Foundation Skills for Humanities Students:
- Data literacy and source evaluation
- Excel and spreadsheet analysis
- Research, storytelling and editing
- Charts and simple dashboards
- AI literacy and responsible use
- Clear client communication

Optional Technical Start:
- SQL filters, sorting and simple joins
- Read a CSV and create a basic chart
- Use a dashboard tool for reports
- Python variables and simple functions
- Learn APIs after workflow basics

Analytics and AI Skills:
- Excel, charts and dashboard basics
- SQL filters and simple joins
- Research data and source evaluation
- Prompt engineering and context design
- AI-assisted writing and editing
- Ethics, privacy and human review

Humanities Learning Order:
1. Understand data and source quality
2. Learn Excel and visual summaries
3. Practice SQL and dashboard tools
4. Build three useful case studies
5. Add AI prompts and automation
6. Learn basic Python when needed

Every Humanities Data or AI Project Should Include:
1. Business question and dataset source
2. Data cleaning steps
3. Data source and cleaning steps
4. Charts, insights or prompts used
5. Human review and fact-checking steps
6. Clear case study and recommendations

Humanities Portfolio Checklist:
- One survey or public-data dashboard
- One social research case study
- One AI content or research workflow
- One education or community project
- Screenshots, queries and quality checks
- One-page resume with measurable impact

Humanities Data and AI Learning Timeline:

Months 1-2: Data Literacy
- Understand tables, charts, averages and sources
- Learn Excel summaries and research basics
- Practice privacy and fact checking

Months 3-5: Analytics Basics
- Learn SQL filters and dashboard tools
- Build one survey or public-data case study
- Document insights and recommendations

Months 6-8: AI Workflows
- Build research, content and reporting workflows
- Document prompts and quality checks
- Publish a repeatable AI case study

Months 9-12: Specialization and Jobs
- Choose content, research, education or operations
- Build three portfolio case studies
- Prepare resume, interviews and applications

Job Preparation Guide:

Target Roles:
Junior Data Analyst | Research Analyst | AI Operations Associate | Content Analyst

Resume Keywords:
Excel | SQL | Data Visualization | Research | AI Tools | Prompt Engineering | Communication

Interview Topics:
Charts | SQL basics | Source quality | AI limitations | Research | Communication | Projects

Portfolio Proof:
Dashboard | Research report | AI workflow | Screenshots | Recommendations

Helpful Certifications:
- Data analytics or AI fundamentals certification
- Research, communication or design portfolio
- Optional Python and automation projects

Humanities Data and AI Job Search Plan:

Months 1-5: Analytics Foundation
- Excel, data literacy and dashboards
- Build one public-data case study
- Document insights and recommendations

Months 6-8: AI Skills
- AI tools, prompts and simple automation
- Learn basic Python or SQL if useful
- Publish one practical case study

Months 9-12: Applications
- Resume, portfolio and mock interviews
- Apply to analytics, research and AI operations roles
- Network with data and AI practitioners

Target outcomes:
- Three documented data/AI case studies
- One dashboard and one reusable AI workflow
- Clear explanation of insight and impact

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 – Data Analytics Training Course
- DataCamp – Analytics tracks
- Coursera – Specializations
- LinkedIn Learning – BI courses

Certifications (Recommended):
- Google Advanced Data Analytics Professional Certificate
- AI tools and no-code workflow practice