The Data Science Ecosystem in One Tidy Infographic
It probably comes as no surprise, but we talk to a lot of data scientists at CrowdFlower. We like learning the tools they use, the programs that make their lives easier, and how everything works together. Today, we'll really pleased to unveil the first of a three-part series about the data science ecosystem. Here it is in infographic form because, let's face it, everybody likes infographics:
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Comment by Benjamin Baer on May 19, 2016 at 11:57am
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Very pretty but very incomplete! As most of these lists seem to focus on what's hot rather than what's actually being adopted and making money. Someone else listed all the missing IBM tools but you're also missing FICO... they've only been in the analytics business for a short 60 years. I can understand you missing them.
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Comment by C. Sachs on November 7, 2015 at 7:45am
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Very cool... of course, working in the public sector, much of this is beyond our budget. So, in the Applications -> Customer box, we'd show Request Tracker (lame, I know), though we do have Tableau for Business Intelligence.
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Comment by Bob Vanderheyden on July 24, 2015 at 3:12am
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While I understand that its impossible for this to be a comprehensive listing and I may simply be overlooking them, but it seems that R, Cognos and DB2 should be in Statistical Tools/Predictive Analytics, BI and Structured Data, respectively. All 3 are major players in their respective spaces (far more prevalent than some that are listed).
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Nice list. Thank you for sharing. Pandas could be on there as well.
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Why are you only listing companies? The most used technologies are Open Source, and not limited those version supported by Cloudera, Hortonworks and Databricks.
Where is Hive? Mahout, Sqoop, Oozie, HBase, Storm, you know, the big data techs that everyone actually uses?
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Comment by Nagaraj Kulkarni on March 29, 2015 at 7:05pm
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Sorry. I was not careful. Thanks.
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Comment by Javier Alonso on March 27, 2015 at 4:01pm
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R missing? Do I see R Studio under Data Collaboration? And SPSS under Statistical Tools?
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It's a great list, but it looks like you've omitted a number of IBM tools:
SPSS - not just for statistical analysis, but also for data mining/predictive analytics/data collaboration (SPSS Modeler)
Watson Analytics for data exploration
IBM dashDB for warehousing and analytics on the cloud
IBM DB2 for databases (BLU acceleration for in-memory computing)
There's others of course...
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A concise ready reckoner for Data scientists; nice job. Could have entry for R and Matlab and category for Data visualisation.
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Nice, clear and readable. Good taxonomy work.
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