PARIS
Following statistical school and almost five years in Insurance and Pharmaceutical industry, I have spent the last five years in professional marketing.
I urge my business partners on the importance of data analysis.
A statistician can make analyzes variables with or without title explicit .That is why the mastery of the business sector of the company is not a discriminatory factor to perform a reliable statistical analysis. So, the job of statistician is cross-disciplinary.
To ensure the reliability of data also of my analysis , I am following different vocational training courses at SAS Institute in France.
- SAS ® Programming Fast Track
- Using SQL in a SAS session
- The macro language: automation and configuration treatments SAS
- Statistical Analyst Level 1
- Data mining with SAS: Construction of a predictive scoring model and technical
- Statistical Analyst Level 2
- Qualitative data analysis using logistic regression
- Classification Techniques
- Analysis of mixed models with SAS on health data
- Analysis of data on French
o Statistics
- Collection, description, visualization and synthesis of statistical data
- Datamining
- Decision-making support, forecasts
- Inquiries, market studies
- Statistical software (SAS, SPAD, SPLUS, XLSTAT, SPHINX, R)
o Computing
- Operating system: WINDOWS
- Planning : Algorithmics, Language VB, PHP
- Information system and Base of Relational data
- Conception: model Entities / associations
- Exploitation of the data: SQL
- IT development application: ACCESS
- Use of software under WINDOWS
- Spreadsheet : Excel
- Geographical Information System : MapInfo
o Descriptive Economy and Accounting
- Descriptive Economy, Politics Economy
- Establishment of costs
- Approach of the accounting organizations
- Accounting information system
o Géography
-Geomorphology - Climatology
-Geopolitics - Mapping
Mes compétences :
Statistiques
Microsoft Excel
SAS ® Programming Fast Track
Data Base
Geography
Using SQL in a SAS session
SAS
Informatique
Statiscal Analyst level 1
Regression Diagnostics
Multiple Regression
Linear Regression
Statiscal Analyst level 2
Analysis of variance
Statistics
Big data
Principal component analysis
Cluster analysis of observations
Variable Clustering
Correspondance analysis
Predictive Modeling
Fitting the Model
Measuring Classifier Performance
Preparing the Input Variables
Pas de formation renseignée
