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 Science Core Skills:
- Python, NumPy and Pandas
- SQL and database fundamentals
- Probability and descriptive statistics
- Data visualization with Matplotlib or Seaborn
- Scikit-learn and model evaluation
- Git, notebooks and documentation

Recommended Learning Order:
1. Python programming fundamentals
2. SQL and data cleaning
3. Statistics and probability
4. Exploratory data analysis
5. Machine learning algorithms
6. Deployment and communication

Every Data Science Project Should Include:
1. Business question and dataset source
2. Data cleaning steps
3. Exploratory data analysis and visualizations
4. Baseline model and selected algorithm
5. Metrics, error analysis and limitations
6. Business recommendations and next steps

Data Science Portfolio Checklist:
- One complete Python EDA project
- One supervised ML project
- One clustering or segmentation project
- Reproducible notebook and README
- Model metrics with honest limitations
- One-page resume with project impact

Data Science Learning Timeline:

Months 1-3: Python and Mathematics
- Python syntax, functions and OOP basics
- Algebra, probability and descriptive statistics
- Solve small coding exercises

Months 4-6: Data Analysis
- SQL, Pandas and data cleaning
- EDA with charts and written insights
- Complete one reproducible notebook

Months 7-10: Machine Learning
- Regression, classification and clustering
- Train-test split, cross-validation and metrics
- Compare baseline and improved models

Months 11-15: Portfolio and Jobs
- Build three end-to-end projects
- Write README files and explain limitations
- Prepare resume, interviews and applications

Job Preparation Guide:

Target Roles:
Junior Data Scientist | ML Intern | Data Analyst | Research Analyst

Resume Keywords:
Python | SQL | Pandas | Statistics | Scikit-learn | EDA | ML

Interview Topics:
Probability | Bias-variance | Metrics | SQL | Feature engineering | Projects

Portfolio Proof:
Three projects | Reproducible notebooks | Model evaluation | Clear README

Helpful Certifications:
- Google Advanced Data Analytics Professional Certificate
- Practical Python and machine learning projects

Data Science Job Search Plan:

Months 1-6: Foundations
- Python, SQL and statistics
- Practice on public datasets
- Document learning publicly

Months 7-10: Machine Learning
- Build and compare baseline models
- Evaluate metrics and errors
- Write project explanations

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

Target outcomes:
- Three documented projects
- One deployed or reproducible model
- Clear explanation of limitations

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
- Python and machine learning project practice