, Applied Data Science and Machine Learning

APPLIED DATA

SCIENCE AND

MACHINE

LEARNING

We’ve teamed up with GTK Cyber, the best at the intersection between data science and cybersecurity for this interactive course.

Learn how to utilize data science techniques to quickly manipulate and analyze network & security data and ultimately uncover valuable insights from this data.

Coming soon Q1 2022.

Course Objectives

  • Rapidly explore, visualize and analyze security data using open source tools

  • Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference

  • Identify and deploy modeling in order to extract meaningful information for decision making

  • Construct, train, evaluate & deploy supervised ML models to solve difficult security related problems

  • Construct unsupervised models for anomaly detection and other exploratory analysis

32 Lecture Hours

Online
Instruction

19 Labs

Heavily Interactive Learning

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Certificate

Awarded Upon
Completion

Questions?

Book a call with our team

Course Overview

The Applied Data Science for Cybersecurity interactive course will teach cybersecurity professionals how to use data science techniques to quickly manipulate and analyze network & security data and ultimately uncover valuable insights from this data. Through cutting-edge labs and real-world data sets, students will gain real and applicable knowledge in data science specifically applied to cybersecurity challenges.

The course will cover the entire data science process from data preparation, feature engineering and selection, exploratory data analysis, data visualization, machine learning, model evaluation & optimization and finally, implementing at scale. Participants will learn how to read data in a variety of common formats, and then write scripts to analyze and visualize the data in meaningful ways. The course is specifically designed to provide sophisticated training in data science as applied to cybersecurity-related challenges & scenarios. Additionally, it would be beneficial for participants to be comfortable with the basics of Python programming, but it is not required to in order to take this course.

Once you’ve completed this course you have the skills to read data in a variety of common formats then write scripts to analyze and visualize that data.

Curriculum

  • Exploratory Data Analysis in One Dimension
  • Exploratory Data Analysis in Two Dimensions
  • Data Visualization
  • Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Anomaly Detection
  • Hunting with Data Science
  • Hacking Machine Learning Models
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Frequently Asked Questions

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