Foundations for Cybersecurity Analytics
Launch your career in Cybersecurity Analytics…
CLASSES BEGIN SUMMER 2024
Get ready for your career journey in Cybersecurity & Analytics!
How do businesses analyze data to generate new insights? How can technology and statistical methods enable data-driven decision making? How can machine-learning detect patterns in data to predict future behavior? Foundations for Cybersecurity and Analytics is an entry-level series of live online short courses to get you started answering these questions and more. No coding experience required.
This flexible modular program enables learners to select one or more individual 3-hour courses to build their skills in specific areas of interest. Students earn a digital Badge/Certificate in “Foundations Certificate in for Cybersecurity & Analytics” upon completion of a minimum of 8 courses within 6 months.
EMPOWER YOURSELF
Update your skills for your current job or transition into a new career
LEARN ANYWHERE
Work-compatible LIVE online instruction
PREPARE FOR THE FUTURE OF WORK
CyberSecurity & Analytics Jobs are in High Demand
Courses
Text Mining
This course will introduce students to the fundamental concepts and techniques used in text mining. It covers essential topics such as natural language processing (NLP), text preprocessing, feature extraction, and text classification. By the end of the course, participants will be equipped with the skills necessary to perform text mining tasks and develop their own text analysis models.
Key Topics
- Introduction to Text Mining and NLP
- Text Preprocessing
- Feature Extraction
- Text Classification
- Clustering and Topic Modeling
- Sentiment Analysis
- Deploying Text Mining Models
After completing this course, participants should be able to:
- Preprocess and clean textual data for analysis
- Extract meaningful features from text data
- Develop and evaluate text classification models
- Implement clustering and topic-modeling techniques
- Perform sentiment analysis on text data
- Deploy text mining models as web services for real-world application
Predictive Analytics – Data Mining & Machine Learning
Machine learning is the use of computer algorithms, or rules, to automatically detect patterns. Data mining takes machine learning a step further and uses those patterns to predict future behavior within the available data. These can be used for data you have previous conceptual knowledge about, or data that is completely new, to discover new patterns.
This course will expose students to the underlying concepts of machine learning pipelines. Key topics include predictive modeling, the split/train/score/evaluate framework, and basic data cleaning and preparation. After completing this module, participants will be able to generate their own machine learning web service which can be called from a Microsoft Excel spreadsheet.
Key Topics
- Predictive modeling
- Split/train/score/evaluate process
- Cleaning missing data
- Deploying machine learning
Specific Objectives After completing this module, participants should:
- Generate their own machine learning pipelines
- Choose the best algorithm for their data
- Choose the best features to predict an outcome
- Create machine learning web services
Cybersecurity Analytics
Course Description
Introduces a data-oriented approach to studying cybersecurity threats and the analysis of large data sets pulled from multiple cybersecurity domains. Illustrates how machine learning may be used to provide insights into logs and other data generated during cyber-attacks. Explores methods to extend anomaly detection using data analytics. The tools discussed allow us to understand cybersecurity threats better and prevent future cyber-attacks.
Key Topics
- Analysis of log data
- Threat hunting
- Indicators of compromise
- Anomaly detection
Specific Objectives After completing this module, participants should:
- Ability to parse large amounts of raw data to locate actionable intelligence
- Understanding of machine learning and the practical use of artificial intelligence
- Knowledge of basic scripting methods
- Familiarity with various types of information systems security event logs
Machine learning for Cybersecurity Analytics
Course Description
This course will cover the fundamentals of applying machine learning to cybersecurity analytics. We will start with an introduction to machine learning. By the end of this module, participants will be equipped to use various machine learning algorithms for cybersecurity analytics.
Key Topics
- Data Preprocessing
- Supervised and Unsupervised Learning Algorithms
- Threat Intelligence and Predictive Analytics
- Privacy and Ethical Considerations
- Case Studies and Practical Applications
Specific objectives after completing this module, participants should be able to
- Data Cleansing
- Be aware of various kinds of Machine Learning algorithms that can be used for cybersecurity analytics
- Be able to perform analytics on various data sets
Sample list of software that will be used:
- Google collab
Cyber Risk and Risk Management Analytics
Course Description
Discusses effective risk management as a critical success factor in modern business. A core component of modern risks are technology and cybersecurity. Proper treatment of cybersecurity risks requires the use of a large amount of diverse data. Machine learning algorithms and automation are helpful in defining repeatable measurements that astute management may use as metrics. This course aids managers in more fully understanding and measuring cyber risks.
Key Topics
- Analysis of business and technology risk
- Quantitative risk assessment methods
- Best use of qualitative risk assessment
- Machine Learning techniques
- Use of Artificial intelligence
Specific Objectives After completing this module, participants should:
- Understanding of machine learning and the practical use of artificial intelligence
- Ability to assess risks and model threats to determine cost-effective controls
- Familiarity with various types of information systems security risks
- Knowledge of quantitative analysis methods as used to support business case analysis for risk mitigation
Business Intelligence, Analytics and Data Science
Now you have the data, let’s transform it into actionable insights to inform business decisions. Learn how to define goals and objectives, and to analyze data ethically and in a socially responsible way.
This course will introduce, and expose students to the fundamentals of business intelligence, business analytics and data science in the era of big data and cloud computing for digital transformation.
Key Topics
- Overview of business intelligence, data analytics and data science
- Competing on data analytics
- How to map goals and objectives to tactics and metrics with data analytics
- Overview of descriptive, diagnostic and predictive analytics
- Career pathways in data analytics
- Future trends in data analytics, e.g. ML, AI, IA, IoT, smart services
Specific Objectives After completing this module, participants should:
- Evaluate the concepts with innovative uses of data, information, knowledge and analytics to support managerial decision-making
- Analyze fundamentals of OLTP vs. OLAP solutions
- Synthesize the directions in which BA is evolving. Which are the cutting-edge practices and solutions (e.g. mobile, social, cloud intelligence) within BA through which competitive advantage can be built?
- Evaluate the foundations of analytics with different levels of analytics, such as graphs, standard reporting, ad hoc reports, score cards, key performance indicators, dashboards, alerts, statistical analysis, forecasting, predictive modeling and data/text mining.
- Recognize and analyze ethical dilemmas and social responsibilities.
Forensics Analytics Case Studies-AI in Cybersecurity and Digital Forensics
This course will introduce the current usage of Artificial Intelligence in Cybersecurity and Digital Forensics/Incident Response (aka: DFIR). ChatGPT will be used to demonstrate several methods of embedded AI into cybersecurity controls and analysis. After completing this module, participants will have an understanding of AI in relation to cybersecurity, and a high-level view of AI prompts and usage of ChatGPT including verifying its results to filter out “hallucinations” and inaccurate output.
- Introduction to AI and Cybersecurity
- AI Technologies in Cybersecurity and Digital Forensics
- Ethical and Practical Challenges
- Case Study 1: Using ChatGPT to Identify Phishing
- Case Study 2: AI-Assisted Network Monitoring
- Future Trends and Course Conclusion
- Have foundational knowledge in usage of AI platforms such as ChatGPT
- Be aware of common misperceptions of AI
- Be able to validate AI results
- Customize prompts to receive specific and accurate answers
- Internet access
- ChatGPT user account (free account for participants)
- Will discuss Python but not necessary for use in the module
Data Visualization
This course will cover the basics of the art and science of data visualization and how to create insightful dashboards using Power BI via a case study. After completing this module, participants should be able to identify and create most appropriate diagrams for a given dataset and business purpose, which contributes to better data exploration, more informed decision making and more compelling communications.
Key Topics
- Best practices and pitfalls
- Graph types
- Power BI capabilities
- Dashboard creation in Power BI
Specific Objectives After completing this module, participants should:
- Utilize data visualization principles and avoid common mistakes
- Be aware of different graph types and identify the most effective one given a business context
- Create and publish Power BI dashboards
- Customize dashboards in Power BI
Sample list of software that will be used:
- Power BI
- Microsoft Excel
Choose your learning journey!
This program offers 2 different options for students to define their learning pathways based on their needs.
1. Pursue our Cybersecurity Certificate Pathway
The Cybersecurity Certificate Pathway has been designed by the Milgard Center for Business Analytics and Master Cybersecurity Faculty at UW Tacoma. Students earn a digital Badge/Certificate in “Foundations Certificate in for Cybersecurity & Analytics” upon completion of a minimum of 8 courses within 8 months.
2. Not interested in a Certificate? Build your skills with one or more courses.
Students can select one or more individual courses to build skills in specific areas of interest.
Program Highlights
100% LIVE Online Instruction
3-hour courses delivered live on weekends and weekday evenings. Review the class recording and practice what you’ve learned at your own pace thereafter.
Virtual Analytics Lab
Use our cutting-edge technology tools and cloud resources to do your course work.
Select a Pre-designed Pathway or Create your Own Pathway
Our certificate program has been designed to develop your skills in applying state of the art analytics to cybersecurity problems.
Earn a Certificate Digital Badge
Complete 8 courses* and earn a shareable and verifiable certificate digital badge to demonstrate your achievement.
Taught by UW Faculty and Industry Professionals
Learn from thought leaders in academia and from accomplished practitioners in industry.
*To earn the Certificate, students must complete a minimum of 8 courses within 6 months. Completion of a course requires a score of 80% or better on the course post-module quiz or assignment. Students will have one week after the course is delivered to complete the post-module quiz or assignment.
** Early Bird Registration Discount is available through June 1, 2024. 2.5% registration fee will be added to the price at the checkout.
Class Schedule
Class
Date
Time
Cybersecurity Analytics
Sunday,
July 28,2024
9:00am-12:20pm
Business Intelligence, Analytics, and Data Science
Sunday,
August 18,2024
9:00am-12:20pm
Cyber Risk and Risk Management Analytics
Sunday,
September 1,2024
9:00am-12:20pm
Data Visualization
Sunday,
September 15,2024
9:00am-12:20pm
Predictive Analytics-Data Mining and Machine Learning
Sunday,
October 20,2024
9:00am-12:20pm
Machine Learning for Cybersecurity Analytics
Sunday,
October 27, 2024
9:00am-12:20pm
Text Mining
Sunday,
November 24, 2024
9:00am-12:20pm
Forensics Analytics Case Studies
Sunday,
December 8, 2024
9:00am-12:20pm
Instructors
Yan Bai
Mark Keith
Pelin Muharremoglu
Daniel Soper
Michael Turek
Brett Shavers
D.C. Grant
Ankur Suri
You can enhance your cyber security skills with a certificate in cyber security analytics.
- Develop knowledge of cybersecurity data analysis tools.
- Learn to apply machine learning and data visualization tools to understand cyber security threats and unwanted behavior.
- Gain an understanding of how business analytics tools can be used in the cyber security professions.
- Get hands-on experience to develop skills via industry specific and open-source Security tools
Developed by UW Tacoma expert faculty and renowned professionals, The Foundation of Cybersecurity Analytics certificate program is designed for professionals who are looking to enhance their analytical skills in cybersecurity. You will understand how to use analytics to identify security vulnerabilities, threats, attacks, and mitigation solutions for modern cyber networks.
The certificate program will help you expand your professional skill set in cyber security analytics to unlock opportunities in new roles and areas as Cyber Security Analyst:
https://www.ziprecruiter.com/Salaries/Cyber-Security-Analyst-Salary-in-Tacoma,WA
https://www.salary.com/research/salary/posting/entry-level-cyber-security-analyst-salary/wa
https://money.usnews.com/careers/best-jobs/information-security-analyst/salary