Professional Certificate in
Security Analytics Course
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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.
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.
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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
Trends in Security Analytics Course
A recent market survey has predicted that the Security Analytics market is expected to grow with a CAGR of 10.7% during the forecast period. With the rapid growth of the internet, traditional security solutions are no long enough to stop cyber-attacks. Big Data and Machine Learning is a great solution over computer networks that renders traditional solutions obsolete.
The traditional network infrastructure of the majority of organizations is slowly becoming vulnerable to cyber criminals. As IoT has advanced the speed of cyber-attacks also increased and to counter them Security Analytics is the solution.
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