CodeIsGo.com

Tony Ojeda witnessed the lack of structure in conventional approaches in Exploratory data analysis, so he decided to document his own process in an attempt to come up with a framework for data exploration.

Exploratory analysis that gives us a sense of what additional work should be performed to quantify and extract insights from our data.

The framework he came up with consists of a Prep Phase and an Explore Phase. Each phase has several steps in it that we will walk through together as we progress through this series.

Pictures, schemas, explanation on types of information, example data set, example code and more included.

UC Berkeley published their Course Lectures: Analyzing Big Data With Twitter. Bit older but still very good - published and available for free. Over 15+ hours of video lectures. These lecture notes simply summarized the course at a high level.

Lectures include Twitter’s Vice President and various employees, Hadoop contributor, Splunk employee and more. e.g for Spark - next thing for Hadoop / MapReduce - Matei Zaharia, a PhD student in UC Berkeley’s AMP Lab, gave us a terrific lecture about how it works and what’s coming next.

If you’d like to know how Twitter computes its Trending Topic - Kostas Tsioutsiouliklis (@kostas) shared some of the secrets with the class. He also talked about MinHash algorithms.

There are also assignments for students added and best of them selected with supporting documentation. Teriffic.

Zalando Tech team (German electronic commerce company) has created comprehensive set of guidelines and the best practices for API design for mutli-team development company.

Great RESTful APIs look like they were designed by a single team. This promotes API adoption, reduces friction, and enables clients to use them properly. To build APIs that meet this standard, and to answer many common questions encountered along the way of RESTful API development.

It covers: design principles, security, compatibility, JSON guidelines, naming, resources, HTTP, performance, pagination, hypermedia, data formats and much more.

Really nice job, congrats to Zalando Team.

Marc Curry’s interesting article in which he claims: We’ve got a design for your next cloud-based container deployment. He aims to save you inordinate amount of time otherwise spent researching and debating architectural decisions, tooling, parameters, or a required sequence of tasks when trying to deploy a project to the cloud.

Deploying on the most popular Cloud Infrastructure Providers

Red Hat provides, and periodically updates, a comprehensive Reference Architecture document specific to deploying Red Hat OpenShift Container Platform on the most popular Cloud Infrastructure Providers: Red Hat OpenStack Platform, Amazon Web Services (AWS), Google Cloud Engine (GCE), and VMware vCenter.

Quality charts and schemas provided. You will find here everything needed for successful deployment on your chosen cloud including decent amount of explanation at common customization points.

Jack Maden (and team) created series of articles in which they summarize the key findings of our extensive data science report on load time. While part one looked at bounce rate, and part two focused on page count, this post summarizes their findings regarding focus time.

Authors of study looked at 64,407 user sessions of ecommerce website. Results: as average load time goes up, focus time goes down.

Then they compared it with travel website, where they looked at 19,421 user sessions. Result: the longer a website takes to load for a user, the less time they will spend actively engaging with it.

Overall, however, it’s clear that in order to get users to spend more time actively engaging with websites, businesses must keep load times to a minimum. Extensive charts are provided. Eyes opener.

A business technologist Joseph Jude wrote article outlining the best practical approach to learning for programmers. In software industry, only one principle remains true: if you are not updated, you will be outdated.

Author had gone through the cycle of learning new technology over and over again. As he repeated this cycle, he noticed a pattern. He has fine tuned that pattern and developed a systematic approach to learn new language or technology.

Article lists useful resources aimed at helping you with learning - including books, blogs & sites, newsletters and code repositories, including curated “awesome” repositories.

Learning is only one aspect of learning. If you want to grasp the concepts and internalise them, then you should try them out. Commit to do it for an extended period of time. … Amazing stuff, please read and keep learning!

Vik Paruchuri - Dataquest’s founder has put together a fantastic resource on building a data science portfolio. Article is one in series of 3 articles and together they create comprehensive overview how to demonstrate your skills for potential employer in data science industry.

Article describes how to create end to end project, including:

• Understanding the context
• Exploring the data and figuring out the nuances
• Creating a well-structured project, so its easy to integrate into operational flows
• Writing high-performance code that runs quickly and uses minimal system resources
• Documenting the installation and usage of your code well, so others can use it

Summary: If you put some work in, you can make a great portfolio that companies are impressed by. And an example code in Python included in GitHub repository.

Timothy Prickett Morgan great article about getting max juice from your database for machine learning using graphics processing unit (GPU).

GPUs has become go-to accelerator for databases to get performance and scale boost. GPUs are used to execute the parallel functions of SQL queries to massively speed up the processing of queries against databases.

Article is about GPUdb database (used by US federal government) and its commercial version Kinetica developed by company of the same name. Commercial version was tested with US POstal Service and helped the organization drive 70 million fewer miles than it did in 2015 and save 7 million gallons of fuel.

Interesting reading about data lake size (200,000 devices are streaming data into this cluster once every minute) and also query performance compared to SAP HANA.

Vinay Sahni - Founder of Enchant and Full stack developer - shares his experience with designing RESTful API. He tried to come up with pragmatic answers to hard questions so final API is easy to use, easy to adopt and flexible enough to dogfood for our own user interfaces.

Articles lists zillion of carefully selected resources to help you follow recommended practices when it makes sense and to help you guide decision making process.

What you learn

• It helps you with identification of key requirements for the API
• Explains RESTful URLs and actions, e.g.separating your API into logical resource
• How you deal with relations
• Mentions importance of SSL** etc.
• An API is only as good as its documentation.
• Do not forget to version your API - always.

Well worth your time.

Jorge Bay - lead engineer for the Node.js and C# client drivers for Apache Cassandra and DSE at DataStax - published article to help you improve performance of Node.js and focus on the most significant areas that could cause throughput degradation.

In order to improve the performance of an application that involves IO, you should understand how your CPU cycles are spent. Article focus on benchmarks, CPU profiling, system calls, Node.js timers and also Ecmascript features and dependencies.

Key Takeaways

• Try to minimize the amount of syscalls by grouping / batching writes.
• Consider the overhead of issuing and clearing the different timers in your application.
• CPU profilers give you useful information but won’t tell you the whole story.
• Be wary of high-level ECMAScript features, especially if you are not using the latest JavaScript engine or a transpiler.
• Control your dependency tree and benchmark your dependencies.