Saudi Arabia’s Global and Regional Economy Shifts in 2018

By Sahil Bali

Sаudі Arаbіа іѕ аn Arab ѕtаtе іn Wеѕtеrn Aѕіа соnѕtіtutіng thе bulk оf thе Arabian Peninsula. Wіth a lаnd area оf аррrоxіmаtеlу 2,150,000 km2 (830,000 ѕԛ mі), Sаudі Arаbіа іѕ gеоgrарhісаllу the second-largest state in thе Arаb wоrld аftеr Algeria. Sаudі Arаbіа is home tо the rеlіgіоn’ѕ 2 mоѕt ѕасrеd mоѕԛuеѕ: Mаѕjіd аl-Hаrаm, іn Mесса, the dеѕtіnаtіоn оf thе аnnuаl Hаjj pilgrimage, аnd Mеdіnа’ѕ Masjid an-Nabawi, thе burial ѕіtе оf the рrорhеt Muhammad. Rіуаdh, thе саріtаl, is a ѕkуѕсrареr-fіllеd metropolis. Saudi Arаbіа іѕ a dеѕеrt соuntrу encompassing most оf the Arаbіаn Peninsula, wіth thе Rеd Sеа and Pеrѕіаn Gulf соаѕtlіnеѕ. Nаturе hаѕ gіftеd this Arаb state wіth rісh оіl resources, соntrоllіng thе second lаrgеѕt оіl reserves in thе world.

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The (Data Science) Notebook: A Love Story by David Wallace

Computational notebooks for data science have exploded in popularity in recent years, and there’s a growing consensus that the notebook interface is the best environment to communicate the data science process and share its conclusions. We’ve seen this growth firsthand; notebook support in Mode quickly became one of our most adopted features since launched in 2016.

This growth trend is corroborated by Github, the world’s most popular code repository. The amount of Jupyter (then called iPython) notebooks hosted on Github has climbed from 200,000 in 2015, to almost two million today. Data from the nbestimate repository shows that the number of Jupyter notebooks hosted on GitHub is growing exponentially:

This trend begs a question: What’s driving the rapid adoption of the notebook interface as the preferred environment for data science work?

Inspired by an Analog Ancestor

The notebook interface draws inspiration (unsurprisingly) from the research lab notebook. In academic research, the methodology, results, and insights from experiments are sequentially documented in a physical lab notebook. This style of documentation is a natural fit for academic research because experiments must be intelligible, repeatable, and searchable.

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Resources for Data Science Job Seekers

February 12, 2018 | Sadavath Sharma — Analyst

Getting your first job in data science can be a full-time job all on its own. Simply finding a job post worth applying to can be a chaotic pursuit (though we’ve tried to make that part easier with the Mode Analytics Data Jobs Board (edited). Once you’ve found a job posting that looks like it could be a fit, you need to make sure you stand out from the crowd of other applicants.

As a data science job applicant, there are two stages to your search. First, you need to get an interview. To do that, you need documentation that you can fill the role. This is where your resume, your portfolio, and (unfortunately) your online presence come in. There are serious issues with looking up candidates on search engines, which range from creating unconscious bias to opening up murky legal situations, but it happens (not here at Mode though). For better or worse, it’s worth taking a quick look at your name’s search results to get a sense for what people might find.

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Thinking in SQL vs Thinking in Python

July 7, 2016 | Benn Stancil — Chief Analyst at Mode

Over the years, I’ve used a variety of languages and tools to analyze data. As I think back on my time using each tool, I’ve come to realize that each encourages a different mental framework for solving analytical problems. Being conscious of these frameworks—and the behaviors they promote—can be just as important as mastering the technical features of a new language or tool.

I was first introduced to data analysis about ten years ago as a college student (my time studying the backs of baseball cards notwithstanding). In school, and later as a economics researcher, I worked almost exclusively in two tools—Excel and R—which both worked well with CSVs or Excel files downloaded from government data portals or academic sources.

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101+ Infographic Tools And Resources

The below is a roundup of helpful tools, resources, and articles to create infographics that people love. Broken down into categories according to each stage of the infographic creation process, from brainstorming to distribution, so you can skip to the categories you might be most interested in.

Infographic Tools & Resources for Ideas/Inspiration

  1. Why ideas are the most important piece of an infographic: Find out what makes a great infographic idea.
  2. 16 exercises to come up with great infographic ideas: Our favorite tips and tricks.
  3. 5 ways to know if your idea will work: Framework to vet your ideas.
  4. Alltop: An aggregator of the Internet’s most popular stories.
  5. Answer the Public: Visualizations of the questions people ask Google.
  6. Brainpickings: An inventory of cross-disciplinary interestingness, spanning art, science, design, history, philosophy, and more.
  7. BuzzSumo: Insights on the most-shared content on any topic.
  8. Dadaviz: Charts on a variety of subjects.
  9. Daily Infographic: Design inspiration updated each day.
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Data Will Save Music

The writing is on the wall.
The music industry is dying.
Nobody buys music.
It’s the Wild West.
The last one might be true. But the rest? Not exactly.

In the Wild West, the winner of the shootout was always the one who was armed the best and able to take the best shot. Nowadays, artists and executives need to have that same kill or be killed attitude. It’s time to upgrade the arsenal.

Leonardo da Vinci left us with a quote that we can use to bridge the gap of this analogy:

“Principles for the Development of a Complete Mind: Study the science of art. Study the art of science. Develop your senses — especially learn how to see. Realize that everything connects to everything else.”

Science + Art. That’s the future of the music (and entertainment) industry.

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Simple Analytics is Good for Business

A research paper by renowned consultancy Aberdeen Group reveals that “[data] complexity is often best answered with simplicity”. Several new surveys conducted by the group reveal some interesting findings with regards to the costs and benefits of using an integrated tool for data preparation, querying and visualization, as opposed to the “assembly line” approach of dividing these tasks between various proprietary DW, ETL and visualization tools.

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Reid Hoffman: A.I. Is Going to Change Everything About Managing Teams

Imagine a spider chart mapping a complex web of interactions, sentiments, and workflow within an office. What would your company look like?

When most of us think of artificial intelligence in the workplace, we imagine automated assembly lines of robots managed by an algorithm. LinkedIn’s Reid Hoffman has a different idea.

In an essay for MIT Sloan Management Review, Hoffman describes human applications for the technology. Among other things, it would help to use data science to improve the way we onboard new team members, organize workflow, and communicate about performance. Addressing the question of how technology will change management practices over the next five years, Hoffman explains how the use of a “knowledge graph” will become standard management practice.

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How virtual, augmented reality helps NASA explore space

Before astronaut Scott Kelly ended his year in space, he accomplished an unprecedented technological feat. He called mission control using Skype and streamed his first-person perspective through an augmented reality headset that NASA sent to the International Space Station in December.

“We messed around with it for like two hours and immediately I sensed this is a capability we could use right now,” Kelly said during a news conference in March.

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The dplyr package for R

dplyr: A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

When working with data you must:
Figure out what you want to do.
Precisely describe what you want in the form of a computer program.
Execute the code.

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