level-up-together

This is primarily targeted at Masters of IT in Business (MITB) Singapore Management University (SMU) students audience whom we have divided into 2 personas.

One big source of inspiration for this is The Missing Semester of Your CS Education by MIT students.

Tech Transition Student Personas

• Code-Curious Casey
• AI Aspiring Alex

We choose the names Casey and Alex because they are the most gender neutral while still conforming to alliteration.

The names "Code-Curious Casey" and "AI-Aspiring Alex" are designed to be memorable while highlighting their key characteristics:

• Casey represents the careful but determined career-changer who needs confidence-building and clear pathways
• Alex represents the technically confident professional who needs to bridge specific knowledge gaps

1. "Code-Curious Casey"

The Career Pivoting Professional

Background

• Current role in non-tech field (e.g., marketing, finance, sciences, or education)
• Has basic computer literacy but no formal programming experience
• Windows user with limited exposure to development environments (or Mac user but never used the terminal)
• Masters program is their first step into formal tech education

Technical Environment Challenges

• Little to zero experience with coding
• Unfamiliar with command line/shell operations
• Never used version control systems like Git
• Limited understanding of IDE features and benefits
• No exposure to CI/CD concepts or tools
• Primarily used GUI-based applications for work
• No idea what MLOps workflow is about

Key Attributes

• Motivated but intimidated by technical concepts
• Strong soft skills from previous career
• Eager to combine existing expertise with new technical skills
• Concerned about competing with CS graduates
• Values practical, hands-on learning experiences

Typical Questions

• "What's the difference between Command Prompt and PowerShell?"
• "What's the difference between Linux and Windows operating systems?"
• "Why can't I just download code files manually instead of using Git?"
• "How do I set up my computer for coding?"
• "What does IDE actually mean and why do I need one?"
• "How do professional developers organize their work?"
• "What skills am I lacking?"
• "Where do I start?"
• "What resources are there for me?"

Dream Roles

• Business Analyst with coding responsibilities
• Technical Product Manager
• Low-code/No-code Developer
• Solutions Consultant
• Technical Customer Success Manager
• Engineer/scientist using AI/ML tools (e.g., research scientist, manufacturing engineer, data scientist)

2. "AI-Aspiring Alex"

The Tech Professional Seeking ML Evolution

Background

• 3-5 years in software development or related tech role
• Solid programming fundamentals (typically in Python/Java)
• Strong understanding of software development lifecycle
• Currently working on traditional web/mobile applications
• Seeking to pivot into AI/ML/DE space

Community & Learning Focus

• Actively seeking AI/ML learning communities and study groups
• Wants to contribute to open-source ML projects
• Looking for mentorship opportunities
• Interested in attending AI/ML conferences and meetups
• Desires to build a network in the AI/ML space

Key Attributes

• Confident with general programming concepts
• Concerned about keeping up with rapid AI advancements
• Wants to stay current with latest frameworks and tools
• Values peer learning and knowledge sharing
• Seeks balance between depth and breadth in AI/ML knowledge

Typical Questions

• "How do I stay updated with the latest AI research and tools?"
• "Which AI communities are most welcoming to newcomers?"
• "How can I find study partners for ML projects?"
• "What conferences or meetups should I prioritize?"
• "How do I balance learning fundamentals vs. new tools?"
• "What's a good standard ML Lifecycle process to adopt?" See https://ml-ops.org/content/crisp-ml for possible answer

Dream Roles

• Machine Learning Engineer
• MLOps Engineer
• Data Engineer
• AI Application Developer
• ML Platform Engineer

Learning Priorities

• Building learning roadmap for emerging AI technologies
• Finding reliable sources for staying updated
• Connecting with AI practitioners and researchers
• Contributing to AI open source projects
• Participating in AI hackathons and competitions

Learning Paths

Learning Path for Code-Curious Casey

Taking inspiration from The Missing Semester of Your CS Education

We cover breadth but leave it to your individual selves to go in-depth via Udemy courses and YouTube videos.

• Shell (PowerShell / xMobTerm)
• Command Line (SSH, file operations)
• Version Control (Git & GitHub)
• Video recording
• Docker and VMs (Cloud)
• IDE (VS Code)
• Database GUI Tools
• Continuous Integration / Continuous Delivery (CI/CD)
• Debugging and Profiling
• Markdown
• Markup Languages (JSON, YAML)
• APIs
• Using LLM to learn
• Diagram Drawing (Mermaid)

Why These Topics For Casey

Mostly for two reasons:

1. They are career show-stoppers
2. They are level-up blockers

Career Show-stoppers: By career show-stoppers, we mean if you want a code-facing tech job and you know nothing of this skill, hiring managers will use this to filter you out.

How much to know? Enough basics to get by.

Level-up Blockers: By level-up blockers, we assume you want to be AI-Aspiring Alex eventually, so not knowing this skill may prevent you from going to that level.

How much to know? Again, enough to get by.

Learning Path for AI-Aspiring Alex

The learning path for AI-Aspiring Alex is more speculative than the learning path for Code-Curious Casey.

This is to be expected as you get closer to the cutting edge where sometimes things don't work out.

On the plus side, that means more room for you to contribute back and stand out.

• Hammerspoom (Desktop automation in macOS)
• LaTex
• Local LLM (Ollama)