How being used at your company? What is Data Science? http://www.oralytics.com/2012/06/data-science-is-multidisciplinary.html

Another Data Science Venn Diagram Data science is a multifaceted discipline, which encompasses machine learning and other analytic processes, statistics and related branches of mathematics, increasingly borrows from high performance scientific computing, all in order to ultimately extract insight from data and use this new-found information to tell stories. These stories are often accompanied by pictures (we call them visualizations), and are aimed at industry, research, or even just at ourselves, with the purpose of gleaning some new idea from The Data. http://www.kdnuggets.com/2016/03/data-science-puzzle-explained.html Venn diagram from KDnuggets' Gregory Piatetsky-Shapiro

Artificial Intelligence AI- subfield of computer science, uses set of rules: if this, then that Artificial Intelligence Machine Learning ML- computers/algorithms learn from experience or data v. explicit programming ANN- one approach to ML, one neuron is one function, a function has input that provides output, collection of trainable math units that collectively learn complex functions DL- consists of multiple layers of ANN, creating complex algorithms that mirror how we learn, for image recognition, training data consists of 10-100 million images Artificial Neural Network Deep Learning  ANN is a collection of trainable math units that collectively learn complex functions. One neuron is one function.  A function has input that provides output. DL consists of multiple layers in ANN. In the case of image recognition, training data consists of 10-100 million images. http://www.kdnuggets.com/2017/07/ai-deep-learning-explained-simply.html

https://www. womeninbigdata https://www.womeninbigdata.org/2017/05/30/machine-learning-is-the-new-black/ (Nvidia deck)

A few more concepts: Machine Learning Applied to Big Data Delivers Value via Business Insights http://www.kdnuggets.com/2017/07/4-types-data-analytics.html

Data Science Process Value of data is in insights that are clear and meaningful. Descriptive Analysis: What is happening in my business? Diagnostic Analysis: Why is it happening? Predictive Analysis: What is likely to happen? Prescriptive Analysis: What do I need to do? Neural networks one approach to machine learning Pattern recognition utilizes decision trees, etc. http://www.kdnuggets.com/2017/07/machine-learning-big-data-explained.html

Data Science Resources (sample list) Universities: Stanford http://deeplearning.stanford.edu/tutorial/ https://see.stanford.edu/Course/CS229 Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning; unsupervised learning; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Need MATLAB programming, matrix and vector math skills. MOOCs (https://en.wikipedia.org/wiki/Massive_open_online_course): Coursera- Intro to Machine Learning (Andrew Ng) https://www.youtube.com/watch?v=qeHZOdmJvFU&list=PLVJA7edNhnRTYqqW5zIj0gkVmxWnkXqTP Udacity- Intro to Data Science https://www.youtube.com/watch?v=Cgm3rG0cMQ&list=PLAwxTw4SYaPk41og7PER4HBpGciPw6n3x Organizations: Women in Big Data https://www.womeninbigdata.org/courses/https://www.womeninbigdata.org/blog/ https://www.womeninbigdata.org/2017/05/30/machine-learning-is-the-new-black/ (w/data scientists from Google, SAP, NVidia includes slide decks on Machine Learning, Neural Networks, Deep Learning) Technical training blogs from events w/ SAS, Crowdflower, AWS, IBM, SAP, MapR, etc. (and soft skills blogs) Publications: MIT Technology Review https://www.technologyreview.com/lists/companies/2017/intro/#23andme Sanford: high level courses to very in depth with Andrew Ng MOOCs: see Wikipedia for list of MOOCs Organizations: WiBD, list of courses, blogs Publications: MIT list of international companies, most touching AI, etc.

Data Science Resources (sample list) Corporate Websites, GitHub, YouTube: eBay: http://www.ebaytechblog.com/about/ https://github.com/eBay Google: https://www.udacity.com/course/deep-learning--ud730 IBM: https://www.youtube.com/watch?v=GXXYs51ADJU&t=18s Intel: https://software.intel.com/en-us/articles/data-science-is-an-ocean-of-information-stay-focused https://software.intel.com/en-us/ai-academy/training https://github.com/NervanaSystems/neon Microsoft: https://www.microsoft.com/en-us/research/group/deep-learning-group/ NVidia: https://www.nvidia.com/en-us/deep-learning-ai/education/

Helen Kim , VP of Business Operations for Product & Technology , eBay Telle Whitney, President & CEO Anita Borg Institute Ziya Ma, VP Software and Services Group & Director of big data technologies, Intel Erika Lunceford, Director, Technology Services, BNY Mellon Jennifer Bow, Head of Consumer Platforms, Oracle Data Cloud  Deepti Gupta, ABI.SV Community Leader & S/W Engineer, Paypal Steph Tung, ABI.SV & Manager, Product Ops, Uber Regina Karson, Women in Big Data Core Team & Principal ,RK Consulting