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Professional Certificate in

Security Analytics Course

With our Security Analytics course, you will emerge as an HR professional ready to grab innumerable opportunities in the space of Self-Service Avenues, Talent Management, Payroll, and Reporting.
  • 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
Security Analytics course - 360digitmg
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Security Analytics course reviews - 360digitmg
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Academic Partners & International Accreditations
  • Security Analytics course certification with Microsoft
  • Security Analytics course certification certification with NASSCOM certificate
  • Security Analytics course certification with INNODATATICS certificate
  • Security Analytics course certification with TUV
  • Security Analytics course certification with SUNY
  • Security Analytics course certification with NEF

The need for Security Analytics has grown exponentially due to the rapid advancements in malware and cyber exploits. Security Analytics can be defined as a combination of the analytic process, software, and algorithms used to detect potential IT threats.

Security Analytics

Program Cost

INR 12,000/-

Overview of Security Analytics Course

Hackers are attacking IT firms with malware aggressively and it takes a long time for the IT department to detect ad counter the attack, to keep the company safe from cyber-attacks Security Analytics is a boon to organizations. To stay ahead of the cyber attackers, traditional security solutions like antivirus, firewalls and hacking detection and prevention systems will require automation and it is possible with the help of Security Analytics. 360DigiTMG course graduates can easily develop optimized security solutions and help companies in countering cyber-attacks.

Security Analytics Course Training Learning Outcomes

Machine Learning and Big Data Analytics have become game-changer in the Security analytics domain. The Certification Program in Security Analytics is a sui generis attempt to blend machine learning solutions for traditional security problems. Specifically designed to suit the security 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 Security Analytics course is meant for professionals from the security domain as it provides a comprehensive picture of how data science and artificial intelligence can be leveraged to increase the speed and accuracy of developing anti-cyber-attack applications. Understanding the applications of Data Science, Machine Learning to security will be the prime objective of this content-rich program.

Work with diverse information sources, including logs, network traffic, and security events
Analyze structured and unstructured security data using various tools and techniques
Develop a solid understanding of descriptive and predictive analytics specific to security analytics
Apply data-driven, machine learning approaches for identifying security threats and predicting potential attacks
Gain knowledge of survey methodologies and perform analytics on security-related surveys
Utilize machine learning techniques to enhance security measures and personalize security experiences
Utilize data visualization concepts to effectively represent security data for enhanced comprehension and decision-making.
Block Your Time
Security Analytics course - 360digitmg

16 hours

Classroom Sessions

Security Analytics course - 360digitmg

20 hours

Python
Programming Videos

Who Should Sign Up?
  • IT Security Professionals
  • Cybersecurity Analysts
  • Security Operations Center (SOC) Analysts
  • Security Engineers
  • Incident Responders
  • Threat Intelligence Analysts
  • Data Scientists with an interest in security
  • Information Security Managers
  • Security Consultants
  • Security Auditors
  • IT Professionals looking to specialize in security analytics

Security Analytics Course Modules

  • This module introduces the topic of security
  • 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
  • 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
How we prepare you
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