The Knowledge House · Innovation Fellowship
Fellowship Prep Guide
Welcome! This page is for fellows in The Knowledge House's Innovation Fellowship — whether you're in the AI Business Solutions, Data Analytics, or Cybersecurity track. The goal is simple: get comfortable with the tools you'll be using before class starts, so day one is about learning the material, not fighting with installs and sign-ups. Nobody expects you to master any of this in advance — just create the accounts and install the software so it's one less thing to figure out in week one.
Set these up before orientation, no matter your track
Show toolsHide
These tools show up in more than one track from the very first week, so everyone should have them ready.
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| Python | The core programming language used throughout both the AI Business Solutions and Data Analytics tracks, starting week one. | Free | |
| Git & GitHub | Version control — you'll push, pull, and commit your project work to GitHub throughout the fellowship. Create a free GitHub account now if you don't have one. | Free | |
| Visual Studio Code | The code editor used in the fellowship's programming environment. | Free | |
| A Google account | Used for Google Sheets (data visualization) and, in some tracks, project submission. | Free | |
| Coursera account | The Data Analytics track assigns weekly Coursera modules and quizzes as homework starting week one — create a free account now. | Freemium Free to enroll; some course content may require a paid Coursera plan depending on what your instructor assigns. |
AI Business Solutions track
Show toolsHide
In Phase 1 (Feb–May) you'll build foundational programming, data, and interface skills; in Phase 2 (June–Aug) you'll go deeper into machine learning, natural language processing, and AI agents, building toward a capstone that combines retrieval-augmented generation (RAG) with a fine-tuned model.
Phase 1 tools
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| NumPy | Python library for numerical computing, used throughout the data and machine learning coursework. | Free | |
| Pandas | Python library for working with structured datasets — you'll use this constantly for data handling. | Free | |
| Figma | Where you'll design user-centered interfaces before building them. | Freemium Free tier available. | |
| Lovable | You'll use this to turn Figma designs into working frontend webpages, and later to visualize data. | Freemium Free tier available. | |
| SQLTools (VS Code extension) | A SQL client inside VS Code for practicing SELECT/INSERT/UPDATE/DELETE queries and database seeding. | Free | |
| Pytest | Python's standard testing framework — used when you package applications and write unit tests. | Free | |
| Pydantic | Used for creating structured data models, covered when you build customer-interaction data models. | Free | |
| scikit-learn | Your introduction to building simple machine learning models in Python. | Free |
Phase 2 tools (deep learning, NLP & AI agents)
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| Keras | Used to build sequential neural network models, starting with a digit-classification project. | Free | |
| NLTK | One of three libraries used to learn tokenization — how computers break text into processable units. | Free | |
| spaCy | The second tokenization library you'll work with. | Free | |
| Hugging Face | The third tokenization library, and your entry point into pretrained NLP models generally. | Freemium Free tier available. | |
| FAISS | The vector database you'll implement for semantic search over documents — core to your RAG capstone. | Free | |
| LangChain | The framework you'll use to build AI agent workflows — chaining prompts, tools, and memory together. | Freemium Free tier available. |
Data Analytics track
Show toolsHide
In Phase 1 (Feb–May) you'll build a programming and statistics foundation using Python, Git, and Pandas; in Phase 2 (June–Aug) you'll move into SQL at scale, dashboarding, and a full progression through regression, classification, ensemble methods, and neural networks.
Phase 1 tools
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| Anaconda | Manages your Python environment and packages — set this up in week two of the program. | Free | |
| Jupyter Notebook | Where you'll write and run a lot of your early Python and data analysis work. | Free | |
| Replit | An in-browser alternative for running Python, introduced alongside Jupyter early on. | Freemium Free tier available. | |
| NumPy | Numerical computing fundamentals used throughout the data analysis coursework. | Free | |
| Pandas | Central to the track — indexing, dataframe manipulation, group analysis, and time-series work all build on this. | Free | |
| Requests (Python library) | Used for working with REST APIs and unstructured data. | Free | |
| Google Sheets | Used for multivariate data analysis exercises. | Free |
Phase 2 tools
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| Google BigQuery | You'll query a real Amazon retail dataset through BigQuery as a graded project. | Freemium Free tier available (BigQuery sandbox), paid usage-based tiers beyond that. | |
| Tableau | Where you'll build reporting and monitoring dashboards using business-intelligence best practices. | Paid only Free trial; paid product (student/education licensing may be available — worth checking with your instructor). | |
| scikit-learn | Used across the regression, classification, and ensemble-method curriculum (logistic regression, decision trees, KNN, Naive Bayes, Random Forest). | Free | |
| XGBoost | Covered specifically during the ensemble-methods unit. | Free | |
| Keras | Introduced for neural networks, multi-layer perceptrons, and an MNIST handwritten-digit recognition project. | Free | |
| GitHub Codespaces | Your Phase 2 development environment runs through this — a cloud-based VS Code environment tied to your GitHub repo. | Freemium Free tier included with GitHub account; paid usage beyond free monthly hours. |
Cybersecurity track
Show toolsHide
Organized by the program's five stated focus areas.
About this list
A full syllabus for this track wasn't available when this page was built. The tools below are drawn from the program's stated focus areas. Where a specific tool was named, it's listed directly; where only a skill area was given (marked with an asterisk), the tool shown is a commonly used example for that area, not a confirmed course requirement — check with your instructor once you have the real syllabus.
System & Network Security
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| A Linux distribution (e.g. Ubuntu) | System administration fundamentals start here. | Free | |
| Wireshark* | The standard tool for TCP/IP traffic analysis. | Free | |
| pfSense* | An example open-source firewall platform for practicing defense-in-depth strategy. | Free | |
| AWS VPC | Where you'll practice subnetting and secure network architecture. | Freemium Free tier available. |
Offensive Security & Defense
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| OSINT Framework | A directory of open-source intelligence reconnaissance resources. | Free | |
| Nmap | Network scanning and reconnaissance. | Free | |
| CVE Program | The official database for tracking known vulnerabilities. | Free | |
| Metasploit | Vulnerability assessment and penetration testing framework. | Freemium Free (community edition); paid Pro tier available. | |
| Snort* | An example open-source intrusion detection system for active-defense practice. | Free | |
| Autopsy | Digital forensics platform for investigating what happened on a system. | Free | |
| Volatility | Memory forensics framework. | Free |
Cloud & Infrastructure Hardening
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| AWS (IAM, EC2, S3, RDS) | Core AWS services you'll work with for cloud architecture. | Freemium Free tier available. | |
| Terraform | Infrastructure as Code — defining and provisioning infrastructure through configuration files. | Freemium Free (open-source); paid tiers for team/enterprise features. | |
| Docker | Containerization fundamentals. | Freemium Free tier available. | |
| Amazon EKS | Managed Kubernetes, where you'll apply container security and RBAC. | Paid only Paid (usage-based). | |
| Kubernetes RBAC documentation | Reference for role-based access control concepts used with EKS. | Free |
Automation & DevSecOps
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| Python | Used for security-auditing scripts throughout this focus area. | Free | |
| GitHub Actions* | An example CI/CD platform for practicing secure pipelines with SAST/DAST gates. | Freemium Free tier included with GitHub account. | |
| Trivy* | An example open-source container-scanning tool. | Free |
Emerging Threats & AI Security
| Tool | Why you need it | Link | Cost |
|---|---|---|---|
| OWASP Top 10 for LLM Applications | The standard reference for AI/LLM security risks, including prompt injection — the core resource for this focus area. | Free |
Other Free Training Programs Worth Knowing About
The Knowledge House isn't the only organization doing this work. Here are a few other tuition-free programs worth knowing about, whether you're looking for something to pair with your fellowship, a backup option, or where to point a friend.
- Cybersecurity
Path2Tech: Cybersecurity
NPower
Part-time online security training for people with some tech background, using hands-on security tools and preparing for CompTIA Security+ and Linux+.
- Format
- About 5 months, part-time, online
- Cost
- Free
- Eligibility
- 1–2 years of tech experience or NPower alumni status; high school diploma; US work authorization; age 18+.
- Locations
- Baltimore, Dayton, Detroit, Dallas-Fort Worth, Harris County, San Antonio, Los Angeles, Sacramento, SF Bay Area, San Jose, Newark, NYC, St. Louis, Raleigh
- Any track (pre-fellowship foundation)
Tech Fundamentals
NPower
Foundational IT training covering Microsoft, Cisco, AWS, and core hardware and software, preparing for CompTIA A+/Tech+ and the Google IT Support Certificate.
- Format
- Up to 20 weeks, virtual, instructor-led
- Cost
- Free
- Eligibility
- Ages 18–26 (or veterans and spouses 21+); high school diploma; first-time NPower students; within 2–3 hours of a program location.
- Locations
- Baltimore, Brooklyn/NYC, Dallas, San Antonio, Houston, Detroit, St. Louis, Kansas City, Raleigh, Dayton
- Data Analytics
Data Analytics
NPower
IT fundamentals combined with hands-on data work, preparing for Microsoft Power BI (PL-300), Tableau Desktop Specialist, and Microsoft Excel Expert certifications.
- Format
- 19 weeks, virtual
- Cost
- Free
- Eligibility
- Ages 18–26 (or veterans of any age); high school diploma; basic computer skills; US work authorization.
- Locations
- Michigan and Ohio only
- AI Business Solutions
AI Prompt + Power Automation Program
NPower
Prompt engineering, Microsoft 365 Copilot, Power Automate, and AI ethics, ending in a portfolio capstone on a real business problem; prepares for AB-900, PL-900, and Google AI Essentials.
- Format
- 20 weeks, virtual, 120 hours (Mon/Wed/Thu evenings)
- Cost
- Free
- Eligibility
- Age 21+; high school diploma; 1–3 years of professional experience in any field; US work authorization.
- Locations
- New York/New Jersey, Washington, D.C., Dallas, Austin, Atlanta
- Data AnalyticsCollege grads
COOP Careers Fellowship
COOP Careers
In-person cohorts of 10–16 covering Excel, Google Analytics, SQL, and programmatic advertising, plus networking and job-search prep, with a capstone in digital marketing, data analytics, or financial services.
- Format
- 16 weeks, 200 hours, in person; alumni coaching for at least a year
- Cost
- Free
- Eligibility
- Built for underemployed college graduates.
- Locations
- New York, California, Miami, Chicago (spring and fall cohorts)
COOP reports that 3 of 4 alumni are fully employed within 12 months, averaging $50K a year (organization-reported figure).