data and finding insights from it.
  • 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
  • Use Case: Classification of illegal activities in Chicago from 2001-2017.
  • Re-cap on 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
  • Use Case: Classification of criminal activities in Chicago from 2001-2017
  • 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
  • Use Case: Working on Swedish Auto Insurance problem.
  • 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
  • A brief intro to gradient descent
  • Use Case: Classification of illegitimate activities during 2001-2017 in Chicago city using MLP.

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