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)
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