Massive open online courses (MOOC), youtube videos, ebooks, documentation of analytics software and programming libraries are freely available. These resources allow us to build and maintain a data analytics environment, to deal and analyze with data from diverse sources. The necessary skills to work with such an environment require permanent training and willingness to explore new approaches.

This list provides links to material explaining the techniques and strategies applied for the analyses of data from various sources. Whenever possible we link to resources which offer hands-on explanations, but also introduce or refer to theoretical concepts.

Microsoft Virtual Academy -> Video tutorials about data science, machine learning, data handling and more. The focus is obviously on Microsoft products and the Azure cloud computing. -> Videos from renown universities and large enterprises. The offering is large. We focused on courses related to python, statistics and machine learning.> Frank Kane provides lots of hand-on explainations about data science, machine-learning, with python libraries tensorflow and keras. -> MOOC about data mining with Weka.

Python libraries

NumPy, Skilit-learn, Pandas, Matplotlib, and other libraries are best understood by starting with their documentation, which most of the imte offer very usefull tutorials.