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Python Security for Data Analysts & Quants training course

Name: Python Security for Data Analysts & Quants training course
Availability: InStock

Python-focused “Security-First Best Practices” course tailored for Quants, Data Analysts and Traders who build small Python apps, scripts, tools, or APIs inside a bank.

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"

Brian Leek, Data Analyst, May 2022

Public Courses

05/01/26 - 1 days

£2250 +VAT

Enrol

16/02/26 - 1 days

£2250 +VAT

Enrol

30/03/26 - 1 days

£2250 +VAT

Enrol

Customised Courses

* Train a team
* Tailor content
* Flex dates

From £1200 / day

Highlights Details Audience Feedback Prices/Dates Related Courses

Public Courses

Highlights

Explore why security is critical when building custom Python tools and how real-world failures happen.
Learn essential Python secure-coding fundamentals to build safer applications from the start.
Understand the big three Python threats—injection, unsafe deserialization, and arbitrary code execution—and how to defend against them.
Apply secure practices for handling, storing, and transferring sensitive data within Python workflows.
Utilise proper secrets management and modern authentication methods to eliminate risky hard-coding.
Evaluate dependency risks, environment isolation, and deployment security to avoid supply-chain and packaging threats.
Enhance your safety posture in Jupyter and other interactive environments by following notebook-specific security best practices.
Review a practical secure-coding checklist to reinforce learning and guide future Python development.

Course Details

Module 1 — Why Security Matters for Desk-Built Python Tools

Key Topics:

How small quant/analyst scripts create enterprise-level exposure
Model theft and Model IP protection; Market data and API security including rate limiting
Real examples of spreadsheet/Python notebook breaches
Data classification: market data, client data, PII, trading logic
Regulatory context (e.g., GDPR, FCA, internal audit requirements)
MiFID II record-keeping requirements, EU DORA ICT Risk Management requirements.
“Shadow IT” and why Python tools often bypass security controls
Takeaway
Learners understand why security applies to them, not just developers.

Module 2 — Python Secure Coding Fundamentals

Key Topics:

The Least Privilege Principle in code & data access
Input validation for parameters, files, CSV uploads, API inputs
Safe error handling and avoiding information leakage
Creating safe defaults (timeouts, retries, limited file access)
Logging without exposing sensitive data
Practical Red Flags
Printing credentials during debugging
Exposing stack traces to internal users
Using “temporary” shortcuts that become permanent in production

Module 3 — The Big Three Python Threats: Injection, Deserialization, Execution

1. Injection Attacks
SQL injection through string formatting
Safe use of parameterized queries
Injection through Pandas query() or notebook widgets
2. Deserialization Risks
pickle / joblib loading untrusted data
Malicious objects executing on load
Safer alternatives (json, yaml.safe_load, protobuf)
3. Arbitrary Code Execution
Why eval, exec, and dynamic imports are dangerous
Unsafe shelling-out (os.system, subprocess.Popen)
How attackers can escalate through seemingly harmless inputs
Hands-On Exercise
Fixing vulnerabilities in a short Python example script.

Module 4 — Protecting Data: Secure Handling, Storage, and Movement

Key Topics
Handling of client data, trade data, PII
Preventing data leakage through logs, temp files, notebooks
Encryption in transit: using HTTPS, certificates
Avoiding local file dumps and shared drive exposure
Sanitizing output before sending to business users or clients
Scenario Discussion
What happens if a quant tool produces a CSV with unintended sensitive columns?

Module 5 — Secrets Management & Authentication

Key Topics
Never hardcoding credentials (API keys, DB passwords)
Secure use of environment variables
Using the bank’s secret manager or credential vault
Key rotation & lifecycle
Risks of storing secrets in notebooks or git repos
Practical Red Flags
Jupyter notebooks uploaded with visible credentials
Python scripts emailed with embedded tokens

Module 6 — Dependencies, Environments & Deployment Risks

Key Topics
Why dependency risks matter even for small Python tools
Using internal package repositories only
Pinning versions (requirements.txt, pip freeze)
Known vulnerabilities (CVE scanning)
Conda/venv isolation
Risks of running untrusted open-source packages
Practical Example
A popular ML library compromise — how supply-chain vulnerabilities arise.
Supply Chain Security, Typo squatting attacks and vetting third party libraries.

Module 7 — Jupyter Notebook & Interactive Tool Security

Key Topics
Notebooks as attack surfaces
Removing sensitive output before sharing
Preventing user-supplied code execution in widgets
Converting notebooks safely to HTML/PDF
Output caching and hidden cell risks
Red Flags
Notebooks stored on shared drives with embedded datasets
“Accidental” model dumps containing client identifiers
Module 8 — Lightweight Threat Modeling for Python Tools (30 min)
Key Topics
Identifying what your code connects to (data sources & systems)
Mapping trust boundaries
Asking “What could go wrong?” before releasing a tool
Quick 5-question security check for every script/app

Module 9 — Secure Coding Checklist & Final Review

Learners receive a Python-specific, desk-friendly checklist:
Am I accepting untrusted input?
Am I using any dynamic execution (eval/exec)?
Are secrets exposed anywhere?
Does the script access more data than needed?
Are dependencies safe and version-pinned?
Could logs or errors leak sensitive information?
Have I documented known assumptions and risks?
OPTIONAL: Hands-On Assessment (20–30 min)
A short secure-code review exercise using a flawed Python script.

Who should attend

Python Developers
Software Engineers
Security Professionals (SecDevOps)
Software Architects
Data Scientists & Machine Learning Engineers
Quality Assurance Engineers
Developers Transitioning to Security
Ethical Hackers and Penetration Testers
Project Managers and Product Owners

Feedback

4.8 out of 5 average

Brian Leek, Data Analyst, May 2022

“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

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More about this course

This course provides a practical introduction to why security is essential when building custom Python tools, supported by real-world examples of what can go wrong. You’ll learn core Python secure-coding fundamentals and gain a clear understanding of major threats such as injection, unsafe deserialization, and arbitrary code execution. Through hands-on guidance, you’ll apply best practices for handling, storing, and moving sensitive data safely within Python workflows.

The course also covers modern secrets management and authentication approaches that eliminate dangerous hard-coded credentials. You’ll evaluate dependency and environment risks to avoid supply-chain vulnerabilities and strengthen deployment security. Additional modules focus on staying safe within Jupyter and other interactive environments. Finally, you’ll leave with a practical secure-coding checklist to support ongoing, secure Python development.

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CONTACT
+44 (0)20 8446 7555

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