Priscilla Nuñez

Twitter: @solutions

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A Beginner’s Guide to AI/ML  – Machine Learning for Humans – Medium

The ultimate guide to machine learning. Simple, plain-English explanations accompanied by math, code, and real-world examples.

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François Fleuret

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#Deeplearning #DL

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1 points and 0 comments so far on reddit

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Using SVG graphics in blog posts

Scalable vector graphics • http://www.magesblog.com/2016/02/using-svg-graphics-in-blog-posts.html

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Drone Owner

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The 12 Algorithms Every Data Scientist Must Know credit via Twitter: @DataScienceCtrl @EvanSinar

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Bayesian Statistics - simple

Excellent! Bayesian Statistics explained to Beginners in Simple English @analyticsvidhya #DataScience #Statistics

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E-Man: How technology is changing our experiences & ways of being.

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Helpful CPI Campaign Guide for Mobile App Developers

“Building a Successful CPI Campaign: A How-to Guide” with Appnext by @appnext_updates http://www.slideshare.net/appnext/building-a-successful-cpi-campaign-a-howto-guide-with-appnext via @SlideShare

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Coding Tip #8 Keep a cheat sheet!

Whether you’re practicing a new language or mastering one you already know, keeping a cheat sheet can always be helpful. The reason I recommend this is because, although you might know most methods, some may slip out of thought and a cheat sheet will always act as a nice refresher

These are the cheat sheets I use (mainly bc these are my primary languages aside from the ones used for web design)

Java

Ruby

and I know these aren’t the full sheets but they can be found here amongst most other languages. 

as a plus, the methods have a redirect link with further explanation + possible input&output. 

im also taking tip recommendations

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What is Data Science?

Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.

Data Science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, operations research,information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining,database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing. Methods that scale to big data are of particular interest in data science, although the discipline is not generally considered to be restricted to such big data, and big data technologies are often focused on organizing and preprocessing the data instead of analysis. The development of machine learning has enhanced the growth and importance of data science.

Is there a difference between a Business Intelligence Officer & Data Scientist? 

There is a clear separation between someone who is in Business Intelligence vs. Data Science. A Data Scientist creates the software intelligence to assist in research and interprets the information while the Business Intelligence Officer, also known as a BIO, is one who translates the outcome of what the Data Scientist statistically created. Many times the Data Scientist is the one that does both - creates the software along with providing visual interpretation. Data Scientist are well rounded in computer programming languages like Java, Python, R, Scala, C programming (along with influences) and structured query language (SQL) to communicate with relational Database management. Another language you may want to observe is Julia.