AI News, Twitter Data Science Interview Questions — Acing the AI Interview

Twitter Data Science Interview Questions — Acing the AI Interview

Twitter has one of the biggest data sets in the world.

Twitter data sets are awesome troves of information and provide great insights.

Working on some Twitter data set and providing valuable insights can be a good portfolio project to showcase.

As a followup, next couple of articles were on how to prepare for these interviews split into two parts, Part 1 and Part 2.

Now onto the Twitter Data Science Questions article… The interview process usually consists of phone interview with the hiring manager.

The questions are usually algorithmic in nature including some machine learning questions, math/application based questions and one system design question around working on a distributed system to deliver high scale machine learning.

What We Learned Analyzing Hundreds of Data Science Interviews

Top data science teams around the world are doing incredible work on some of the most interesting datasets in the world.

With machine learning, and artificial intelligence, top data science teams are changing the way we ingest and process data, and they are coming up with actionable insights that impact the lives of millions.

This has led us to write up a guide to data science jobs and a guide to data science interviews in order to help our students take the next step to an ideal job in the field.

We took it upon ourselves to source data with Glassdoor testimonials of different data science interview questions from a selection of companies whose data science teams are considered world-class.

Here’s an article to help you with statistics and probability questions: How Bayes Theorem, Probability, Logic and Data Intersect Here’s a book to help you with statistics and probability questions: Think Stats, Probability and Statistics for Programmers Here’s an interactive course to help you with statistics and probability questions: Probability and statistics with KhanAcademy

Here’s an article to help you with programming questions: Data science sexiness: Your guide to Python and R, and which one is best Here’s a book to help you with programming questions: Cracking the Programming Interview Here’s an interactive course to help you with programming questions: Intro to Python for Data Science

Here’s an article to help you with business thinking and case studies questions: Tips for Data Scientists: Think Like a Business Executive Here’s a book to help you with business thinking and case studies questions: Data Science for Business Here’s an interactive course to help you with business thinking and case studies questions: Data Analytics for Business

After examining the categories of data science interview questions 500+ data scientists were asked,we decided to look more deeply at a few data science teams we knew were highly respected across the industry –from Google to LinkedIn.

These were large companies that could afford to spend on top data science talent and had a large collection of data science interview reviews, which allowed us to explore their interview process in-depth.

What the data science team at Facebook is doing: The research team at Facebook shares what they’re working on, including an in-depth analysis on what pushes news cycles and how blind people interact with social networking sites.

This was a standard process with a phone call screen, a homework assignment that was timed to be done in two hours (split into SQL analysis and an open-ended problem with a sample dataset), and then an on-site interview series with a mix of technical and behavioral questions.

Technical questions are specific to Uber’s problems for Uber data science interview questions: you’ll be asked to deal with Poisson distributions, time series analysis, and problems related to how a driver should algorithmically accept bookings.

What the data science team at Uber is doing: This piece delves into the day-to-day of data science at Uber with Emi Wang, a current employee, who talks about alternating between writing production code, doing business analysis, and creating models for new projects, including reconciling supply and demand for Geosurge, Uber’s internal engine for surge pricing.,7_KO8,22.htm Twitter’s data science interview process was largely neutral with a response rate of 45% neutral and both 27% positive and 27% negative, and most applicants came in from online applications.

He describes filtering for people who have worked on data problems before with the phone screen, doing a basic data challenge, then an in-house data case crack, followed by four interviews focused on culture fit and ability to communicate with business partners.

The Glassdoor reviews confirm that this is the process in place, with the take-home data challenge being focused on A/B split tests and the significance of certain results and the in-house data challenge focused on statistical modelling.

The Airbnb data science team differentiates itself from other teams in this analysis by caring deeply about how you think about the Airbnb product and when you’ve used it, so be prepared to field questions about your usage of the Airbnb app and what you think about it.

Yelp data science interview questions are fairly standard.  What the data science team at Yelp is working on: This article describes a sample project in which deep learning was used to classify restaurant images to determine whether they were images of food, or of the interior/exterior of the restaurant.

Google’s data science interview questions focus on how well you can slice and dice data.  What the data science team at Google is working on: The “unofficial” Google data science blog shares a wealth of projects that the team is working on, and includes a primer on how to join Google as a data scientist.

What the data science team at JPMorgan is working on: JP Morgan uses Hadoop to take large amounts of customer and transactional data and combine it together with social media mentions to get a complete view of the customers they serve.

You can compare your followers with different personas, demographics, interests and consumer behaviors to see how your brand measures up.  Under the Tweets section, you can find a list of all your Tweets and the number of impressions.

If your Tweets are receiving little engagement, you may want to rethink your subject matter and format, for instance, you may want to add photo or video to your content mix, which tends to generate more engagement.  In the followers dashboard, you can track how your following has increased over the last 30 days, and also how many new followers you’ve received per day.

Top Data Scientists to follow on Twitter

English physicist and mathematician Sir Isaac Newton is credited as saying, “If I have seen further, it is by standing on the shoulders of giants.” The humble metaphor describes Newton’s belief in the importance of prior generations’ work in allowing him to progress so far in his research.

In the late 1960s and early 1970s, statisticians and computer scientists conceptualized the field of data science, perhaps not giving it a formal title, but instead dreamed up the value of synthesizing computer science and statistics for data-based discovery.

His famous deep learning method that formed the basis for his research in the 1980s, back-propagation, wouldn’t be appreciated by the tech industry for another couple of decades.

How Neural Networks Really Work Geoffrey Hinton: Deep learning with multiplicative interactions Reddit AMA with Geoffrey Hinton In the News: Meet the Man Google Hired to Make AI a Reality The meaning of AlphaGo, the AI program that beat a Go champ Social Media and Online: Web Page, Twitter NTSB chief says fully autonomous cars are unlikely.

Considered one of the founding fathers of deep learning, LeCun created one of the earliest bank check recognition systems, and is a leading researcher in computer vision.

This group has given birth to Google’s fleet of self-driving cars, Google Glass, and is now working on a plan to provide global internet access via balloons.

Why you should know who Hadley Wickham is: If you are familiar with the statistical programming language R, chances are you’ve used one of the packages created by Wickham.

Wes McKinney In the News: DataPad emerges to let everyone at your company create and play with charts Cloudera bought DataPad because data scientists need tooling, too Social Media and Online: Web Page, Twitter, LinkedIn Data scientists who have accumulated massive followings on twitter, whose talks attract massive crowds, and whose efforts are popularizing data science.

a new position created by the Obama administration in an effort to “ensure government remains effective and innovative for the American public in our increasingly digital world.” He, along with former Cloudera co-founder Jeff Hammerbacher, coined the term “Data Scientist” in 2008.

Dr. DJ Patil DJ Patil Talks Nerdy To Us In the news: U.S. Traffic Deaths Make the Biggest Leap in 50 Years Patient access to data will bolster precision medicine, cancer moonshot, US Chief Data Scientist DJ Patil says Social Media and Online: Web Page, Twitter, LinkedIn They were running these tests with subsets of users, &

Why you should know who Peter Skomoroch is: Rated as one of the top twitter influencers in data science according to KDnuggets, Skomoroch has amassed a large following on social media.

But more importantly, Skomoroch is the mind that powered the data science team at the world’s most popular professional networking website, LinkedIn, from 2009 to 2013.

The co-founder of Coursera, Chief Scientist at Baidu, and associate professor at Stanford leads the charge in development of free technical courses for data scientists by creating the first-of-its-kind Machine Learning course now offered by Coursera.

Why you should know who Kirk Borne is: His LinkedIn profile boasts a reputation as the “#1 Big Data, #2 Machine Learning, and #13 IOT influencer worldwide,” and as principal data scientist at one of the most prestigious consulting firms in the nation, Booz Allen Hamilton, Kirk Borne is a major player in the Data Science industry.

What you should take a look at: Selling Illusory Joy: Emotions, Big Data and the Coming Retail Renaissance How to NOT FAIL at Big Data in 2 Minutes or Less In the news: Big Data in 2016: Cloudy, with a Chance of Disappointment, Disillusionment, and Disruption Social Media and Online: Web Page, Twitter, LinkedIn First it becomes possible, then it becomes so cheap that anyone can do it…

Formerly the Chief Scientist at, the popular URL shortening service, Mason has since moved on to co-found HackNY, a non-profit educator for the computer scientists and engineers, and a machine intelligence startup, Fast Forward Labs.

Artificial Intelligence (AI) continues to make its way into the world, influencing popular culture (think Steven Spielberg’s “A.I.”, or Disney’s “Big Hero 6”) and becoming a disruptor is a variety of industries.

From customer service chatbots to extremely sophisticated autopilot driving machines, artificial intelligence is undoubtedly making an impact on everything around us.

You have a set amount of inputs (ingredients) designed to produce a repeatable output — apple pie, for example.

Machine learning programs in AI use algorithms to make predictions, and in the case of marketing, suggestions are based on algorithms that hope to target the user’s specific preferences (if website visitor ‘A’ visits pages for kid’s short sleeve shirts an algorithm will email them coupons for kid’s short sleeve shirts).

This could be used to determine target audiences, decide on optimal times to send emails, or segment out groupings for deeper engagement.

More sophisticated programs can decipher speech in various languages, understanding not only the actual vocabulary, but also pulling out context and more hidden meanings.

Structured to be similar to the human brain, this AI model incorporates natural language processing and deep learning to identify faces in photos and analyze handwriting.

more sophisticated form of Natural Language Processing, this concept is focused on the process of stringing words together as well as the way that language is understood through cultural context.

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