Data Analysis
- Data cleaning and transformation
- Exploratory data analysis
- Pattern detection and business insights
- Data visualization
Junior Data Analyst / Data Scientist
Carlos Peñalver
Turning raw data into clear insights, visualizations and predictive models.
Computer Engineering graduate focused on data analysis, SQL, machine learning and applied data projects. I enjoy working with real datasets, understanding the business problem behind them and transforming complex information into useful conclusions.
Revenue & logistics overview
SQL EDAModel metrics
Accuracy82%
Recall76%
F1-score79%
SELECT segment, rate
FROM campaign_summary
WHERE subscribed = true;
model.fit(X_train, y_train)
About
Focused on practical data work
I am a Computer Engineering graduate currently focused on Data Analysis, Data Science and applied Machine Learning. My recent projects combine SQL, Python, exploratory data analysis, data preprocessing, visualization and predictive modeling. I am especially interested in transforming imperfect or complex datasets into clear, useful and actionable insights.
Contribution
What I can contribute
SQL & Databases
- SQL queries for analysis
- Relational and dimensional modeling
- Views, functions, scripts and indexes
- Analytical workflows from raw data to insights
Machine Learning
- Classification models
- Data preprocessing
- Feature engineering
- Model evaluation with metrics
- Comparison of models before and after preprocessing
Featured Projects
Projects that show the workflow
SQL EDA E-commerce / Olist
Exploratory data analysis project using an e-commerce dataset, building a complete SQL workflow from raw CSV files to a dimensional model, analytical queries and business insights.
MySQLSQLData ModelingDimensional ModelEDA
- Loaded CSV files into raw SQL tables
- Built a dimensional model with fact and dimension tables
- Created analytical SQL scripts, views, functions and indexes
- Generated insights about revenue, product categories, logistics and time trends
Bank Marketing Analysis
Analysis of a bank marketing campaign dataset to identify patterns related to customer subscription, segmentation and campaign performance.
PythonPandasMatplotlibSeabornData Analysis
- Cleaned and explored campaign data
- Created derived features for better segmentation
- Visualized conversion rates and customer behavior
- Extracted business-oriented conclusions from the analysis
Clinical Data Preprocessing
Final Degree Project focused on studying the importance of data preprocessing in clinical prediction problems, comparing machine learning models before and after applying preprocessing techniques.
PythonScikit-learnMachine LearningPreprocessingMedical Data
- Worked with several clinical datasets
- Compared models with and without preprocessing
- Applied techniques such as imputation, scaling, balancing and feature selection
- Evaluated models using Accuracy, Precision, Recall and F1-score
CSV Analytics Web App
Web application that allows users to upload CSV files and receive an initial automatic analysis, including dataset summary, detected issues, visualizations and basic insights.
PythonStreamlitPandasData Visualization
- CSV upload and automatic data preview
- Basic dataset profiling
- Detection of missing values and potential issues
- Initial visualizations and insights
Skills
Tools and techniques
Programming & Data
PythonPandasNumPyScikit-learnMatplotlibSeaborn
Databases
SQLMySQLPostgreSQLData modelingDimensional modeling
Machine Learning
Classification modelsPreprocessingFeature engineeringModel evaluationImbalanced data handling
Tools
GitGitHubJupyter NotebookVS CodeMySQL WorkbenchExcelPower BI