1. How being used at your company? What is Data Science?

2. 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.
Venn diagram from KDnuggets' Gregory Piatetsky-Shapiro

3. 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 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 million images.

4. https://www. womeninbigdata
(Nvidia deck)

5. A few more concepts: Machine Learning Applied to Big Data Delivers Value via Business Insights

6. 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.

7. Data Science Resources (sample list)
Universities:
Stanford
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 (
Coursera- Intro to Machine Learning (Andrew Ng)
Udacity- Intro to Data Science
Organizations:
Women in Big Data
(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
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.

8. Data Science Resources (sample list)
Corporate Websites, GitHub, YouTube:
eBay:
Google:
IBM:
Intel:
Microsoft:
NVidia:

9. 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