
Hello, I’m Gabriel, a student of Data Analysis at INSEEC, located in Bordeaux, a distinguished French business school.
My primary focus lies in data analysis, encompassing everything from initial exploration to visualization, prediction, and communicating findings. I possess robust skills in techniques such as Exploratory Data Analysis (EDA) and data cleansing.
My enthusiasm for Big Data has driven me to acquire proficiency in various tools, including R, Python, Tableau, Power BI, and SQL. Moreover, I’ve had the privilege of applying these skills during internships at prestigious companies like Credit Agricole.
I am dedicated to advancing professionally in the field of data analysis and eagerly anticipate the opportunities that lie ahead in this domain.
Data Analyst
Services
Data Cleaning
Refining data to ensure accuracy, consistency, and reliability.
How can I assist you?
- Removing duplicates: Identifying and removing duplicate records in datasets to ensure data integrity.
- Data normalization: Ensuring data is in a consistent and uniform format, such as standardizing names, addresses, or dates.
- Fixing format errors: Correcting common format errors in data, such as capitalization errors, extra spaces, or unwanted special characters.
- Cleaning missing data: Handling missing or null values in data using techniques like data imputation, deletion, or estimation.
- Data validation: Verifying data accuracy and consistency using validation rules and cross-checks.
- Unit standardization: Converting measurement units to a common standard for easier analysis and comparison.
- Detecting and correcting inconsistencies: Identifying and correcting data inconsistencies, such as out-of-range values or data not matching other sources.
- Date and time normalization: Ensuring dates and times are in the correct format and consistent across the dataset.
- Removing irrelevant or redundant data: Identifying and removing data that is not useful for analysis or duplicated in the dataset.
- Cleaning special characters: Removing unnecessary special characters that may affect data integrity or subsequent processing.
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Examples
- I identified and corrected the presence of duplicates and variations in variable names, such as ‘Mercedes-Benz’, ‘Mercedez’, ‘Mercedez-Benz’, ‘MB’, among others, ensuring data consistency and coherence for precise analysis.
- During a data analysis project to identify the best movies according to professional (IMDB) and popular (public voting) criteria with over a million movies, I came across a database including various video formats. To ensure result accuracy, I adjusted variables such as duration to focus exclusively on feature films, demonstrating my ability to work with complex data and ensure its relevance in analysis.
EDA
Uncovering insights through comprehensive examination and visualization of data.
How can I assist you with Exploratory Data Analysis?
- Identification of key variables: Identifying the most important variables that influence the dataset and the problem being analyzed.
- Descriptive statistical analysis: Performing a statistical summary of variables to understand their distribution and main characteristics.
- Exploration of relationships between variables: Analyzing the relationship and dependency between different variables using techniques such as correlation matrix or scatter plots.
- Trend and temporal pattern analysis: Investigating temporal patterns and trends in the data over time using techniques such as time series or seasonality analysis.
- Data segmentation and clustering: Identifying homogeneous groups within the dataset through clustering techniques.
- Principal component analysis: Reducing the dimensionality of the data and finding the most important features using dimensionality reduction techniques such as PCA.
Examples
- During exploratory data analysis of an e-commerce company, I identified purchasing behavior patterns of customers based on their geographical location, enabling more effective market segmentation and personalized marketing strategy.
- In a study of exploratory data analysis on students’ academic performance, I identified interesting correlations between study time, exam performance, and socioeconomic variables, providing valuable insights for improving educational strategies.
Predictive Modeling
Utilizing historical data to forecast future trends and outcomes.
Data Visualization
Presenting complex data in visual formats for easier interpretation.
How can I assist you with Data Visualization?
- Tool selection: Advising on selecting the best data visualization tools for your needs, such as Tableau, Power BI, or open-source tools like D3.js.
- Development of interactive dashboards: Creating interactive dashboards and dynamic visualizations that allow effective data exploration.
- Data integration: Integrating multiple data sources to create comprehensive and meaningful visualizations.
- Performance optimization: Optimizing the speed and efficiency of visualizations for a smooth user experience.
Examples
- I created an interactive dashboard on Tableau that shows sales performance of a company by region and product, allowing users to filter and explore data easily.
- I developed an executive report on Power BI that visually presents key KPIs of a vast database of American companies, such as revenue, expenses, and profit margin, with interactive charts and pivot tables.
PORTAFOLIO
- ALL
- eda
- Data viz