A Simple Introduction To Data Structures: Part One – Linked Lists

#artificialintelligence

The world of programming is always changing. We are constantly finding better ways to do what it is that we do. That is a great thing. Iteration is a very powerful concept. However, there are a few ideas and constructs in the computer science world that remain constant.


Supercharging Visualization with Apache Arrow

@machinelearnbot

Imagine a future where Minority Report-style data visualizations run in every web browser. This is a big step forward for critical workflows like investigating security and fraud incidents, and making critical insights for the next level of BI. Today's options are dominated by rigid Windows desktop tools and slow web apps with clunky dashboards. The Apache Arrow ecosystem, including the first open source layers for improving JavaScript performance, is changing that. Frustrated with legacy big data vendors for whom "interactive data visualization" does not mean "sub-second", Dremio, Graphistry, and other leaders in the data world have been gutting the cruft from today's web stacks.


Big data and the risks of using NoSQL databases

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NoSQL uses procedural implementation-specific structures expressed in a JSON format to represent its data model. ECMA International Standards body developed JavaScript to handle tasks in the browser. They also provided an extension to JavaScript to develop a lightweight language for interchanging data over the Internet called JavaScript Object Notation (JSON). The downside of JSON is that it lacks the capabilities to provide referential integrity. These data models are neither interoperable nor standardized.


The best programming language for data science and machine learning

ZDNet

Video: What programming languages do you need to know to earn more? Arguing about which programming language is the best one is a favorite pastime among software developers. The tricky part, of course, is defining a set of criteria for "best." With software development being redefined to work in a data science and machine learning context, this timeless question is gaining new relevance. Let's look at some options and their pros and cons, with commentary from domain experts.


Usage-Driven Groupings of Data Science and Machine Learning Programming Languages

#artificialintelligence

Analysis of usage patterns of 16 data science programming languages by over 18,000 data professionals showed that programming languages can be grouped into a smaller set (specifically, 5 groupings). That is, some programming languages tend to be used together apart from other programming languages. A few of the different groupings of languages reflect specific types of applications or specific roles that data professionals could support, including analytics, general-purpose, and front-end efforts. Data scientists and machine learning engineers rely on programming languages to help them get insights from data. A recent analysis showed that data professionals typically use around 3 programming languages.