Gartner Magic Quadrants for 2015, Doug Henschen (Information Week Feb 2015)
This article from InformationWeek summarises a Gartner Magic Quadrant Analysis of different Data Analytics platforms. The platforms are plotted on a two dimension framework of Vision and Ability and lead to SAS and IBM being identified as the leaders in the Magic Quadrant. R v Python, The DataCamp Team (DataCamp May 2015)
If you don't know where to start this blog discusses whether to learn R or Python and why. But there are lots of other articles easily listed by a google search.
The programming language R, very popular in the Data Science world.
Links to the Official R Project website where you can learn about R and download the latest version.
A Free Development Environment for R. Makes R much easier to use and provides Tutorials on getting started in R.
Links to the Official Python website where you can learn about Python and download the latest version.
This is an open source version of Python packaged up with an easy to use Development Environment and all the packages needed to get started in Data Analytics using Python. There are many other IDE's available too.
The official Knime Website. Knime is an open source project that provides a graphical interface to streamline data flows, analysis and reporting steps in a Data Analysis task.
Tableau is the most popular Data Discovery tool. It allows you to examine relationships in the data with a few mouse moves. You can download a trial version.
A Data Discovery tool similar to Tableau but the free trial version does not expire, although it is limited in that workbooks created cannot be shared with other users.
SAS Academic Version
Links to the SAS Academic Version download site where you can purchase a cheaper version of SAS if you are enrolled at an academic institution.
The Four main languages for Analytics, Data Mining, Data Science
A KDNuggets poll of users as to what languages they use. The leaders being R, SAS, Python and SQL.
Kaggle hosts data science competitions. This links to a page on the Kaggle wiki which lists resources for learning data science programming.