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kdnuggets.com June 2, 2017
7 Steps to Mastering Data Preparation with Python

Data preparation, cleaning, pre-processing, cleansing, wrangling. Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. ...
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forbes.com June 6, 2017
The 9 Best Free Online Big Data And Data Science Courses

Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years.
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145 saves
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newyorker.com June 3, 2017
How to Call B.S. on Big Data: A Practical Guide

“Nothing that you will learn in the course of your studies will be of the slightest possible use to you,” the Oxford philosophy professor John Alexander Smith told his students, in 1914, “save only this: if you work hard and intelligently, you should be able to detect when a man is talking
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1,734 saves
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nytimes.com June 5, 2017
10,000 New Yorkers. 2 Decades. A Data Trove About ‘Everything.’

Your phone, in all likelihood, knows more about you than your doctor. Your credit card company knows your likes and dislikes better than your closest friend. Google knows your thoughts, and even completes your sentences. Your telephone service provider knows where you are at all times.
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172 saves
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medium.freecodecamp.com November 1, 2011
An overview of every Data Visualization course on the internet

A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead.
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84 saves
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medium.freecodecamp.com November 1, 2011
The best Data Science courses on the internet, ranked by your reviews

A year and a half ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead.
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1,344 saves
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githubengineering.com May 31, 2017
DNS Infrastructure at GitHub

At GitHub we recently revamped how we do DNS from the ground up. This included both how we interact with external DNS providers and how we serve records internally to our hosts.
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beamandrew.github.io June 4, 2017
You can probably use deep learning even if your data isn't that big

Over at Simply Stats Jeff Leek posted an article entitled “Don’t use deep learning your data isn’t that big” that I’ll admit, rustled my jimmies a little bit.
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1,005 saves
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thenextweb.com May 18, 2017
Understand data science with access to dozens of cool courses – for less than $40

It takes a steady, methodical individual to spot the hidden patterns lurking in large data sets. To understand the information these info dumps are trying to give you, learn the means for managing data analysis with an Ultimate Data and Analytics Bundle.
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115 saves
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dev.to June 4, 2017
Creating Your First Machine Learning Classifier Model with Sklearn

Okay, so you're interested in machine learning. But you don't know where to start, or perhaps you have read some theory, but don't know how to implement what you have learned.
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205 saves

theguardian.com January 19, 2017
How statistics lost their power – and why we should fear what comes next

In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone – no matter what their politics – can agree on.
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engineering.linkedin.com December 16, 2013
The Log: What every software engineer should know about real-time data's unifying abstraction

I joined LinkedIn about six years ago at a particularly interesting time. We were just beginning to run up against the limits of our monolithic, centralized database and needed to start the transition to a portfolio of specialized distributed systems.
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11,513 saves
forbes.com February 12, 2016
Big Data: 33 Brilliant And Free Data Sources For 2016

Data is ubiquitous — but sometimes it can be hard to see the forest for the trees, as it were. Many companies of various sizes believe they have to collect their own data to see benefits from big data analytics, but it’s simply not true.
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1,664 saves
medium.freecodecamp.com January 20, 2017
The Rise of the Data Engineer

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. I wasn’t promoted or assigned to this new role. Instead, Facebook came to realize that the work we were doing transcended classic business intelligence.
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fastcodesign.com February 8, 2017
A Dead Simple Tool To Find Out What Facebook Knows About You

That’s the question behind the new Chrome extension Data Selfie.
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13,475 saves
aadrake.com January 18, 2014
Command-line Tools can be 235x Faster than your Hadoop Cluster

Introduction As I was browsing the web and catching up on some sites I visit periodically, I found a cool article from Tom Hayden about using Amazon Elastic Map Reduce (EMR) and mrjob in order to compute some statistics on win/loss ratios for chess games he downloaded from the millionbase archive,
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slack.engineering December 7, 2016
Data Wrangling at Slack

For a company like Slack that strives to be as data-driven as possible, understanding how our users use our product is essential. We knew when we started building this system that we would need flexibility in choosing the tools to process and analyze our data.
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1,834 saves
johnnunemaker.com February 24, 2014
Analytics at GitHub

In the interest of getting back into writing, I want to break the seal with a simple “what have I been up to and thinking about lately” style post. Hopefully future topics will be more focused and frequent. For the past year, I have been working on data and analytics at GitHub.
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1,006 saves
rs.io May 29, 2014
100+ Interesting Data Sets for Statistics

Edit: Hey guys! This has proved to be one of the most popular articles on the site, so I’ve created a supplemental download on the 5 biggest statistics mistakes beginners make and how to avoid them.
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1,651 saves
mckinsey.com January 25, 2017
Making data analytics work for you—instead of the other way around

Does your data have a purpose? If not, you’re spinning your wheels. Here’s how to discover one and then translate it into action.
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1,807 saves
code.facebook.com August 31, 2016
Smaller and faster data compression with Zstandard

People are creating, sharing, and storing data at a faster rate than at any other time in history.
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1,317 saves
hbr.org June 1, 2016
Visualizations That Really Work

Knowledge workers need greater visual literacy than they used to, because so much data—and so many ideas—are now presented graphically. But few of us have been taught data-visualization skills.
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3,522 saves
martinruenz.de February 14, 2016
The dark side of big data

First things first, what is big data about? Control.
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605 saves
multithreaded.stitchfix.com March 16, 2016
Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department

“What is the relationship like between your team and the data scientists?” This is, without a doubt, the question I’m most frequently asked when conducting interviews for data platform engineers.
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2,151 saves
kaushik.net February 25, 2014
7 Data Presentation Tips: Think, Focus, Simplify, Calibrate, Visualize++

There are three elements to our "big data" efforts, or unhyped normal data efforts: Data Collection, Data Reporting, and Data Analysis. We are all aware that the best companies in the world have an optimal DC-DR-DA allocation when it comes to time/money/people: 15%-20%-65%.
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833 saves
datacamp.com January 3, 2017
Python Machine Learning: Scikit-Learn Tutorial

The first step to about anything in data science is loading in your data. This is also the starting point of this scikit-learn tutorial. This discipline typically works with observed data. This data might be collected by yourself or you can browse through other sources to find data sets.
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554 saves
thenextweb.com May 18, 2017
The 14 best data visualization tools

Raw data is boring and it’s difficult to make sense of it in its natural form. Add visualization to it and you get something that everybody can easily digest.
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5,352 saves
techblog.netflix.com January 27, 2015
Netflix's Viewing Data: How We Know Where You Are in House of Cards

Over the past 7 years, Netflix streaming has expanded from thousands of members watching occasionally to millions of members watching over two billion hours every month.
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1,247 saves
forbes.com June 16, 2016
Excel Reporting: 5 Reasons Why It Is Bad For Business

We’ve entered the age of big data, in which more and more companies are seeing the value and importance of data in many different areas of their business, from market and customer research, to internal sales figures and HR analytics.
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990 saves
econsultancy.com February 26, 2017
17 visualisation tools to make your data beautiful

You don’t need to be a seasoned data scientist or have a degree in graphic design in order to create incredible data visualisations. It has become a lot simpler to mine your data and interpret your insights in an engaging, attractive, and most importantly easy to understand way.
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1,327 saves
forbes.com March 31, 2016
Data Storytelling: The Essential Data Science Skill Everyone Needs

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it.
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1,550 saves
nirvacana.com
Becoming a Data Scientist – Curriculum via Metromap 90

Data Science, Machine Learning, Big Data Analytics, Cognitive Computing …. well all of us have been avalanched with articles, skills demand info graph’s and point of views on these topics (yawn!). One thing is for sure; you cannot become a data scientist overnight.
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1,444 saves
analyticsvidhya.com October 26, 2016
17 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)

Data science projects offer you a promising way to kick-start your analytics career. Not only you get to learn data science by applying, you also get projects to showcase on your CV. Nowadays, recruiters evaluate a candidate’s potential by his/her work, not as much by certificates and resumes.
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1,583 saves
idlewords.com
Haunted By Data

In preparing this talk I decided to check out the data landscape, since I hadn't seen it for a while. The terminology around Big Data is surprisingly bucolic. Data flows through streams into the data lake, or else it's captured in logs.
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1,851 saves
stripe.com February 2, 2017
Online migrations at scale

Engineering teams face a common challenge when building software: they eventually need to redesign the data models they use to support clean abstractions and more complex features.
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1,267 saves
blog.algorithmia.com October 31, 2016
Introduction to Machine Learning for Developers

Today’s developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.
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3,172 saves
adilmoujahid.com August 13, 2016
Interactive Data Visualization of Geospatial Data using D3.js, DC.js, Leaflet.js and Python // Adil Moujahid // Data Analytics and more

The goal of this tutorial is to introduce the steps for building an interactive visualization of geospatial data. To do this, we will use a dataset from a Kaggle competition to build a data visualization that shows the distribution of mobile phone users in China.
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622 saves
economist.com May 6, 2017
Data is giving rise to a new economy

AN OIL refinery is an industrial cathedral, a place of power, drama and dark recesses: ornate cracking towers its gothic pinnacles, flaring gas its stained glass, the stench of hydrocarbons its heady incense.
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blog.stephenwolfram.com April 24, 2013
Data Science of the Facebook World—Stephen Wolfram Blog

More than a million people have now used our Wolfram|Alpha Personal Analytics for Facebook. And as part of our latest update, in addition to collecting some anonymized statistics, we launched a Data Donor program that allows people to contribute detailed data to us for research purposes.
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3,801 saves
johnwittenauer.net December 5, 2014
Machine Learning Exercises In Python, Part 1

This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. The original code, exercise text, and data files for this post are available here. One of the pivotal moments in my professional development this year came when I discovered Coursera.
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2,558 saves
eng.uber.com July 19, 2016
The Uber Engineering Tech Stack, Part I: The Foundation

Update: This article discusses the lower half of the stack. For the rest, see Part II: The Edge and Beyond. Uber’s mission is transportation as reliable as running water, everywhere, for everyone. To make that possible, we create and work with complex data.
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5,118 saves
datasciencecentral.com March 28, 2015
How to Become a Data Scientist - On your own

Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ Patil as the first chief data scientist to the United Nations using predictive analytics to forecast bombings on scho
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1,631 saves
venturebeat.com July 17, 2016
Forget learning to code: Every employee should know data analysis

“Learn to code.” Around the world, it has been a familiar refrain. And the world has duly learned. Time was that the standard in-school computer education was little more than word processing, spreadsheets and some basic programming.
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3,001 saves
analyticsvidhya.com February 17, 2017
Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL & Big Data

Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That’s why we have cheat sheets.
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3,348 saves
forbes.com March 8, 2016
21 Scary Things Big Data Knows About You

What does big data know about you? Quite a lot.
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1,081 saves
lifehacker.com June 25, 2014
What Is "Big Data," and Who's Collecting It?

Dear Lifehacker, I've been hearing more and more about "big data." What is it, and is it something I should be worried about? Is this another way companies harvest my data and sell it? Dear Bewitched by Buzzwords, "Big data" is the latest tech industry buzz-phrase.
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1,177 saves
alyssafrazee.com January 2, 2014
alyssa frazee

My sister is a senior undergraduate majoring in sociology. She just landed an awesome analyst job for next semester and was told she'll be using some R in the course of her work.
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916 saves
mattturck.com February 1, 2016
Is Big Data Still a Thing? (The 2016 Big Data Landscape)

In a tech startup industry that loves its shiny new objects, the term “Big Data” is in the unenviable position of sounding increasingly “3 years ago”. While Hadoop was created in 2006, interest in the concept of “Big Data” reached fever pitch sometime between 2011 and 2014.
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1,199 saves
engineeringblog.yelp.com July 14, 2016
Billions of Messages a Day - Yelp's Real-time Data Pipeline

This is the first post in a series covering Yelp's real-time streaming data infrastructure.
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1,158 saves
blog.yhat.com January 26, 2017
Becoming a Data Scientist

This blogpost is an excerpt of Springboard's free guide to data science jobs and originally appeared on the Springboard blog. Most data scientists use a combination of skills every day, some of which they have taught themselves on the job or otherwise. They also come from various backgrounds.
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949 saves
wired.com November 27, 2014
The Next Era of Designers Will Use Data as Their Medium

The software industry today is in need of a new kind of designer: one proficient in the meaning, form, movement, and transformation of data. I believe this Data Designer will turn out to be the most important new creative role of the next five years.
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1,015 saves
confluent.io February 25, 2015
Putting Apache Kafka To Use: A Practical Guide to Building a Streaming Platform (Part 1)

Data systems have mostly focused on the passive storage of data. Phrases like “data warehouse” or “data lake” or even the ubiquitous “data store” all evoke places data goes to sit.
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1,215 saves
lifehacker.com October 25, 2016
How to Lie to Yourself and Others With Statistics

Misusing statistics is one of the most powerful ways to lie. Normally, we teach you how to avoid misinterpreting statistics, but knowing how numbers are manipulated can help you spot when it happens.
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3,137 saves
mattturck.com April 5, 2017
Firing on All Cylinders: The 2017 Big Data Landscape

It feels good to be a data geek in 2017. Last year, we asked “Is Big Data Still a Thing?”, observing that since Big Data is largely “plumbing”, it has been subject to enterprise adoption cycles that are much slower than the hype cycle.
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1,065 saves
medium.com February 15, 2016
Evolution of the Netflix Data Pipeline

Our new Keystone data pipeline went live in December of 2015. In this article, we talk about the evolution of Netflix’s data pipeline over the years. This is the first of a series of articles about the new Keystone data pipeline. Netflix is a data-driven company.
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1,544 saves
blog.codeclimate.com
5 Reasons to Use Protocol Buffers Instead of JSON For Your Next Service

Service-Oriented Architecture has a well-deserved reputation amongst Ruby and Rails developers as a solid approach to easing painful growth by extracting concerns from large applications. These new, smaller services typically still use Rails or Sinatra, and use JSON to communicate over HTTP.
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2,043 saves
hbr.org March 16, 2017
A 5-Step Process to Get More Out of Your Organization’s Data

Daniel (not his real name) was a VP human resource manager at a Fortune 500 company. I asked him whether he had collected any data that could provide him with insights into systematic patterns. “I made sure we get exit interviews done with every single employee who is leaving us,” he replied.
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726 saves
medium.com June 20, 2015
Doing Data Science at Twitter

On June 17, 2015, I celebrated my two year #Twitterversary @Twitter. Looking back, the Data Science (short for DS) landscape at Twitter has shifted quite a bit:
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datasciencecentral.com February 27, 2017
Data Science, Machine Learning, BI Explained in a Amazing Few Pictures

Guest blog post by Rubens Zimbres, PhD. This article brings images from my work modeling with Mathematica, my experience as a Business Analyst and also my doctorate lessons.
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1,545 saves
thenewinquiry.com October 9, 2014
View From Nowhere

On the cultural ideology of Big Data. “What science becomes in any historical era depends on what we make of it” —Sandra Harding, Whose Science? Whose Knowledge? (1991)
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1,105 saves
medium.freecodecamp.com November 1, 2011
I ranked every Intro to Data Science course on the internet, based on thousands of data points

A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead.
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2,283 saves
forbes.com March 14, 2016
Top 10 Hot Big Data Technologies

As the big data analytics market rapidly expands to include mainstream customers, which technologies are most in demand and promise the most growth potential? The answers can be found in TechRadar: Big Data, Q1 2016, a new Forrester Research report evaluating the maturi
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948 saves
washingtonpost.com December 13, 2016
Scientists are frantically copying U.S. climate data, fearing it might vanish under Trump

Alarmed that decades of crucial climate measurements could vanish under a hostile Trump administration, scientists have begun a feverish attempt to copy reams of government data onto independent servers in hopes of safeguarding it from any political interference.
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economist.com August 13, 2015
We’ll see you, anon

FREQUENT visitors to the Hustler Club, a gentlemen’s entertainment venue in New York, could not have known that they would become part of a debate about anonymity in the era of “big data”.
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1,035 saves
code.facebook.com November 14, 2014
Introducing data center fabric, the next-generation Facebook data center network

The more than 1.35 billion people who use Facebook on an ongoing basis rely on a seamless, “always on” site performance.
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1,654 saves
forbes.com May 28, 2013
A Very Short History Of Data Science

The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one–computer science.
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1,310 saves
venturebeat.com April 1, 2014
Data scientists need their own GitHub. Here are four of the best options

Imagine if a company’s three highly valued data scientists can happily work together without duplicating each other’s efforts and can easily call up the ingredients and results of each other’s previous work. That day has come.
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742 saves
blog.districtdatalabs.com December 29, 2016
Data Exploration with Python, Part 1

Exploratory data analysis (EDA) is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with.
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478 saves
minimaxir.com October 2, 2015
How to Analyze Every Reddit Submission and Comment, in Seconds, for Free

While working on my statistical analysis of 142 million Reddit submissions last year, I had a surprising amount of trouble settings things up. It took a few hours to download the 40+ gigabytes of compressed data, and another few hours to parse the data and store in a local database.
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465 saves
auth0.com March 23, 2016
Introduction to Immutable.js and Functional Programming Concepts

Functional programming has been on the rise the last few years. Languages such as Clojure, Scala and Haskell have brought to the eyes of imperative programmers interesting techniques that can provide significant benefits in certain use cases. Immutable.
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1,699 saves
blog.parsely.com March 12, 2015
Lucene: The Good Parts

Before MongoDB, before Cassandra, before “NoSQL”, there was Lucene. Did you know that Doug Cutting wrote the first versions of Lucene in 1999? To put things in context, this was around the time Google was more a research project than an actual trusted application.
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1,243 saves
blog.bigml.com February 28, 2013
Data, Data, Data: Thousands of Public Data Sources

We love data, big and small and we are always on the lookout for interesting datasets. Over the last two years, the BigML team has compiled a long list of sources of data that anyone can use.
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632 saves
hackernoon.com November 14, 2016
Aspiring Data Scientists! Learn the basics with these 7 books!

In the last few years I spent a significant time with reading books about Data Science. I found these 7 books the best. These together are a very valuable source of learning the basics. It drives you through everything, you need to know.
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2,013 saves
code.facebook.com June 2, 2015
Recommending items to more than a billion people

The growth of data on the web has made it harder to employ many machine learning algorithms on the full data sets.
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928 saves
msdn.microsoft.com
The What, Why, and How of Master Data Management

Summary: The recent emphasis on regulatory compliance, SOA, and mergers and acquisitions has made the creating and maintaining of accurate and complete master data a business imperative.
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360 saves
blog.parsely.com May 6, 2015
This Team Used Apache Cassandra… You Won’t Believe What Happened Next

You know that old saying, “If it seems too good to be true, it probably is?” We technologists should probably apply that saying to database vendor claims pretty regularly. In the summer of 2014, the Parse.ly team finally kicked the tires on Apache Cassandra.
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897 saves
blogs.dropbox.com July 14, 2016
Lepton image compression: saving 22% losslessly from images at 15MB/s

We are pleased to announce the open source release of Lepton, our new streaming image compression format, under the Apache license.
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1,540 saves
blog.binaryedge.io September 8, 2015
The Data Science Workflow

When dealing with data, it helps to have a well defined workflow. Specifically, whether we want to perform an analysis with the sole intent of "telling the story" (Data Visualisation/Journalism) or build a system that relies on data to model a certain task (Data Mining), process matters.
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750 saves
kdnuggets.com May 17, 2017
Every Intro to Data Science Course on the Internet, Ranked

A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead.
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841 saves
thinkgrowth.org March 31, 2017
The Startup Founder’s Guide to Analytics

You need analytics. I’m very confident of that, because today, everyone needs analytics. Not just product, not just marketing, not just finance… sales, fulfillment, everyone at a startup needs analytics today.
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2,192 saves
qz.com May 6, 2013
Most data isn’t “big,” and businesses are wasting money pretending it is

Big data! If you don’t have it, you better get yourself some. Your competition has it, after all. Bottom line: If your data is little, your rivals are going to kick sand in your face and steal your girlfriend.
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1,702 saves
hbr.org October 28, 2015
The Best Data Storytellers Aren’t Always the Numbers People

Taking a cue from the rise of data journalism, brands that rely on content marketing are starting to use data to tell stories about their brands, customers and products.
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524 saves
multithreaded.stitchfix.com June 9, 2016
Good Books for All Things Data

One of the greatest benefits of working among a diverse group of data scientists and data engineers at Stitch Fix is how much we can learn from our peers. Usually that means getting ad hoc help with specific questions from the resident expert(s).
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822 saves
signal-to-noise.xyz April 3, 2017
Interesting data structures: the BK-tree

A BK-tree is a tree data structure specialized to index data in a metric space. A metric space is essentially a set of objects which we equip with a distance function $d(a, b)$ for every pair of elements $(a, b)$.
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1,055 saves
hbr.org March 10, 2017
Root Out Bias from Your Decision-Making Process

We’ve all experienced the disappointment of an important decision not going our way.
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961 saves
jenunderwood.com March 14, 2017
My Favorite Public Data Sources

Where to get public data? In this article, I will share a few of my personal favorite public data sources. Combining public data in your own analytics projects can add powerful external context.
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731 saves
blog.udacity.com November 7, 2014
8 Skills You Need to Be a Data Scientist

Interested in landing a job as a data scientist? You’re in good company – a recent article by Thomas Davenport and D.J. Patil in the Harvard Business Review calls ‘data scientist’ the sexiest job of the 21st century.
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1,888 saves
rayli.net
Top 10 data mining algorithms in plain English

Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper.
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5,897 saves
opensource.com November 22, 2016
Python versus R for machine learning and data analysis

Machine learning and data analysis are two areas where open source has become almost the de facto license for innovative new tools.
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273 saves
cloud.google.com
Google BigQuery Public Datasets

A public dataset is any dataset that is stored in BigQuery and made available to the general public. This page lists a special group of public datasets that Google BigQuery hosts for you to access and integrate into your applications.
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670 saves
lifehacker.com February 18, 2014
Four Skills That Will Turn You Into a Spreadsheet Ninja

Spreadsheets are one of the most mystifying pieces of software you'll encounter in your adult life. As scary as they can be, though, you can do an awful lot with just four simple skills.
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10,031 saves