This is an interactive course, with the goal of teaching security professionals how to implement data science techniques in order to obtain valuable insights. This course will encompass various topics including: feature engineering, exploratory data analysis, data visualization, machine learning and probabilistic graphical models. I believe the combination of data science and security allows the security community to move our assumptions, opinions and beliefs into knowledge. This two day course teaches attendees the data science concepts, techniques and processes for analyzing various security datasets.

No previous data science experience is necessary as this course is designed for those who are new to data science. Background understanding programming is very helpful, specifically Python.

Student Requirements:

Technical Skills: No previous experience in data science is necessary. High-level understanding of programming is recommended.

Class Materials Needed: Students will be required to bring their own laptops for the class. Laptops will need a VMWare Workstation or VirtualBox installation with an install of any operating systems is fine, as well as the installation of Python3 and Jupyter Notebooks. I will provide a Ubuntu VM if necessary, all other tools will be provided.

Course outline:

Day 1:

• Introduction to Data Science
• Introduction/Review Python
• Introduction/Review Jupyter Notebooks
• Introduction/Review Kaggle
• What is Data?
• Data Visual Analysis
• Exploratory Data Analysis
• Time Series Analysis

Day 2:

• Graph Theory
• Machine Learning
• Probabilistic Graphical Models
• Cybersecurity Data Science Competition

Bio: Imani Palmer is a recent Ph.D. graduate from the Department of Computer Science at the University of Illinois. She has real-world experience in analyzing various datasets from digital forensics to network traffic. She has spoken at many security conferences on various topics and teaching courses at multiple institutions.