IT Switch Foundation:
- Digital tools and spreadsheet confidence
- SQL and basic data handling
- Charts, KPIs and business questions
- Research and source evaluation
- Clear written and verbal communication
- Portfolio and networking habits

Technical Start:
- Python variables, loops and functions
- SQL filters, joins and aggregations
- Read a CSV and create a basic chart
- Understand APIs at a beginner level
- Version control with Git and GitHub

Path Comparison:
- Data Analytics: Excel | SQL | Power BI | business insights
- Data Science: Python | statistics | ML | experiments
- AI: LLMs | APIs | RAG | automation | deployment
- Common: communication | projects | GitHub | interviews

Non-IT Learning Order:
1. Digital basics and data literacy
2. Excel, SQL and dashboards
3. Python and statistics if choosing Data Science
4. ML or LLM applications for specialization
5. Build projects and document outcomes
6. Apply, network and keep improving

Every Career Switch Project Should Include:
1. Business question and dataset source
2. Data cleaning steps
3. Tool or workflow used
4. Insights, metrics or time saved
5. Screenshots, README and limitations
6. Business impact and next steps

Career Switch Portfolio Checklist:
- One dashboard or reporting project
- One Python or Data Science case study
- One AI research or automation workflow
- One domain project from your previous career
- GitHub or shareable case-study folder
- One-page resume with measurable impact

Non-IT to IT Learning Timeline:

Months 1-2: Digital Foundation
- Spreadsheets, data literacy and research
- Understand your target IT role
- Build a simple reporting project

Months 3-5: Analytics Entry Path
- SQL, dashboards and business cases
- Build two portfolio projects
- Practice interview fundamentals

Months 6-10: Choose a Specialization
- Python and statistics for Data Science
- AI tools, prompts and automation for AI
- Build one specialization project

Months 11-15: Job Switch
- Resume, portfolio and mock interviews
- Apply to internships and junior roles
- Network with practitioners and recruiters

Job Preparation Guide:

Target Roles:
Data Analyst | Data Scientist Intern | AI Operations | Automation Associate

Resume Keywords:
Excel | SQL | Power BI | Python | Statistics | AI Tools | Automation | Projects

Interview Topics:
SQL | Dashboards | Business cases | Python basics | AI scenarios | Projects

Portfolio Proof:
Dashboard | Case study | AI workflow | README | Business impact

Helpful Certifications:
- Data Analytics or AI fundamentals certification
- Domain-related capstone or open-source contribution
- Practical SQL, Python and automation projects

Non-IT to IT Job Search Plan:

Months 1-5: Analytics Foundation
- Excel, SQL, dashboards and business cases
- Build two portfolio projects
- Document insights and outcomes

Months 6-10: Specialization
- Python, ML or AI tools based on target role
- Build and explain one specialist project
- Publish portfolio and case study

Months 11-15: Applications
- Resume, portfolio and mock interviews
- Apply to internships and junior roles
- Network with IT practitioners

Target outcomes:
- Three documented career-switch projects
- One analytics or AI specialization project
- Clear explanation of previous and new skills

Recommended Resources:

Free Resources:
- Python documentation and practice notebooks
- Kaggle – Datasets and notebooks
- Scikit-learn user guide
- SQL practice datasets
- YouTube – Statistics and ML channels

Paid Resources:
- Uncodemy – AI and Python Engineering Training
- DataCamp – Analytics tracks
- Coursera – Specializations
- LinkedIn Learning – BI courses

Certifications (Recommended):
- Cloud, Python and AI engineering certification
- AI tools and no-code workflow practice