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Data Science at the Command Line: Exploring Data

@machinelearnbot

The tools for data science are many and varied, and span a variety of ecosystems. Python and R tend to be some of the more popular programming environments, but there are many other options, including a plethora of programming and scripting languages, GUI- and web-based tools.


How to Lie with Visualizations: Statistics, Causation vs Correlation, and Intuition!

@machinelearnbot

Honestly, I never use Excel for analysis-- just too many challenges (scale, robustness, etc)! But that's really the overarching theme of this article-- that statistics (and even visualization) isn't just something you run in Excel or do without proper forethought. So in the spirit of things, the flaws are by design and you're on point-- there are a lot of errors and we should be cautious of being bamboozled by "data-driven results" because not all of them are created equal!


Data Science and Big Data, Explained

@machinelearnbot

Data science incorporates mathematics, statistics, computer science and programming, statistical modeling, database technologies, signal processing, data modeling, artificial intelligence and learning, natural language processing, visualization, predictive analytics, and so on. It is often applied to large data sets in order to perform general data analysis and find trends, or to create predictive models.


How to learn data science: from data mining to machine learning Packt Hub

#artificialintelligence

Data science is a field that's complex and diverse. If you're trying to learn data science and become a data scientist it can be easy to fall down a rabbit hole of machine learning or data processing. To be an effective data scientist you need to be curious. You need to be prepared to take on a range of different tasks and challenges. But that's not always that efficient: if you want to learn quickly and effectively, you need a clear structure โ€“ a curriculum โ€“ that you can follow.


Journal of Open Source Software

#artificialintelligence

A week ago, I received a request for refereeing a paper for the Journal of Open Source Software, which I have never seen (or heard of) before. The concept is quite interesting with a scope much broader than statistical computing (as I do not know anyone in the board and no-one there seems affiliated with a Statistics department). Papers are very terse, describing the associated code in one page or two, and the purpose of refereeing is to check the code. Which is a pretty light task if the code is friendly enough to operate right away and provide demos. Please comment on the article here: R โ€“ Xi'an's Og The post Journal of Open Source Software appeared first on All About Statistics.