Digital Transformation Delivers Change at Scale
Just about every enterprise right now is going through some sort of digital transformation. For most, it’s about surviving, for many, it’s about disrupting and leading. Software and user experience (UX) are the new competitive edge. Managing availability and performance is now a matter of life or death for IT Operations/Devops. Digital transformation guarantees two things for IT Operations: more change and scale. If enterprises want to move faster they have to break things up into smaller pieces, and let teams work independently in an autonomous way. Agile, DevOps, Cloud and Microservices are real-life…
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10 Machine learning Project for…
PowerToys Run is a quick launcher for power users that contains some additional features without sacrificing performance. It is open source and modular for additional plugins.
A system-wide color picking utility for Windows 10 that enables you to pick colors from any currently running application and automatically copies it in a configurable format to your clipboard.
In addition to the global outbreak of a Coronavirus, one of the most memorable stuff in 2020 is Donald Trump, who is governing the country on Twitter. Obviously, the tweets don’t help to prevent the virus, but they do trigger lots of fluctuation on the US stock market.
So, is it possible to do quantitative trading based on Trumps’ tweets? or at least can we predict stocks’ price trending ?
The answer is, absolutely YES!
We build a bot long time ago named trump2cash. This bot watches Donald Trump’s tweets and waits for him to mention any publicly traded companies…
If you’re wondering if your project is “graphy” and how to get started with graph data science (GDS), this chapter can help. We give you some Neo4j resources to guide you to more information, and to help you explore your project’s opportunity and successfully move forward from concepts to production, we include these ten tips:
Investigate use cases and get comfortable with concepts.
Because graph technology is applied across industries and in various use cases, it can be hard to know where to start. To expand your knowledge and help you get comfortable with GDS, review these examples:
In this chapter, we walk you through an example of applying graph data science (GDS) techniques to investigate and predict financial fraud. After we familiarize you with a sample financial
transaction dataset, we then remove the outlier information that may skew your results and identify suspicious clusters of clients. After that, you visually explore one of the clusters for graph-based indicators of fraud and look at how graph-based features can help predict fraudulent behavior in the larger dataset.
Finding a Good Fraud Dataset
To simulate a good fraud dataset, you want to create realistic, synthetic data to describe fraudulent transactions…
If you’re going to use graph data science (GDS), you should run it on a platform. In this chapter, we show you what platform pieces Neo4j offers to help you. Neo4j is a graph technology company that provides an enterprise-grade GDS platform that includes four components.
Neo4j supports transactional processing and analytical processing of graph data as well as visualization. It also includes graph storage and compute with data management and analytics tooling. The set of integrated tools includes a common protocol, API, and query language (Cypher) to provide effective access for different uses. …