Data Science & Statistical Consulting

I offer specialized statistical consulting and data science services to a wide range of clients, including businesses, public institutions, and researchers.

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With expertise in statistical software like R and Python, I support you through each phase of your project—from study design and data preparation to advanced statistical analysis, impactful visualization, and clear presentation of your findings.

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My goal is to help you navigate complex data challenges, uncover valuable insights, and communicate them effectively.

• Sampling Design: Selecting sampling units by evaluating different data collection methods to ensure population representation.

• Randomization Techniques: Implementing random assignment strategies to ensure unbiased group allocation.

• Quasi-Experimental Design: Designing studies when random assignment isn’t possible to infer causality.

• Mixed Survey Mode: Strategically combining various survey data collection methods, such as online, phone, and in-person.

• Cross-Sectional & Longitudinal Design: Planning and implementing one-time as well as recurring surveys.

• Sample Size Calculation: Determining the number of sampling units for an optimal balance between statistical accuracy and cost efficiency.

• Weighting: Adjusting survey data to compensate for undersampling or unit non-response bias.

• Questionnaire Design: Crafting effective, unbiased questionnaires with careful consideration of wording, order, and format.

Data Preparation & Statistical Analysis

• Data Exploration: Employing exploratory techniques to uncover patterns, anomalies, and relationships within the data.

• Data Cleaning and Manipulation: Removing unnecessary data, specifying appropriate data classes, and transforming data structures.

• Missing Data Imputation: Reducing bias by substituting missing data points using statistical methods.

• Measurement Validity: Testing if measurement tools accurately represent the concept they are intended to measure.

• Dimensionality Reduction: Reducing the complexity of high-dimensional data using techniques such as Principal Component Analysis and Factor Analysis.

• Automatic Feature Selection: Identifying key variables for statistical models, enhancing model parsimony.

• Statistical Modeling: Developing and applying models to test hypotheses, predict outcomes, and analyze complex variable relationships.

• Machine Learning Analysis: Utilizing advanced algorithms to extract valuable insights from complex data sets.

• Statistical Inference: Interpreting the results of statistical analyses to derive actionable insights and informed decisions.

Data Visualization & Reporting

• Compelling Visuals: Creating intuitive and esthetical visualizations that transform complex data insights into clear, engaging visuals.

• Interactive Graphics: Designing dynamic graphics and dashboards, enabling an engaging user experience.

• Data Storytelling: Crafting engaging narratives with visualizations to convey data insights clearly and memorably.

• Detailed Reporting: Developing clear reports and presentations that translate complex data into understandable formats for diverse audiences.

• Automated Documents: Implementing efficient automated processes, ideal for standardizing recurring reporting tasks.