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Cybersecurity Data Science Training Course
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Data Science is regarded as one of the most in-demand careers today. It is also one of the most fascinating and diversified IT careers in the world, with premium packages that attract attention. The majority of cyber-attacks compromise a company’s stored data and use it for fraudulent purposes. Data security is at the heart of cybersecurity. Cybersecurity Data Science training course from Infosectrain is a novel approach to using data science to detect, prevent, and mitigate cybersecurity threats.

Cybersecurity Data Science Course Highlights

  • 24 hours of Instructor Led training
  • Certified & Experienced Trainers
  • Small Size Batch
  • 1 : 1 Mentor Support
  • Access to Pre-recorded Sessions
  • Study Materials

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InfosecTrain Learning Bonanza Offer Buy 1 Get 4*

Register for any Course and get 4 eLearning (Worth USD 199 Self-paced Learning) Courses 100% free. Don't miss this offer Enroll Now

  • Cyber Security Engineer Masterclass
  • Cyber Security Fundamentals (JCP)
  • Introduction of Data Privacy
  • Red Team Ethical Hacking Masterclass

*All 4 free courses are eLearning modules, providing self-paced learning through instructional videos. Offer valid till February 29th, 2024.

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Cybersecurity Data Science Course Description


Today, data science is one of the most in-demand fields in the IT business. Cybersecurity has also undergone tremendous technological and operational changes in recent days. Infosectrain’s Data Science for Cyber Security course is designed to be an all-in-one resource that may help you advance your profession. Examine all of the fundamentals of Data Science for Cyber Security while also ensuring that you have sufficient exposure to more advanced topics.

The key to making a security system automated and intelligent is to extract security incident patterns or insights from cybersecurity data and construct a data-driven model to go with it. Various scientific methodologies, machine learning techniques, procedures, and systems are employed in order to comprehend and analyse the actual phenomena with data. The information is acquired from reputable cybersecurity sources, and the analytics are used to supplement the most recent data-driven patterns in order to provide more effective security solutions.

Why Data Science for Cyber Security Training with Infosectrain?

InfosecTrain is a proficient technology and security training and consulting organization across the globe, specializing in various IT security courses and services. Our Data Science for Cyber Security training course aims to apply Data Science skills in Cyber Security to mitigate the security threats. You can leverage the following benefits with InfosecTrain:

  • We can help you present your qualifications and work experience for the designated profile.
  • We provide a flexible training schedule.
  • We provide recorded videos after the session to each participant.
  • We provide post-training assistance.
  • We also provide a certificate of participation to each candidate.

Target Audience

  • Candidates who want to build their career in Cyber Security and Data Science
  • Candidates willing to learn Data Science for Cyber Security from Scratch


  • Basic knowledge of programming languages
  • Basic understanding of network essentials, core concepts including server and network components


5 + 35 =

Cybersecurity Data Science Course Content

Introduction to Data Science

Introduction to Cyber Security

Python Basics

  • Statements and Loops
  • Creating Functions
  • Data Wrangling using Pandas and NumPy

Exploratory Data Analysis

  • Summary Statistics
  • Data Visualization
  • Missing Value detection
  • Outlier detection
  • Data Transformation
  • Splitting the data Into Training and Test Data

Machine Learning for Cyber Security

  • Data Dimensionality Reduction PCA Statistical Intuition and Implementation
  • Segregating data using clustering Statistical Intuition and Implementation
  • Training an XGBoost classifier
  • Analyzing time series using statsmodels Explanation and Implementation using statsmodels
  • Anomaly detection with Isolation Forest Explanation and Implementation
  • Natural language processing using a hashing vectorizer and Tf-Idf Explanation and Implementation
  • Hyperparameter tuning with scikit-optimize Implementation
  • Generating text using Markov chains

Email Cybersecurity Threats Detection

  • Introduction to detect spam with Perceptrons
  • Introduction to Perceptrons
  • Introduction to spam filters
  • Spam filter in action
  • Detecting spam with linear classifiers
  • How the Perceptron learns
  • A simple Perceptron-based spam filter
  • Pros and cons of Perceptrons
  • Introduction to Spam detection with SVMs
  • SVM spam filter example
  • Introduction to Phishing detection with logistic regression and decision trees
  • Linear regression for spam detection
  • introduction to Logistic regression
  • Logistic Regression Implementation
  • Introduction to making decisions with trees
  • Phishing detection with decision trees
  • Spam detection with Naive Bayes
  • NLP with Naive Bayes Implementation

Malware Threat Detection

  • Introduction to Malware detection
  • Malware goes by many names
  • Malware analysis tools of the trade
  • Static malware analysis
  • Dynamic malware analysis
  • Hacking the PE file format
  • Introduction of Decision tree malware detectors
  • Malware detection with decision trees
  • Random Forest Malware classifier
  • Clustering malware with K-Means
  • K-Means steps and its advantages and disadvantages
  • Detecting metamorphic malware with HMMs Introductions
  • Polymorphic malware detection strategies
  • HMM Implementation

Advanced malware threat detection

  • Obfuscation Detection Python Implementation
  • Obfuscation Detection Python Explanation
  • Tracking malware drift Implementation
  • Tracking malware drift Explanation

Network Anomaly Detection

  • Turning service logs into datasets
  • Introduction to classification of network attacks
  • Detecting botnet topology
  • Introduction to different ML algorithms for botnet detection
  • Introduction to Gaussian anomaly detection
  • Gaussian anomaly detection Implementation

Securing Users Authentication

  • Introduction to Authentication abuse prevention
  • Fake login management- reactive versus predictive
  • Account reputation scoring
  • User authentication with keystroke recognition Introduction
  • User authentication with keystroke recognition Implementation
  • Biometric authentication with facial recognition Introduction
  • Dimensionality reduction with principal component analysis (PCA) Introduction
  • Eigenfaces Implementation

Automatic intrusion detection

  • Detecting DDos Attack
  • Credit Card fraud detection Introduction
  • Credit Card fraud detection Implementation
  • Counterfeit bank note detection Implementation
  • Ad blocking using machine learning Implementation
  • Wireless indoor localization Implementation
  • IoT device type identification using machine learning
  • Deepfake recognition

Securing and Attacking Data with Machine Learning

  • Assessing password security using ML
  • ML-based steganalysis Introduction
  • ML-based steganalysis Implementation
  • ML attacks on PUFs Introduction
  • ML attacks on PUFs Implementation
  • ML attacks on PUFs Explanation
  • HIPAA data breaches – data exploration and visualization


  • Online Transaction Fraud Detection
  • Fake News Detection
  • Fake Product Review Detection
  • Spam Email Detection

Credit Card Fraud Detection

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Cybersecurity Data Science Course Benefits

Cybersecurity Data Science career benefit

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Benefits You Will Access Why Infosec Train

Student-infosectrain Certified & Experienced Instructors
24x71-infosectrain Post Training Support
tailor-infosectrain Customized Training
flexible-infosectrain Flexible Schedule
video1-infosectrain Access to Recorded Sessions

Cybersecurity Data Science FAQs

1. Why Data Science with Us?
InfoSecTrain is a leading certification and upskilling training provider with alumni across the globe. Our trainers are high-experienced industry veterans with years of experience in the different domains and roles in Data Science. Our state-of-the-art online training materials along with access to downloadable study resources and access to pre-recorded training sessions are a boost to your Data Science Learning journey.
2. Can you combine data science with cyber security?
Artificial intelligence and data science can augment traditional cybersecurity, and it can do wonders.
3. Is data science required for cyber security?
While data science relies on cyber security for data integrity and protection, cyber security relies on data science to acquire relevant, actionable data to help secure systems, networks, and data.
4. What is the role of data analytics in cybersecurity?
When we talk about big data analytics in cyber security, we’re talking about the ability to collect massive volumes of digital data. It works by extracting, visualising, and evaluating futuristic insights in order to predict severe cyber threats and attacks ahead of time.
5. What are the risks associated with big data?
Big data raises security problems—privacy and security are major considerations when it comes to big data. Big data can be abused by bad actors. If data entered into the wrong hands, it can be used for phishing, frauds, and spreading disinformation.

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