Data Science for Managers

Learn to identify problems solvable through data science, hire and manage data science teams, and build supporting infrastructure.

Overview

According to McKinsey, the United States faces a shortage of 1.5 million managers and analysts with the skills to understand and make decisions based on big data analysis. This course addresses that gap directly.

Data Science for Managers is a 16-hour course designed for organizational leaders who need to understand data science well enough to make hiring decisions, manage projects, allocate resources, and evaluate results, without needing to write production code themselves.

50% of class time is instructor-led. The other 50% is practical exercises that give managers hands-on exposure to what their teams do.

What You Will Learn

  • Read data in various common formats and create scripts for basic analysis and visualization
  • Identify which business and security problems are solvable through data science techniques
  • Hire and retain qualified data science professionals
  • Manage data science projects effectively (timelines, deliverables, evaluation criteria)
  • Build the infrastructure and organizational support data science teams need to succeed

Who This Is For

Managers, directors, VPs, and executives who lead or oversee teams that work with data. You do not need a technical background. This course is specifically designed for leaders who need to understand data science at a strategic level.

Topics covered

  • Reading data in common formats and creating analysis scripts
  • Identifying problems solvable through data science
  • Hiring and retaining data science teams
  • Managing data science projects
  • Building infrastructure to support data science initiatives
  • Understanding data-driven decision making

Frequently Asked Questions

How is Data Science for Managers different from a technical data science course?
Technical courses teach managers' teams how to write Pandas code and train models. This course teaches managers how to scope a data science problem, hire and retain practitioners, manage projects with non-deterministic deliverables, and evaluate results. The hands-on portion exists so managers understand what their teams are doing, not so they can do it themselves.
What should I look for when hiring a data scientist for a security team?
Strong programming fundamentals (Python at minimum, SQL useful), familiarity with applied ML (not just theory), and the ability to communicate findings to non-technical stakeholders. Security domain knowledge matters less than analytical rigor; a strong data scientist can learn the security side faster than a security analyst can learn modeling. Watch for candidates who insist on deep learning for problems a logistic regression would solve.
What infrastructure does a small data science team need to be effective?
A shared compute environment (cloud notebooks, JupyterHub, or managed services like Databricks), a queryable data layer that aggregates the source systems analysts care about, version control for code (Git), and a path to deploy models to production. Most teams overinvest in tooling early. Start lean and add infrastructure when a specific bottleneck appears.
How long does a typical data science project take in a security organization?
Discovery and data exploration usually take 2 to 4 weeks. A first working model takes another 2 to 6 weeks. Deployment, monitoring, and iteration extend the timeline indefinitely. Plan for the model to be wrong on first deployment and budget for iteration. Projects that promise production-ready results in two weeks are oversold.
Should I require a PhD for data scientists I hire?
Usually not. PhD training is valuable for original research and novel modeling work. Most applied data science in security is well-served by candidates with strong programming, statistics, and business judgment from a Master's or Bachelor's path with relevant experience. Hiring exclusively from PhD pools shrinks the candidate pool unnecessarily.

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