Understanding, modeling, explaining and actioning data has been a major part of my career that I applied to the several industries and business functions I occupied.

Over the past decade I solved data challenges for over 500 customers and realized that virtually all professionals I talk to are budding data scientists that are passionate about data as much as I am.

Professionals are quite often challenged by the long-term strategies of their companies, their projects are not always prioritized, they need to fight for a budget to RFP a tool that they maybe don’t absolutely need.

From my experience, at least 80% of the data challenges that they encounter can easily be solved by data science literacy and open-source technologies.

And this is exactly what datascience-apps.com is about: helping my clients with data science processes with quick turnarounds, concrete applications, intuitive interpretations and cost-savvy services, while having a great deal of fun doing it.

Tools, methods & languages:
Focus on the R environment: Shiny, RStudio, tidyverse, ggplot2, rmarkdown, caret, tm, tidytext, survival, … (the list is endless, I load an average of 30 packages per project)

Some other techs (alphabetical order): Boolean Queries, CSS3, Excel, HTML, JavaScript, Lucene, Python, MySQL, PostreSQL, Regex, SQL, SOLR, Tableau, …

Methods & algorithms: logistic regression, glm’s, naive Bayes, SVMs, decision trees, random forest, biostatistics, survival analysis, rule learners, association rules, clustering, knn, NLP, time series, deep-learning, …

Languages: English, French, German