It takes more than a few tools to be successful at this craft. Data science is at the interface
of algebra, statistics and computer science. Visualizations and dashboards require proficiency with web
development. Modern databases and data storage inevitably leads to cloud technologies.
Tools of the trade
Data science and machine learning fall between several disciplines:
Statistics and probablility
Algebra
Algorithms
Data structures
Each field has subfields, along with specific tools. Statistics for example includes frequentist and Bayesian, as well as statistical learning.
Each subfield requires a set of practical tools, often software libraries, to perform computations. In our case, those tools are all Python libraries. Python is a "glue" language, that works for most applications. From web apps to advanced neural networks.
Speaking of web, we also provide full-stack web development capability, especially for data-driven applications like cloud-based dashboards, visualizations and real-time databases. The backend is typically programmed with Python Django and the front end with Javascript-SASS, using Javascript libraries like D3.js and Plotly.
Listing every tool of the trade would be very lengthy, and quite honestly boring, so here's a dendrogram of the main ones, made with D3.js.