Uber data analysis github

Built on a high performance rendering engine and designed for large-scale data sets. A free tool that shares dynamic insights about traffic and mobility in cities where Uber operates. A platform that enables engineers and across ATG to quickly inspect, debug, and explore data collected by our self driving cars. Autonomous Visualization System: a fast, powerful, web-based 3D visualization toolkit for building applications from autonomous and robotics data.

Cutting edge technology meets beautiful data visualization. Our Frameworks A suite of open-source visualization frameworks. Get Started. A high-performance WebGL 2 rendering framework for big data visualizations that integrates perfectly with reactive applications.

Reproducible Data Analysis in Jupyter, Part 3/10: Version Control with Git & GitHub

A comprehensive React wrapper for mapbox-gl. Designed to work seamlessly as a basemap for geospatial visualizations. Application Showcase Our frameworks work together to enable world-class user experiences.

Vis Academy Learn from the experts and get started quickly. Get started! Our Components Smaller projects that have been created to solve everyday tasks. A compact, modern, and well documented library targeting the needs of 3D graphics. A suite of framework-independent loaders i.The recent Uber breach saw attackers obtain credentials to a private GitHub repository, which they then used to You forgot to provide an Email Address.

This email address is already registered. Please login. You have exceeded the maximum character limit. Please provide a Corporate E-mail Address. Please check the box if you want to proceed. Is a private repository well-protected from threat actors?

Should enterprises think twice about using services like GitHub for fear of exposing sensitive information? Over the past couple of years, Uber has received a few black eyes when it has come to security.

The news of the latest Uber breach involving a private code repository should remind users that code repositories are often targets for attackers due to developers' sloppy coding practices.

uber data analysis github

We've seen many organizations publish code that included passwords and private keys publically to GitHub. Many people seem to jump the gun when considering this breach.

I've spoken to a few people about this, and Uber wasn't hosting their code on a public version of GitHub. That being said, there are obvious concerns about hosting data on a third-party site without having additional security controls in place.

It's unclear what, if any, controls were in place for Uber's repository and how the hackers obtained access to it.

uber data analysis github

It was reported that the attackers used login credentials found in the repository to access Uber's AWS environment. They were then able to further sift through the AWS infrastructure until they found sensitive data that was valuable enough to sell. Personally, I think this is less of a code repository issue and more of a general security failure because, in this scenario, there were multiple areas of failure that led to the data breach.

First things first: Let's not publish passwords, tokens or encryption keys in software code itself. This is just good practice, and starting there will help to develop a resilient threat model. The same advice goes for both public and private code being stored in repositories. Likewise, when authenticating to both GitHub and AWS, using multifactor authentication for both is not only possible, but highly recommended.

There are risks when using third-party code repositories, as the Uber breach demonstrated, but many third-party providers offer security features that should be utilized.

In this particular instance, it seems that they weren't used, and were possibly ignored. Ask the expert: Want to ask Matt Pascucci a question about security? All questions are anonymous. Container security continues to be a pressing issue as containers and hosts are being used more frequently.

Learn how to keep your enterprise safe Continue Reading. While there are no set rules, there are some security recommendations when it comes to virtual machines running on one host. Learn the best practices Poisoned search results have spread the Zeus Panda banking Trojan throughout Google. Learn what this means, how search engine poisoning works andUsing that dataset we will perform some Analysis and will draw out some insights like what are the top 10 rated videos on YouTube, who uploaded the most number of videos.

By reading this blog you will understand how to handle data sets that do not have proper structure and how to sort the output of reducer. Column 1: Video id of 11 characters. Column 2: uploader of the video Column 3: Interval between the day of establishment of Youtube and the date of uploading of the video.

Column 4: Category of the video. Column 5: Length of the video. Column 6: Number of views for the video.

Column 7: Rating on the video. Column 8: Number of ratings given for the video Column 9: Number of comments done on the videos. Column Related video ids with the uploaded video. You can download the data set from the below link. Youtube Data set. MapReduce deals with Key and Value pairs. Here we can set the key as gender and value as age. In line 5 we are overriding the map method which will run one time for every line.

In line 9 we are taking a condition if we have the string array of length greater than 6 which means if the line or row has at least 7 columns then it will enter into the if condition and execute the code to eliminate the ArrayIndexOutOfBoundsException. In line 10 we are storing the category which is in the 4 th column. In line 12 we are writing the key and value into the context which will be the output of the map method. In line 2 we are overriding the Reduce method which will run each time for every key.

In line 5 we are storing and calculating the sum of the values. In line 7 writes the respected key and the obtained sum as value to the context.By now, the name Uber has become practically synonymous with scandal.

But this time the company has outdone itself, building a Jenga-style tower of scandals on top of scandals that has only now come crashing down. Not only did the ridesharing service lose control of 57 million people's private information, it also hid that massive breach for more than a year, a cover-up that potentially defied data breach disclosure laws.

Uber may have even actively deceived Federal Trade Commission investigators who were already looking into the company for distinct, earlier data breach. On Tuesday, Uber revealed in a statement from newly installed CEO Dara Khosrowshahi that hackers stole a trover of personal data from the company's network in Octoberincluding the names and driver's license information ofdrivers, and worse, the names, email addresses, and phone numbers of 57 million Uber users.

As bad as that data debacle sounds, Uber's response may end up doing the most damage to the company's relationship with users, and perhaps even exposed it to criminal charges against executives, according to those who have followed the company's ongoing FTC woes. It then failed to disclose the attack to the public—potentially violating breach disclosure laws in many of the states where its users reside—and also kept the data theft secret from the FTC.

You cannot lie to investigators in the process of reaching a settlement with them. According to Bloomberg, Uber's breach occurred when hackers discovered that the company's developers had published code that included their usernames and passwords on a private account of the software repository Github.

Those credentials gave the hackers immediate access to the developers' privileged accounts on Uber's network, and with it, access to sensitive Uber servers hosted on Amazon's servers, including the rider and driver data they stole. While it's not clear how the hackers accessed the private Github account, the initial mistake of sharing credentials in Github code is hardly unique, says Jeremiah Grossman, a web security researcher and chief security strategist at security firm SentinelOne.

Programmers frequently add credentials to code to allow it automated access to privileged data or services, and then fail to restrict how and where they share that credential-laden software. He's far more shocked by the reports of Uber's subsequent coverup. Uber's count of 57 million users covers a significant swath of its total user base, which reached 40 million monthly users last year. The company hasn't notified affected users, writing in its statement that it's "seen no evidence of fraud or misuse tied to the incident," and that it's flagged the affected accounts for additional protection.

As for thedrivers whose information was included in the breach, Uber says it's contacting them now, and offering free credit monitoring and identity theft protection. Mass spills of names, phone numbers, and email addresses represent valuable data for scammers and spammers, who can combine those data points with other data leaks for identity theft, or use them immediately for phishing.

The more sensitive driver data that leaked may offer even more useful private information for fraudsters to exploit. All of it contributes to the dreary, steady erosion of the average person's control of their personal information.

But it's Uber, not the average user whose data it spilled, that may face the most severe and immediate consequences. The company has already fired its chief security officer, Joe Sullivan, who previously led security at Facebook, and before that worked as a federal prosecutor. By failing to publicly disclose the breach for over a year, the company has likely violated breach disclosure laws, and should be bracing for hefty fines in many states where its users live, as well as its home state of California, says the University of Minnesota Law School's McGeveran.

In statements on Twitter embedded above, former FTC attorney Whitney Merrill echoed that interpretation of those breach disclosure laws. If the cover-up included making false statements to the FTC during its investigation of the breach—even though it was a separate incident—that could have even more dire consequences. They presumably omit this 57 million person breach from their disclosure to the FTC. Greenberg's reporting on Ukraine's cyberwar including an excerpt from Sandworm has won a Read more.

Senior Writer Twitter. Featured Video. Heads up, iPhone owners. Topics Uber breaches hacks.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. This dataset contains features such as destination, trip distance, and duration that were not available in other sets released before and thoroughly analyzed by others.

The combination of trip distance and duration allows for estimating Uber's revenue for each trip in NYC. In another hand, the pickup and drop-off locations were anonymized and grouped as taxi zones instead of geographic coordinates.

Hack Brief: Uber Paid Off Hackers to Hide a 57-Million User Data Breach

This is a better attempt to preserve data privacybut it precludes the positioning of such locations on a map. Before diving into the data, let me clarify what the term "very large" in the title means.

The data comprises one complete year of tripswith a total of about 31 million entries. The uncompressed file itself is 1. However, some objects will be large enough to require better reasoning about how to efficiently apply transformations to them, from date-time parsing to arithmetic functions.

In the Jupyter notebook associated with this work, I kept some code commented out in the cells as a note of much less efficient ways to achieve the same output. An update is published twice a year. It's noteworthy that on their website the TLC warns about the non-audited nature of the data:. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.

There were very few clearly erroneous entries in the dataset and a small proportion of suspicious cases or anomalies that warrant further internal analysis. These cases are, for example, those with very long distance traveled, but destination still recorded within New York City, or those with average speed slower than walking, but very long duration beyond a reasonable assumption for the amount of time taken to get out of some really bad traffic gridlock, or the unlikely situation of a driver left waiting.

In addition, there was a small proportion of cases with distance and duration equal to zero. Do they represent canceled trips? A small subset actually shows distinct origin and destination zones, indicating that some distance was driven but not recorded. In other cases, the recorded distance was zero, but the trip duration was more than that, even beyond 5 minutes in rarer cases. Are these system errors, fraud?

The suspicious and anomalous data points were not changed, but the trips with a duration greater than 16 hours cases out of nearly 31 million, mostly system errors were removed from the dataset. In addition, the data was censored at exactly days for convenience, which left only cases out. The imputation method chosen for the latter set was the mean distance and duration of their respective origin-destination pair.

The entries with missing destination were left unchanged, although the information from the vast number of complete cases could potentially be used to determine the most probable destination. NYC is probably the largest and most lucrative rideshare market in the world, with a total demand for taxis and for-hire vehicles in of more than million trips per year.

The number of Uber trips per day in NYC is still growing significantly.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The data contains features distinct from those in the set previously released and throughly explored by FiveThirtyEight and the Kaggle community.

Check the Jupyter Notebook in this repository to see the contents of the data. The analysis and visualizations produced in the Jupyter Notebook provide support for the story to be presented in the project's page.

The code is written in a Jupyter Notebook with a Python 2. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Analyzing 1.1 Billion NYC Taxi and Uber Trips, with a Vengeance

Sign up. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

uber data analysis github

Latest commit. Latest commit ae70 Apr 5, This project aims to: visualize Uber's ridership growth in NYC during the period characterize the demand based on identified patterns in the time series estimate the value of the NYC market for Uber, and its revenue growth other insights about the usage of the service attempt to predict the demand's growth beyond [IN PROGRESS] Publication The analysis and visualizations produced in the Jupyter Notebook provide support for the story to be presented in the project's page.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.In early we started an official data visualization team at Uber. Every day, Uber manages billions of GPS locations. Every minute, our platform handles millions of mobile events. The skills of data visualization specialists span from computer graphics to information design, covering creative technology and web platform development as well.

Our team focuses on areas from visual analytics to mapping, and framework development to public-facing data visualizations. Visual analytics mostly consists of abstract data visualizations. This refers to visualization work where the data has no inherent spatial structure. Opposed to this notion is scientific visualization, where visualization depicts data coming from the physical world maps, 3D physical structures, etc. Most visual analytics work in this case relates to reporting, dashboarding, and real-time analytics in charts and networks.

Our team powers the visualization layers on most business insight applications and business data exploration. Our team enforces building reusable components as we create these applications. We recently open sourced react-visa React and D3 -powered visualization library that provides a JSX -based, domain-specific language to compose charts from visual axes, chart types, and other basic visual elements.

Map-based information is one of our biggest and richest assets at Uber. The billions of GPS points handled by our platform every day in real-time pose atypical challenges for real-time mapping visualizations and in-browser, data-dense visualizations. We develop multiple mapping applications tailored to different customers. These folks need to have in-the-moment information of the current supply and demand distribution. Another customer is data science, which needs rich exploratory interfaces for multidimensional data broken down by product, time, and geo.

We build applications for them to slice and dice that information and get insights from our data. Our tech stack for these applications consists on a few libraries that we developed and open sourced.

But all this technology can be used in creative ways as well. A strong part of data visualization is visual storytelling and data art and illustration. There are many creative ways to tell the story of Uber with data visualization.

We continue working on other visual narratives. This area of work has an interesting mix of data journalism paired with data art and illustration that creates challenges. Data handling is as challenging as the work we do for our internal visual exploratory data analysis products, but aesthetics plays an important role—the visual stimulation and human digestibility is often a bigger priority than effective information design techniques.

For example, we started collaborating with the design team to get branded videos for animated maps showing every car on trip with Uber for a full day, a day in the life of Uber. The result is a WebGL application that runs server-side rendering for each frame and compiles it into a video. The application takes care of everything from the data gathering process through Hive to constructing the video with offline rendering techniques. A 3D immersive animated map shows a full day of anonymized Uber trips:.

For some of this work we also developed a framework called luma. This makes luma. At Uber, data is our biggest asset. We generate insight by using data to create visual exploratory data analysis tools, but data exposition of our business metrics also enables managers in all of our cities to make informed decisions about the business. Sign up for our newsletter for updates from the Uber Engineering blog.

Related Articles More from Author. Popular Articles. Forecasting at Uber: An Introduction September 6, April 16,

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