[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL]

PROFESSIONAL SUMMARY
Data Analyst transitioning to Data Science with [X] years of experience in data analysis, statistical modeling, and business intelligence. Completed [number] machine learning projects demonstrating ability to build predictive models. Strong foundation in SQL, Python, and data visualization with proven ability to drive business decisions through data.

TECHNICAL SKILLS
Programming: Python, R, SQL
Machine Learning: Scikit-learn, TensorFlow, Pandas, NumPy, Statsmodels
Data Analysis: Excel, SQL Server, Statistical Analysis, A/B Testing
Visualization: Tableau, Power BI, Matplotlib, Seaborn
Tools: Jupyter Notebook, Git, Google Analytics, Looker

PROFESSIONAL EXPERIENCE

Data Analyst
[Current Company Name], [City, State]
[Start Date] – Present
• Analyze [type of data] supporting [X] business teams and driving [Y] strategic decisions monthly
• Built [number] dashboards in Tableau tracking KPIs and reducing reporting time by [X%]
• Developed SQL queries processing [amount] of data to generate insights on [business areas]
• Conducted A/B tests analyzing [X] experiments resulting in [Y%] conversion improvement
• Applied statistical methods to forecast [metrics] with [X%] accuracy
• Collaborated with stakeholders to define metrics and requirements for data-driven decisions

Data Analyst
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Created automated reports reducing manual work by [X] hours per week
• Performed exploratory data analysis identifying $[amount] in revenue opportunities
• Designed data collection processes improving data quality by [X%]
• Presented findings to management influencing [specific business decisions]

MACHINE LEARNING PROJECTS

Customer Segmentation Model
• Built K-means clustering model segmenting [X]K customers into [Y] distinct groups
• Applied RFM analysis and principal component analysis to identify high-value segments
• Enabled targeted marketing campaigns increasing conversion by [X%]
• Technologies: Python, Scikit-learn, Pandas, Matplotlib

Predictive Analytics Project
• Developed regression model predicting [target variable] with [X%] R-squared
• Performed feature engineering creating [number] predictive features from raw data
• Delivered actionable insights reducing [metric] by [Y%]
• Technologies: Python, Scikit-learn, Statsmodels, Seaborn

Classification Project
• Built logistic regression and random forest models with [X%] accuracy
• Implemented cross-validation and hyperparameter tuning improving performance by [Y%]
• Created feature importance analysis identifying [number] key drivers
• Technologies: Python, Scikit-learn, XGBoost

EDUCATION
[Degree Name], [Major]
[University Name], [City, State]
Graduation: [Month Year]

CERTIFICATIONS & TRAINING
• [Data Science Certification], [Platform], [Year]
• [Machine Learning Course], [Platform], [Year]
• [SQL or Analytics Certification], [Year]

TECHNICAL PROFICIENCY
• Advanced SQL: Complex queries, window functions, CTEs, query optimization
• Statistical Analysis: Hypothesis testing, regression, time series analysis
• Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Machine Learning: Supervised/unsupervised learning, model evaluation, feature engineering