Enabling Cross-Boundary Data Science with Privacy Enhancing Technology

Presented by Ryan Carr
October 19 | 18:00 | Virtual

As data protection becomes more important, it becomes more and more challenging to easily work with such data. How do you both secure data while making it accessible? With homomorphic encryption, users can do just that. Join us in October to hear from one of the leaders in data protection on how data scientists can work with encrypted data.

After much discussion, we've decided to postpone DAX until Spring 2022. While registrations were increasing, we still fell short of the needed speakers for the event and had always hoped that the first conference would be in-person. We plan on pushing hard the next few months to make our first event a success.

We still need help spreading the word and bringing the community together. If you would like to advocate for the conference, please reach out!

Get in touch if you would like to submit a talk. 
 

Did you miss Matt Harrison's session on Idiomatic Pandas? You can catch it and all of our past talks on our YouTube channel. 

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How Big Data Carried Graph Theory Into New Dimensions — Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data. Tags: Visualization, Big Data, Network Models, Analysis

Applications of Graph Neural Networks (GNN) — This article focuses on GNN applications. Since the basic GNN theory is already covered by the mentioned articles, we will not repeat it here. And for the design details for each application, please also refer to the original research papers. Tags: GNN, Medical Applications

Twelve Quick Tips for Software Design — This paper describes a dozen tips that can help data scientists design large programs. These tips are taken from published sources, the author's personal experience, and discussions over thirty-five years with the creators of widely-used libraries and applications. Tags: Scalability, Software Design

How Midsize Companies Can Compete in AI — Artificial intelligence (AI) as an upcoming general-purpose technology is poised to create many new business opportunities and to disrupt entire industries. Startups and large corporations are seizing AI opportunities and strengthening their position. But what about midsize companies that often lack access to big data and AI talent? These midsize firms risk being left behind in the age of AI. As a remedy, these firms should consider pooling their data and talent in joint AI ventures. Tags: AI, Management

Prescriptive Analytics is Changing the Game for Big Data — This is prescriptive analytics, which combines descriptive analytics and predictive analytics to finally make big data something businesses can use for gain, and not just knowledge. With prescriptive analytics, you can find the best approach to move away from the data and turn it into actionable results. In recent years, prescriptive analytics has defined the evolution of Big Data. It’s here to stay and it’s changing the game. Tags: Predictive Analysis, Business, Management, Effeciency

How to Choose the Right Data Visualization Tool — When developing an application that shares data with users, you may need to present a visualization of graphs, charts, dashboards, or other data embedded in the application. This feature helps users better understand the data, discover insights, and improve the user experience. Looking at a well-designed data visualization, you use more of your application and are happy with the results. Tags: Visualization, Software Development

Storage for Unstructured Big Data Should be Part of a Company's Strategy — For many IT organizations, data storage is an afterthought and not a strategic concern. However, when it comes to big data management, storage should occupy center stage. Tags: Storage, Unstructured Data

Model Drift in Data Analytics: What Is it? So What? Now What? — Today, data analytics models are increasingly becoming the major drivers of business decisions and performance. This trend will continue at a much faster pace, given the rate at which data is captured and the increasing maturity of machine learning (ML) platforms. In this reality, managing model drift is critical to ensuring the accuracy of insights or predictions. Tags: ML, Modeling

Enabling the Use of Ethical and Responsible AI Via Data and Analytics Platforms — Most large enterprises are starting to commit to using artificial intelligence (AI) and machine language (ML) responsibly and ethically, in line with their core values. Enabling ethical and responsible use of AI and ML starts with enabling trust in the underlying data and explaining the role of data combined with the algorithm that generated the insights. Tags: ML, AI

Lifelike Robot Nurse, Grace, Brings Emotional Care to Isolated Patients — The Hong Kong team behind celebrity humanoid robot Sophia is launching a new prototype, Grace, targeted at the healthcare market and designed to interact with the elderly and those isolated by the COVID-19 pandemic. Tags: AI, Medical, Robot

 

Geoda — The free program provides a user friendly and graphical interface to methods of descriptive spatial data analysis, such as spatial autocorrelation statistics, as well as basic spatial regression functionality. The latest version contains several new features such as full space-time data support in all views, a new cartogram, a refined map movie, parallel coordinate plot, 3D visualization, conditional plots (and maps) and spatial regression. Tags: Data Visualization, AI

Glean — Glean is a system for working with facts about source code. You can use it for: Collecting and storing detailed information about code structure and querying information about code. Tags: FaceBook, IDE, Javascript, Hack

SVF — SVF is a source code analysis tool that enables interprocedural dependence analysis for LLVM-based languages. SVF is able to perform pointer alias analysis, memory SSA form construction, value-flow tracking for program variables and memory error checking. Tags: C, C++

Analyzing US Census Data — Census data are widely used in the United States across numerous research and applied fields, including education, business, journalism, and many others. Until recently, the process of working with US Census data has required the use of a wide array of web interfaces and software platforms to prepare, map, and present data products. The goal of this book is to illustrate the utility of the R programming language for handling these tasks, allowing Census data users to manage their projects in a single computing environment. Tags: Analysis

Parallelizing Python Code — Python is great for tasks like training machine learning models, performing numerical simulations, and quickly developing proof-of-concept solutions without setting up development tools and installing several dependencies. When performing these tasks, you also want to use your underlying hardware as much as possible for quick results. Parallelizing Python code enables this. Tags: Python, Development

Homemade Machine Learning — This repository contains examples of popular machine learning algorithms implemented in Python with mathematics behind them being explained. Each algorithm has interactive Jupyter Notebook demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions right in your browser. Tags: ML, Python

DeFi Developer Roadmap — Here we collect and discuss the best DeFi & Blockchain researches and tools - contributions are welcome. Tags: BlockChain

Sports Analytics Reading List — Athletes, coaches, parents, or sport enthusiasts can pour over data to help steer their careers or their teams for the W. Tags: Sports, Analysis

Welcome to IMS &mdash This is the website for Introduction to Modern Statistics, First Edition by Mine Çetinkaya-Rundel and Johanna Hardin. Introduction to Modern Statistics, a downloadable free PDF. Tags: IMS, OpenIntro, Learning

ML for Beginners — Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson (plus one!) curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Tags: ML, Learning

Machine-Learning on Dirty Data in Python — Often in data science, machine-learning applications spend a significant energy preparing, tidying, and cleaning the data before the machine learning. Here we give a set of Python tutorials on how some of these operations can be simplified with adequate machine-learning tools. Tags: ML, Python, Learning

Learn You Some Kedro — In this article, I introduce Kedro, an open-source Python framework for creating reproducible, maintainable and modular data science code. After a brief description of what it is and why it is likely to become a standard part of every data scientist’s toolchain, I describe some technical Kedro concepts and illustrate how to use them with a tutorial. Tags: Python, Learning
 

 

Are you looking for a way to get involved in the community and make an impact? Check out the volunteer opportunities with U.S. Digital Response.  

Book Review Opportunity
Are you interested in reviewing an O'Reilly book for the publisher and sharing your views with the world? As if that isn't enough, you get to take a book home to enjoy as well. Send us an email and we'll get you started.

Data Analysis Volunteer Work to Support Baltimore City
Are you an expert with data and willing to mentor, or are you an up and coming hobbyist looking for a side project to work on? We have put together a group to focus on a few problems working with Baltimore City data and need your help. The current project focuses on data parsing and analysis for the Baltimore Board of Estimates. If interested, please send us an email or join us on Slack to discuss building a side project group.

Considering a Career Change?
Are you a software or system engineer, data scientist, analytic developer, or cybersecurity expert interested in learning about new opportunities?
Please send us an email to learn about the opportunities available with our partners.

Are You Hiring?
If your company is looking for data scientists, data engineers, software engineers, and other data related experts, please reach out so that we can help our members find new opportunities.
Please send us an email introducing your company and needs.

Get Involved with Data Works!
Want to be more involved in our data science community? If you have experience running workshops, hack-a-thons, curating newsletters, or are just interested in helping to grow the meetup, please send us an email!

Erias Ventures
Erias has an immediate need for Software Engineers, System Engineers, Test Engineers, Data Scientists, and System Administrators. External referral bonuses are available. For more information, please contact us at info@eriasventures.com.