Hello, I am Andrés Ramírez

Unlock the power of your data

Business Intelligence & Data Analyst skilled in actionable insights and strategic decision-making through data analysis and visualization. Empower your organization to enhance efficiency, profitability, and achieve long-term success with data-driven strategies.

Andrés Ramírez

Skills

  • Leadership and Teamwork

  • Communication and Storytelling skills

  • Business awareness

  • Qualitative Decision-Making

  • Quantitative Decision-Making

  • Analytical thinking

  • Stakeholders management

  • Problem-Solving skills

  • Machine learning and Data mining.

Tools

  • Viz: Power BI / Tableau / Spotfire

  • Projects: Jira / Trello / Planner

  • ETL: Excel / MySQL / WEKA

  • DATA: Python / R Studio / KNIME

Languages

  • Spanish (Native speaker)

  • English (Advanced)

  • French (Intermediate)

Services

¿What are the underlying causes of the churn rate?

Dashboard Power BI

  • Churn analysis with influential feature filters: Charges, Plans and Usage.

  • Flag potential churn risks, take targeted actions for customer retention.

  • Feature relations and state-specific behavior patterns with charts and a map.

  • DAX functions used to perform calculations and derive meaningful metrics.

Supervised Machine Learning Model Knime

  • Churn prediction model using features: Charges, Plans and Usage.

  • Efficient ETL data management for optimized output.

  • Decision tree algorithm employed for training and testing the model.

  • Accuracy achieved and validated through Confusion Matrix.

¿How features influence House Prices?

Data analysis NumPy and Pandas in Python

  • Comprehensive analysis using Python libraries for insights.

  • ETL Process: Gathered features, preprocess, prepare, and clean data.

  • Feature Importance: Identified significant factors impacting property values.

  • Exploratory Data Analysis (EDA): Analyzed feature data relations and patterns.

Data Visualization and Machine Learning Modeling Seaborn and Scikit-learn

  • Visual Identification: Key factors driving house prices showcased.

  • Summarized Data: Presented through Seaborn plots and charts.

  • Explored Scikit-learn algorithms for accurate estimations.

  • Optimal Model Selection: Most effective model (GBR) after fine-tuning.

¿What are salary trends among data professionals?

Data analysis NumPy and Pandas in Python

  • Use of Numpy and Pandas libraries.

  • EDA made to summarize features and identify general patterns.

  • ETL to preprocess, prepare and clean the data.

  • Statistical techniques to analyze salary variations and correlations.

Data Visualization and Insights Vega-Lite in Python

  • Identification of key factors driving salary trends.

  • Effective communication of findings through data visualization techniques.

  • Clear and interpretable charts to facilitate understanding of data patterns.

  • Evaluation of factors impacting data professionals' salaries.

¿Would you have survived the sinking of The Titanic?

Dashboard Power BI

  • Interactive dashboard highlighting key insights on Titanic sank.

  • Charts to present survival rates based on passenger demographics.

  • Filters, Tooltips and a Help button for enhanced exploration, data analysis and user accessibility.

Supervised Machine Learning Model Knime

  • Survival chance based on features: Sex, Class, Chlidren, Age and Family.

  • Data proccesing and transformation to optimize the output.

  • Decision tree algorithm to train and test the model.

  • Accuracy and precision achieved through testing and validation.

¿How is the content diversity in Netflix's catalog?

Dashboard Tableau

  • Interactive dashboard with Netflix catalog by Country, genre and rating.

  • Dynamic filters to identify relation betwen different features.

  • Geographic Heat Map that displays distribution of data by country.

  • Filters for information related to a specific Movie/TV show.

Unsupervised Machine Learning Model Knime

  • Data proccesing to discover underlying patterns in the Netflix Catalog.

  • K-Means clustering algorithm applied to data´s classification.

  • Elbow Method used for defining the best number of clustering.

  • Validated through manual iteration and comparison with Silhouette Analysis.

Andrés Ramírez

Academic Background

Executive MSc in Business Intelligence.
EIG Business School.
Spain, 2023.Specialization in Business Administration.
CEIPA Business School.
Colombia, 2023.Agile Master 360: Scrum Master, Product Owner, Kanban Expert.
CertiProf/LiteThinking.
USA, 2022.Bachelor of Science in Mechanical Engineering.
Universidad Nacional.
Colombia, 2016.

Working Experience

RENAULT(2017- ) Quality Assurance Manager
- Developed daily KPI monitoring system to track performance and facilitate the PDCA cycle.
- Applied advanced predictive modeling techniques to enhance KPI forecasting accuracy.
- Achieved a 40% reduction in customer claims at dealerships through quality initiatives and stakeholders management.
- Created visualization tool to prioritize problems to solve.

Mitsubishi (2015-2017) Design Team Leader
- Used parametric modeling to optimize accuracy and reduce errors in elevator and escalator part design and selection.

UNAL (2013) Maintenance Intern
- Led a preventive and predictive maintenance plan for a petroleum laboratory, using robust data acquisition process.

Certifications