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Defense Analytics Course

With our Defense 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
Defense Analytics course - 360digitmg
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Academic Partners & International Accreditations
  • Defense Analytics Course with Microsoft
  • Defense Analytics Course certification with NASSCOM certificate
  • Defense Analytics Course with INNODATATICS certificate
  • Defense Analytics Course with TUV
  • Defense Analytics Course with SUNY
  • Defense Analytics Course with NEF

Defense Analytics is an application of Big Data Analytics to the defense data to make informed mission-critical decisions, drive operational agility, and improve service delivery with data.

Defense Analytics

Program Cost

INR 12,000/-

Overview of Defense Analytics Course

Data Analytics has been helping all domains in solving crucial issues and taking informed and profitable decisions. Its defense application has been a lifesaver for public services. Defense agencies are leveraging data to make a critical response faster. Thanks to Defence Analytics now government agencies are responding fast to crises and delivering economic relief and public services. 360DigiTMG course graduates can easily develop optimized Defense solutions and help agencies to make informed decisions.

Defense Analytics Course Training Learning Outcomes

Defense Analytics is a special branch of Data Analytics that involves using public data with analytical tools to improve and provide better public service.
The Certification Program in Defense Analytics is a sui generis attempt to blend analytics solutions for better Defense practices and the development of new strategies for efficient crisis response. Specifically designed to suit the defense professionals and data professionals who wish to understand the application of Big Data Analytics, Machine Learning, Neural Networks, and Deep Learning to defense industry data. Our Defence Analytics course is meant for professionals from the defense domain as it provides a comprehensive picture of how data science and artificial intelligence can be leveraged to increase the efficiency of defense practices and strategies. Understanding the applications of Data Science, Machine Learning to defense 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
Gaining specialization in defense IT sector
Block Your Time
hr analytics course - 360digitmg

16 hours

Classroom Sessions

hr analytics course - 360digitmg

20 hours

Python
Programming Videos

Who Should Sign Up?
  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Math, Science Graduates
  • Graduates Planning to Apply for Railway Jobs

Defense Analytics Course Modules

This document details the defense 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 defensive strategy of the military.

  • Introduction to the world of Defense System for Countries
  • Importance of Logistics in any sort of Military operations
  • Usage of drones for threat monitoring and identification
  • Discussion on the automated self-defense systems like the Iron dome of Israel
  • Basic information on AI and ML
  • Examples of important logistic failures in the history of military warfare, using the example of Napoleon’s 1812 invasion of Russia
  • 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: Clustering of various nations across the globe, as per their defense spending per capita GDP percentage, using historical data from 1960 to 2020
  • 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: Classifying the various nations of the world in terms of their overall weapons export since 1960
  • 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 hyper parameter tuning to optimize the tree
  • Use Case: Classification of historical bombing during ww2 to understand how the process of choosing targets could have been optimized with ML, and how it could have helped avoid various unnecessary civilian casualties
  • 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: This use case deals with the cost of setting up forward operating bases which act as the frontline of any defensive strategy of the military. This will be dealt with the usage of MLR to make accurate predictions on the overall cost of military operations
  • 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 to gradient descent
  • Use Case: Casualties are the harsh reality of war. However, not all casualties are directly related to war itself. Using ANN, trying to understand and predict how casualties of war can be classified

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