Data Analysis Expert

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Expert Data Analysis for Business Success

Transform Your Data into Insights

Data scientist with over 15 years of experience developing and implementing data analysis and facilitating decision-making in public and private companies. Generation, structuring and cleaning of the database.

Development of descriptive statistical analysis and diagnostics from visualization techniques, data mining and econometric models, providing essential information to drive transformation, action, and business success.

These services are based on excellent communication skills to have a strong, organized, and efficient relationship with our clients, with or without technical knowledge.

I am committed to building strong relationships to drive adaptative outcomes and meet the goals of each of our clients.

Publications and Research

2023

Podcast: Data Analysis for Business Success

A deep dive into the development of descriptive statistical analysis and diagnostic tools through advanced visualization techniques, data mining, and econometric modeling. Each episode delivers insightful perspectives that empower transformation, strategic decision-making, and long-term business success.

2020

Mathematical models for behavioral analysis

The chapter outlines the specific contribution of mathematization to the development of the Social Sciences, adopting an approach that serves as a guide for the proper appreciation of statistical methods. Works like this strike a valuable balance by recognizing, on the one hand, the irreplaceable role of statistical analysis in generating theoretically grounded and empirically corroborated explanations, and on the other, the paradox that such explanations—and the models that formalize them—can reach levels of complexity that make them not only difficult to understand but also potentially imprecise.

2019

Study on Corruption in Latin America

This paper focuses on the analysis of corruption in Latin America by studying the effect that the perception of the existence of corrupt officials has on the satisfaction of the democracy regime, considering the political and economic context in which citizens live to accept or reject it. In addition, it investigates the influence of the fight against corruption on the approval of democratic regime. Finally, this paper studies implication of corruption based on a contextual or geographical and analysis of democratic approval. The hypotheses are analyzed generating logit, clarify and multilevel logit model.

2018

Values, Attitudes, and Political Participation

This study uses the material and post-material values described by Inglehart (1977) and applies the methods developed by Schwartz (1992) to identify distinct dimensions of human values in the Mexican case. This paper focuses on materialist and post-materialist values, human values and their effect on instrumental and symbolic political participation. The analysis also investigates the implications of birth cohort on political participation and the impact of attachment to distinct values within generations on those two forms of political action. This suggests a change in the country’s democratic culture or at least a democratic political culture, which is distinctly larger than the one observed in the post-revolutionary era.

2017

The impact of cognitive mobility and the media

This document focuses on the importance of cognitive mobility on political behavior (to vote or not) and the its influence on the voting decision of the Mexican voter. It also investigates the impact of traditional media and new media on the voting option of the electorate. Moreover, it explores the implications of birth cohort for political participation and the impact these distinct forms of information have on each generation’s political behavior. The variables of cognitive mobility are generated using exploratory and confirmatory factor analysis. The hypotheses are tested using logit models. For the analysis of different generations, multigroup logit models are used.

2014

Roughly speaking

A fresh, original, and essential work. This compelling volume brings together a remarkable collection of essays written over a broad span of time. What makes it truly stand out is its dual strength: on the one hand, each essay is firmly anchored in the most current and sophisticated strands of academic thought—particularly in the rigorous methods drawn from the study of social behavior. On the other hand, these texts are written with the clear intention of making that knowledge useful and accessible for interpreting the puzzles and contradictions of everyday life. The result is a collection that generously bridges the gap between scholarly insight and public relevance, inviting not only specialists but also general readers into the conversation.

Services

A business problem starts the data science process.

In this sense, I will work with the client to understand their needs. Once the problem is identified and defined, I can solve it by obtaining, cleaning, exploring, and modelling data and interpreting the results.

Data cleaning consists of normalizing them according to a predetermined format. It includes managing missing data, correcting errors, and deleting outliers. Some examples of data cleansing are:
• Change all date values to a common standard format.
• Correct misspellings or extra spaces.
• Correct mathematical inaccuracies or remove commas from large numbers.

Is a preliminary analysis of data used to plan other strategies for modelling. Data scientists gain an initial understanding of data through descriptive statistics and data visualization tools. They then explore the data to identify interesting patterns that can be studied or used.

Software and algorithms are used to gain deeper insights, predict outcomes, and prescribe the best action. The techniques of regression analysis, multivariate analysis, econometric models, and machine learning, as well as association, classification, and grouping, are applied to the training dataset. The model could be tested with predetermined test data to assess the accuracy of the results. The data model can be adjusted many times to improve outcomes.

Regression analysis is a statistical method that allows examining the relationship between two or more variables and identifying which are the ones that have the most significant impact on a topic of interest.

refers to different methods that study and examine the simultaneous effect of multiple variables. Multivariate statistical methods analyze the collective behaviour of more than one random variable.

These are defined as those models that contain the set of hypotheses necessary for their empirical application; they constitute, in short, the instrument that allows one to connect and confront theory and reality.

Data scientists work alongside analysts and businesses to turn data insights into action. They make diagrams, graphs, and tables to represent trends and predictions. Data synthesis helps stakeholders understand and effectively apply the results.

Economic analysis is crucial to a company's planning, evaluation, and control. This diagnosis always offers a global vision of the organization’s structure concerning its profitability, solvency, and risks. With the data obtained, decisions to be made can be better oriented.

Promote knowledge of financial and entrepreneurship issues in people willing to create new companies.
The Economic Advisory makes available to clients’ consultation services in matters such as financing for projects and companies, economic-financial studies and modelling and financing and refinancing strategies.
Administrative Advice is to support clients in the timely decision-making and proper management of their company, including analysis at the level of the organizational structure considering the distribution of functions for each of the company's jobs causing efficient and effective development.

Coding, in simpler terms, is the language used by computers to understand our commands and, therefore, process our requests. Programming is a list of codes arranged in a sequence that results in the completion of work. Statistical coding is the form of classification that is most familiar to researchers. Coding is the task of taking data and assigning it to categories. This allows us to turn usually qualitative data into quantitative or numerical data.

Statistical Programming refers to computation techniques that help in data analysis. Making sense of data using statistical concepts/methodology is usually achieved by writing code, and the programming language used to perform this task is called statistical programming. This concept is widely used in industries like Pharma, telecom, banking & finance, and weather forecasts.

Some languages come with statistical programming packages/libraries that offer various statistical and graphical techniques to explore large data sets and create graphical displays for better and quick understanding.

These packages support statistical techniques like linear and nonlinear modelling, classification, clustering, and time-series analysis.