Why data analysis?

Data analysis is an essential process in a variety of fields, as it transforms raw data into usable information. Here are just a few reasons why data analysis is crucial:
informed decision-making, identifying trends and patterns, improving efficiency, personalization and segmentation, forecasting and anticipation, performance evaluation, .research and innovation, risk management, Collaboration support.


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Why should I help you with data analysis?

My name is Rodrigue, and I'm a mathematical statistician with solid expertise in mathematics and statistics, with 6 years' experience in this field. Indeed, I hold a research master's degree in statistics and probability and a bachelor's degree in fundamental mathematics.
Choosing a skilled data analyst like me is crucial to maximizing the value of data and achieving an organization's goals. Here are a few reasons why you might be the ideal choice as a data analyst:
1. Solid technical skills
- I have technical skills in data analysis tools such as Python, R, SQL or Power BI.
- I'm proficient in statistical analysis, which enables me to apply the right analysis methods depending on the type of data and the questions to be answered.
2. Practical experience
- I have experience in previous projects, which proves my ability to process real data and derive actionable insights.
- I probably have concrete examples of your work, showing how my analyses have led to strategic decisions or operational improvements.
3. Understanding Business Needs
- I am able to understand business objectives and relate my analyses to the organization's desired outcomes.
Critical Thinking and Problem Solving
- I have the critical thinking skills to interrogate data, ask the right questions, and solve complex problems.
- I am able to think analytically to identify trends, anomalies, and opportunities.
4. Communication skills
- I am able to clearly communicate my findings and recommendations to a non-technical audience, facilitating decision-making within the organization.
- I can present powerful visualizations that make my analyses more accessible and understandable.
5. Passion for Data Analysis
- My enthusiasm for data analysis and my willingness to learn and continually improve are valuable assets.
- I keep abreast of the latest trends and technologies in data analysis, which enables me to bring innovative approaches to bear.
6. Adaptability and flexibility
- I can adapt quickly to changes in organizational priorities or technological developments.
- I am able to work in a dynamic environment, which is essential in the constantly evolving field of data analysis.
7. Ethics and Integrity
- I will adhere to ethical practices in data management and analysis, ensuring the protection of sensitive data and compliance with confidentiality regulations.

Contact me so that we can discuss your problem via chat. That way, if you have any doubts about the methods to use, I'll make suggestions that you're free to approve or not. The aim is not to increase the number of options to be billed to you, but to help you address all the contours of your research question.


Project rendering :
- Results Interpretation: Analysis of statistical significance (p-value), effect sizes (coefficients), and confidence intervals.
- Tables and Visualizations: Creation of tables or graphs for clear visualization of results.
- Final Report: Presentation of findings in the form of reports, highlighting key relationships, insights gained and strategic recommendations based on the analysis.
If you have commissioned the design of a study and the performance of analyses, you will receive, as appropriate, an analysis plan, an analysis report and any other agreed documents (Word or PDF file).

Basic offer:
For 15 euros, you get a univariate analysis of up to 10 variables.
The univariate analysis takes into account :
- Descriptive statistics: Calculate measures of central tendency (mean, median, mode) and dispersion (standard deviation, variance, range, quartiles).
- Distribution: Verification of distributions with histograms, density curves and boxplots.
- Normality tests: Use tests such as the Shapiro-Wilk or Kolmogorov-Smirnov test to check whether data follow a normal distribution.
The results of these analyses will be accompanied by an interpretation of one or 2 sentences at most within 1 day.

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To go even further in my services, I offer you additional options:

PACK A: Bivariate analysis of up to 10 variables. (60 euros)
Bivariate analysis takes into account:
- Correlation: Calculation of correlation coefficients (Pearson for continuous variables, Spearman for ordinal or non-linear data) to quantify the relationship between two variables.
- Visualizations : Scatterplots, correlation matrices, comparative boxplots to explore the relationship between two variables.
- Statistical tests :
o Student's t-test (to compare two means).
o ANOVA (to compare the means of several groups).
o Chi² test (to test the association between two categorical variables).
Results are delivered within 5 days.

PACK B: Complete analysis of a database of up to 20 variables. (100 euros)
1. Univariate analysis
- Descriptive statistics: Calculate measures of central tendency (mean, median, mode) and dispersion (standard deviation, variance, range, quartiles).
- Distribution: Check distributions with histograms, density curves and boxplots.
- Normality tests: Use tests such as the Shapiro-Wilk or Kolmogorov-Smirnov test to check whether data follow a normal distribution.
2. Bivariate analysis
- Correlation: Calculation of correlation coefficients (Pearson for continuous variables, Spearman for ordinal or non-linear data) to quantify the relationship between two variables.
- Visualizations : Scatterplots, correlation matrices, comparative boxplots to explore the relationship between two variables.
- Statistical tests :
o Student's t-test (to compare two means).
o ANOVA (to compare the means of several groups).
o Chi² test (to test association between two categorical variables).
3. Multivariate analysis
- Principal Component Analysis (PCA): reduces the dimensionality of data while retaining maximum variance, useful for visualizing and interpreting high-dimensional data.
- Correspondence Factorial Analysis (CFA): for qualitative data, this technique visualizes the relationships between the modalities of categorical variables.
Results are delivered within 7 days.

PACK C: Principal component analysis of up to 30 variables. (35 euros)
Principal Component Analysis is used to extract important information from a table of multivariate data, and to express this information in the form of a set of a few new variables called Principal Components. Results are delivered within 4 days.

PACK D: Multiple Correspondence Analysis (MCA) of up to 30 variables (50 euros)
Correspondence Analysis (CA) is an extension of Principal Component Analysis, adapted to explore relationships between qualitative variables (or categorical data). Like Principal Component Analysis, it provides a solution for summarizing and visualizing a set of data in two-dimensional plots.
Results are delivered within a maximum of 4 days.

PACK E: Factor analysis of mixed data (FAMD) of up to two variables. (60 euros)
Factor analysis of mixed data (FAMD) is a principal component method dedicated to the analysis of a data set containing both quantitative and qualitative variables.
Results are delivered within a maximum of 5 days.

PACK F: Multiple Factor Analysis (MFA) of up to 30 variables. (70 euros)
Multiple Factor Analysis (MFA) is a multivariate data analysis method used to summarize and visualize a complex data table in which individuals are described by several sets of variables (quantitative and/or qualitative) structured into groups.
Results are delivered within 5 days.

PACK G: Hierarchical clustering on the main components of up to 30 variables. (60 euros)
Clustering is one of the most important data mining methods for discovering knowledge in multivariate datasets. The aim is to identify groups (i.e. clusters) of similar objects within a data set of interest.
Results are delivered within a maximum of 5 days.

PACK H: Simple and multiple linear regression of up to 20 variables ( 35 euros)
Modeling the relationship between a dependent variable and several continuous and/or categorical explanatory variables.
Results provided within 5 days at most.

PACK I: Logistic regression of up to 20 variables (35 euros)
If the dependent variable is binary, this model is used to model the probability of occurrence of an event.
Results are provided within a maximum of 4 days.

PACK J: Two-factor analysis of variance (ANOVA). (35 euros)
Used to compare averages between several groups.
Results provided within 3 days.




I have technical and theoretical skills in :
-Polytomous regression.
-PACK L: Fish regression
-PACK M: Mixed-effects regression
-PACK N: Modeling a differential equation.
-PACK O: Dynamic modeling
-PACK P: Stochastic modeling


Don't hesitate to place your order - you're sure to be satisfied.
If you have a specific need or can't find what you're looking for in all these options? Don't hesitate to contact me via chat. We'll discuss which options are right for you, and if necessary, we'll create custom options for you.


See you soon!