Foundation Skills for Data Careers:
- Excel and spreadsheet analysis
- SQL queries, joins and aggregations
- Python syntax and data structures
- Descriptive statistics and probability
- Data cleaning and documentation
- Clear written and verbal communication

SQL Basics for Analytics:
- SELECT, FROM, WHERE
- JOINs (INNER, LEFT, RIGHT)
- GROUP BY and HAVING
- ORDER BY
- Aggregate functions (SUM, AVG, COUNT)
- Subqueries
- CTEs (Common Table Expressions)
- Window functions
- Date/time functions
- String functions

Data Analyst Skills:
- SQL and dashboard design
- KPI definitions and data storytelling
- Power BI or Tableau
- Basic Python and statistics
- Stakeholder communication
- Portfolio case studies

Business Analyst Skills:
- Requirements gathering
- Process mapping and documentation
- User stories and acceptance criteria
- Stakeholder presentations
- Metrics and impact measurement
- Domain knowledge

AI and Machine Learning Foundations:
- Pandas – Data manipulation
- NumPy – Numerical computing
- Matplotlib / Seaborn – Visualization
- Scikit-learn – Machine learning
- Statsmodels – Statistical modeling
- XGBoost – Advanced ML
- TensorFlow / PyTorch – Deep learning
- SQLAlchemy – Database connectivity
- Plotly – Interactive dashboards
- Streamlit – Web apps for analytics

GenAI Engineering Skills:
1. Azure Machine Learning – End-to-end ML lifecycle
2. Google Cloud AI – AutoML, Vertex AI
3. AWS SageMaker – Build, train, deploy
4. DataRobot – Automated ML
5. H2O.ai – Open-source ML
6. Alteryx – Analytics automation
7. KNIME – Data science workflow
8. RapidMiner – No-code ML

Career Growth Timeline:

Months 0-3: Foundation Skills
- Excel, SQL, Power Query
- Build 3-5 dashboard projects
- Apply for junior analyst roles

Months 3-6: Career Specialization
- Power BI or Tableau
- Build complex dashboards and reports
- Lead small analytics projects

Months 6-12: AI and Machine Learning
- Python, R, AI/ML platforms
- Build predictive models
- Mentor junior analysts

Year 5-8: Leadership
- Cloud platforms (Azure, AWS, GCP)
- Strategy and team management
- Drive data initiatives

Skills Roadmap:

Level 1 (Entry):
Excel | SQL | Power Query | Data Cleaning

Level 2 (Mid):
Power BI | Tableau | DAX | Data Modeling

Level 3 (Senior):
Python | Pandas | Scikit-learn | Statistical Analysis

Level 4 (Leader):
Cloud (Azure/AWS/GCP) | AI/ML | Data Strategy | Leadership

Recommended Certifications:
- Microsoft PL-300 (Power BI)
- Google Data Analytics Professional
- Microsoft Azure Data Scientist

12-Month AI and Data Career Plan:

Month 1: Excel Mastery
- Formulas, pivot tables, charts
- Data cleaning and formatting
- What-if analysis

Month 2: SQL Fundamentals
- SELECT, JOINs, GROUP BY
- Subqueries and CTEs
- Practice on real datasets

Month 3: Power BI or Tableau
- Connect to data sources
- Build dashboards and reports
- Publish and share

Month 4: Projects
- Build 3 portfolio dashboards
- Use real-world datasets
- Document and share on GitHub

Month 5: Python (if applicable)
- Pandas for data manipulation
- Matplotlib for visualization
- Basic machine learning

Month 6: Job Applications
- Resume and portfolio
- Interview preparation
- Networking and applications

Recommended Resources:

Free Resources:
- Microsoft Learn – Power BI
- Google Data Analytics (Coursera)
- W3Schools – SQL and Excel
- Kaggle – Datasets and notebooks
- YouTube – Analytics channels

Paid Resources:
- Uncodemy – Business Analytics Course
- DataCamp – Analytics tracks
- Coursera – Specializations
- LinkedIn Learning – BI courses

Certifications (Recommended):
- Microsoft PL-300 (Power BI)
- Tableau Desktop Specialist
- Google Data Analytics Professional