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21 Places to Find Free Datasets for Data Science Projects (Shared Article from Dataquest)

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This article was originally written by Vik Paruchuri   For the original source click here .

If you’ve ever worked on a personal data science project, you’ve probably spent a lot of time browsing the internet looking for interesting datasets to analyze. It can be fun to sift through dozens of datasets to find the perfect one, but it can also be frustrating to download and import several CSV files, only to realize that the data isn’t that interesting after all. Luckily, there are online repositories that curate datasets and (mostly) remove the uninteresting ones.

In this post, we’ll walk through several types of data science projects, including data visualization projects, data cleaning projects, and machine learning projects, and identify good places to find datasets for each. Whether you want to strengthen your  data science portfolio  by showing that you can visualize data well, or you have a spare few hours and want to practice your machine learning skills, we’ve got you covered.

But first, let’s answer a couple quick, foundational questions:

What is a dataset?

A dataset, or data set, is simply a collection of data.

The simplest and most common format for datasets you’ll find online is a spreadsheet or CSV format — a single file organized as a table of rows and columns. But some datasets will be stored in other formats, and they don’t have to be just one file. Sometimes a dataset may be a zip file or folder containing multiple data tables with related data.

How are datasets created?

Different datasets are created in different ways. In this post, you’ll find links to sources with all kinds of datasets. Some of them will be machine-generated data. Some will be data that’s been collected via surveys. Some may be data that’s recorded from human observations. Some may be data that’s been scraped from websites or pulled via APIs.

Whenever you’re working with a dataset, it’s important to consider: how was  this  dataset created? Where does the data come from? Don’t jump right into the analysis; take the time to first understand the data you are working with.

Public Data Sets for Data Visualization Projects

A typical data visualization project might be something along the lines of “I want to make an infographic about how income varies across the different states in the US”. There are a few considerations to keep in mind when looking for a good data set for a data visualization project:

  • It shouldn’t be messy, because you don’t want to spend a lot of time cleaning data.
  • It should be nuanced and interesting enough to make charts about.
  • Ideally, each column should be well-explained, so the visualization is accurate.
  • The data set shouldn’t have too many rows or columns, so it’s easy to work with.

A good place to find good data sets for data visualization projects are news sites that release their data publicly. They typically clean the data for you, and also already have charts they’ve made that you can replicate or improve.

1. FiveThirtyEight

assignment data set

FiveThirtyEight  is an incredibly popular interactive news and sports site started by  Nate Silver . They write interesting data-driven articles, like  “Don’t blame a skills gap for lack of hiring in manufacturing”  and  “2016 NFL Predictions” .

FiveThirtyEight makes the data sets used in its articles available online on  Github .

View the FiveThirtyEight Data sets

Here are some examples:

  • Airline Safety  — contains information on accidents from each airline.
  • US Weather History  — historical weather data for the US.
  • Study Drugs  — data on who’s taking Adderall in the US.

2. BuzzFeed

assignment data set

BuzzFeed  started as a purveyor of low-quality articles, but has since evolved and now writes some investigative pieces, like  “The court that rules the world”  and  “The short life of Deonte Hoard” .

BuzzFeed makes the data sets used in its articles available on Github.

View the BuzzFeed Data sets

  • Federal Surveillance Planes  — contains data on planes used for domestic surveillance.
  • Zika Virus  — data about the geography of the Zika virus outbreak.
  • Firearm background checks  — data on background checks of people attempting to buy firearms.

NASA is a publicly-funded government organization, and thus all of its data is public. It maintains websites where anyone can download its  datasets related to earth science  and  datasets related to space . You can even sort by format on the earth science site to find all of the available CSV datasets, for example.

Public Data Sets for Data Processing Projects

Sometimes you just want to work with a large data set. The end result doesn’t matter as much as the process of reading in and analyzing the data. You might use tools like  Spark  or  Hadoop  to distribute the processing across multiple nodes. Things to keep in mind when looking for a good data processing data set:

  • The cleaner the data, the better — cleaning a large data set can be very time consuming.
  • The data set should be interesting.
  • There should be an interesting question that can be answered with the data.

A good place to find large public data sets are cloud hosting providers like  Amazon  and  Google . They have an incentive to host the data sets, because they make you analyze them using their infrastructure (and pay them).

4. AWS Public Data sets

assignment data set

Amazon makes large data sets available on its  Amazon Web Services  platform. You can download the data and work with it on your own computer, or analyze the data in the cloud using  EC2  and Hadoop via  EMR . You can read more about how the program works  here .

Amazon has a page that lists all of the data sets for you to browse. You’ll need an AWS account, although Amazon gives you a  free  access tier for new accounts that will enable you to explore the data without being charged.

View AWS Public Data sets

  • Lists of n-grams from Google Books  — common words and groups of words from a huge set of books.
  • Common Crawl Corpus  — data from a crawl of over 5 billion web pages.
  • Landsat images  — moderate resolution satellite images of the surface of the Earth.

5. Google Public Data sets

assignment data set

Much like Amazon, Google also has a cloud hosting service, called  Google Cloud Platform . With GCP, you can use a tool called  BigQuery  to explore large data sets.

Google lists all of the data sets on a page. You’ll need to sign up for a GCP account, but the first 1TB of queries you make are  free .

View Google Public Data sets

  • USA Names  — contains all Social Security name applications in the US, from 1879 to 2015.
  • Github Activity  — contains all public activity on over 2.8 million public Github repositories.
  • Historical Weather  — data from 9000 NOAA weather stations from 1929 to 2016.

6. Wikipedia

assignment data set

Wikipedia  is a free, online, community-edited encyclopedia. Wikipedia contains an astonishing breadth of knowledge, containing pages on everything from the  Ottoman-Habsburg Wars  to  Leonard Nimoy . As part of Wikipedia’s commitment to advancing knowledge, they offer all of their content for free, and regularly generate dumps of all the articles on the site. Additionally, Wikipedia offers edit history and activity, so you can track how a page on a topic evolves over time, and who contributes to it.

You can find the various ways to download the data on the Wikipedia site. You’ll also find scripts to reformat the data in various ways.

View Wikipedia Data sets

  • All images and other media from Wikipedia  — all the images and other media files on Wikipedia.
  • Full site dumps  — of the content on Wikipedia, in various formats.

Public Data Sets for Machine Learning Projects

When you’re working on a machine learning project, you want to be able to predict a column from the other columns in a data set. In order to be able to do this, we need to make sure that:

  • The data set isn’t too messy — if it is, we’ll spend all of our time cleaning the data.
  • There’s an interesting target column to make predictions for.
  • The other variables have some explanatory power for the target column.

There are a few online repositories of data sets that are specifically for machine learning. These data sets are typically cleaned up beforehand, and allow for testing of algorithms very quickly.

assignment data set

Kaggle  is a data science community that hosts machine learning competitions. There are a variety of externally-contributed interesting data sets on the site. Kaggle has both live and historical competitions. You can download data for either, but you have to sign up for Kaggle and accept the terms of service for the competition.

You can download data from Kaggle by entering a  competition . Each competition has its own associated data set. There are also user-contributed data sets found in the new  Kaggle Data sets  offering.

View Kaggle Data sets View Kaggle Competitions

  • Satellite Photograph Order  — a data set of satellite photos of Earth — the goal is to predict which photos were taken earlier than others.
  • Manufacturing Process Failures  — a data set of variables that were measured during the manufacturing process. The goal is to predict faults with the manufacturing.
  • Multiple Choice Questions  — a data set of multiple choice questions and the corresponding correct answers. The goal is to predict the answer for any given question.

8. UCI Machine Learning Repository

The  UCI Machine Learning Repository  is one of the oldest sources of data sets on the web. Although the data sets are user-contributed, and thus have varying levels of documentation and cleanliness, the vast majority are clean and ready for machine learning to be applied. UCI is a great first stop when looking for interesting data sets.

You can download data directly from the UCI Machine Learning repository, without registration. These data sets tend to be fairly small, and don’t have a lot of nuance, but are good for machine learning.

View UCI Machine Learning Repository

  • Email spam  — contains emails, along with a label of whether or not they’re spam.
  • Wine classification  — contains various attributes of 178 different wines.
  • Solar flares  — attributes of solar flares, useful for predicting characteristics of flares.

assignment data set

Quandl  is a repository of economic and financial data. Some of this information is free, but many data sets require purchase. Quandl is useful for building models to predict economic indicators or stock prices. Due to the large amount of available data sets, it’s possible to build a complex model that uses many data sets to predict values in another.

View Quandl Data sets .

  • Entrepreneurial activity by race and other factors  — contains data from the Kauffman foundation on entrepreneurs in the US.
  • Chinese macroeconomic data  — indicators of Chinese economic health.
  • US Federal Reserve data  — US economic indicators, from the Federal Reserve.

Public Data Sets for Data Cleaning Projects

Sometimes, it can be very satisfying to take a data set spread across multiple files, clean them up, condense them into one, and then do some analysis. In data cleaning projects, sometimes it takes hours of research to figure out what each column in the data set means. It may sometimes turn out that the data set you’re analyzing isn’t really suitable for what you’re trying to do, and you’ll need to start over.

When looking for a good data set for a data cleaning project, you want it to:

  • Be spread over multiple files.
  • Have a lot of nuance, and many possible angles to take.
  • Require a good amount of research to understand.
  • Be as “real-world” as possible.

These types of data sets are typically found on aggregators of data sets. These aggregators tend to have data sets from multiple sources, without much curation. Too much curation gives us overly neat data sets that are hard to do extensive cleaning on.

10. data.world

assignment data set

data.world  describes itself at ‘the social network for data people’, but could be more correctly describe as ‘GitHub for data’. It’s a place where you can search for, copy, analyze, and download data sets. In addition, you can upload your data to data.world and use it to collaborate with others.

In a relatively short time it has become one of the ‘go to’ places to acquire data, with lots of user contributed data sets as well as fantastic data sets through data.world’s partnerships with various organizations includeing a large amount of data from the US Federal Government.

One key differentiator of data.world is the tools they have built to make working with data easier – you can write SQL queries within their interface to explore data and join multiple data sets. They also have SDK’s for R an python to make it easier to acquire and work with data in your tool of choice (You might be interested in reading our  tutorial on the data.world Python SDK .)

View data.world Data sets

11. Data.gov

assignment data set

Data.gov  is a relatively new site that’s part of a US effort towards open government. Data.gov makes it possible to download data from multiple US government agencies. Data can range from government budgets to school performance scores. Much of the data requires additional research, and it can sometimes be hard to figure out which data set is the “correct” version. Anyone can download the data, although some data sets require additional hoops to be jumped through, like agreeing to licensing agreements.

You can browse the data sets on Data.gov directly, without registering. You can browse by topic area, or search for a specific data set.

View Data.gov Data sets

  • Food Environment Atlas  — contains data on how local food choices affect diet in the US.
  • School system finances  — a survey of the finances of school systems in the US.
  • Chronic disease data  — data on chronic disease indicators in areas across the US.

12. The World Bank

assignment data set

The World Bank  is a global development organization that offers loans and advice to developing countries. The World Bank regularly funds programs in developing countries, then gathers data to monitor the success of these programs.

You can browse World Bank data sets directly, without registering. The data sets have many missing values, and sometimes take several clicks to actually get to data.

View World Bank Data sets

  • World Development Indicators  — contains country level information on development.
  • Educational Statistics  — data on education by country.
  • World Bank project costs  — data on World Bank projects and their corresponding costs.

13. /r/datasets

assignment data set

Reddit , a popular community discussion site, has a section devoted to sharing interesting data sets. It’s called the  datasets subreddit , or /r/datasets. The scope of these data sets varies a lot, since they’re all user-submitted, but they tend to be very interesting and nuanced.

You can browse the subreddit  here . You can also see the most highly upvoted data sets  here .

View Top /r/datasets Posts

  • All Reddit submissions  — contains reddit submissions through 2015.
  • Jeopardy questions  — questions and point values from the gameshow Jeopardy.
  • New York City property tax data  — data about properties and assessed value in New York City.

14. Academic Torrents

assignment data set

Academic Torrents  is a new site that is geared around sharing the data sets from scientific papers. It’s a newer site, so it’s hard to tell what the most common types of data sets will look like. For now, it has tons of interesting data sets that lack context.

You can browse the data sets directly on the site. Since it’s a torrent site, all of the data sets can be immediately downloaded, but you’ll need a  Bittorrent  client.  Deluge  is a good free option.

View Academic Torrents Data sets

  • Enron emails  — a set of many emails from executives at Enron, a company that famously went bankrupt.
  • Student learning factors  — a set of factors that measure and influence student learning.
  • News articles  — contains news article attributes and a target variable.

Bonus: Streaming data

It’s very common when you’re building a data science project to download a data set and then process it. However, as online services generate more and more data, an increasing amount is generated in real-time, and not available in data set form. Some examples of this include data on tweets from  Twitter , and stock price data. There aren’t many good sources to acquire this kind of data, but we’ll list a few in case you want to try your hand at a streaming data project.

15. Twitter

assignment data set

Twitter  has a good streaming API, and makes it relatively straightforward to filter and stream tweets. You can get started  here . There are tons of options here — you could figure out what states are the happiest, or which countries use the most complex language. We also recently wrote an article to get you started with the Twitter API  here .

Get started with the Twitter API

assignment data set

Github  has an API that allows you to access repository activity and code. You can get started with the API  here . The options are endless — you could build a system to automatically score code quality, or figure out how code evolves over time in large projects.

Get started with the Github API

17. Quantopian

assignment data set

Quantopian is a site where you can develop, test, and operationalize stock trading algorithms. In order to help you do that, they give you access to free minute by minute stock price data. You could build a stock price prediction algorithm.

Get started with Quantopian

18. Wunderground

assignment data set

Wunderground  has an API for weather forecasts that free up to 500 API calls per day. You could use these calls to build up a set of historical weather data, and make predictions about the weather tomorrow.

Get started with the Wunderground API

Bonus: Personal Data

The internet is full of cool data sets you can work with. But for something truly unique, what about analyzing your own personal data? Here are some popular sites that make it possible to download and work with data you’ve generated.

Amazon allows you to download your personal spending data, order history, and more. To access it,  click this link  (you’ll need to be logged in for it to work) or navigate to the Accounts and Lists button in the top right. On the next page, look for the Ordering and Shopping Preferences section, and click on the link under that heading that says “Download order reports”.

Here is  a simple data project tutorial  that you could do using your own Amazon data to analyze your spending habits.

20. Facebook

Facebook also allows you to download your personal activity data. To access it,  click this link  (you’ll need to be logged in for it to work) and select the types of data you’d like to download.

Here is an example of  a simple data project you could build using your own personal Facebook data .

21. Netflix

Netflix allows you to  request your own data for download , although it will make you jump through a few hoops, and warns the process of collating your data may take 30 days. As of the last time we checked, the data they allow you to download is fairly limited, but it could still be suitable for some types of projects and analysis.

In this post, we covered good places to find data sets for any type of data science project. We hope that you find something interesting that you want to sink your teeth into!

If you do end up building a project, we’d love to hear about it. Please  let us know !

At  Dataquest , our interactive guided projects are designed to help you start building a data science portfolio to demonstrate your skills to employers and get a job in data. If you’re interested, you can  signup and do our first module for free .

If you liked this, you might like to read the other posts in our ‘Build a Data Science Portfolio’ series:

  • Storytelling with data .
  • How to setup up a data science blog .
  • Building a machine learning project .
  • The key to building a data science portfolio that will get you a job .
  • How to present your data science portfolio on Github

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assignment data set

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assignment data set

  • Research and Course Guides
  • Statistics Library Resources

Sample Datasets

Statistics library resources: sample datasets.

  • Getting Started
  • Types of Data
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  • Cite Your Data
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The resources here constitute test, sample, or practice data, or representative samples of live datasets that may be used in teaching and learning statistical analysis techniques. I recommend using these when the actual topic or question of your research is secondary to learning the techniques--if that is not the case, and the substance of the data matters, see our Datasets & Statistics guide or contact our Data Services Librarian. 

  • CORGIS CORGIS is The Collection of Really Great, Interesting, Situated Datasets, compiled by instructors at Virginia Tech.
  • R Datasets A Github repository of datasets available through R packages (the download files will work in any statistical analysis tool). You can see the number of rows, the number of columns, and also how many columns are binary, character, numeric, etc. Includes download (.csv) and documentation links.
  • Dataset and Story Library (DASL) Sample datasets organized by a by a Cornell University statistics professor.
  • Tableau Sample Datasets Practice data collected by Tableau.
  • FiveThirtyEight Our Data page An archive of the data and code behind many of the website's articles and graphics.
  • FuelEconomy.gov Download fuel economy datasets by make, model, other variables.
  • Kaggle Datasets Open dataset repository.
  • Kickstarter Project Data Via our ICPSR membership. Create a free personal account using your St. Thomas email to download.
  • Market Values of College and University Endowments Values of endowment funds at U.S. colleges and universities, with classifications. From the National Association of College and University Business Officers (NACUBO). See also their Research page for related studies.
  • National Database of Childcare Prices Sponsored by the Department of Labor Women's Bureau, the site contains a worksheet of county-level childcare price data from across the U.S. dating back to 2008, along with a Technical Guide (codebook) and associated research.
  • Office of the State Auditor: Municipal Liquor Store Operations Data Annual data on the financial operations of city-owned liquor stores across Minnesota; this site includes raw data files and narrative reports. Includes both quantitative and categorical variables.
  • Sample Sales Data This link is simply a Google search for "sample sales data by store", which will give you a number of possibilities
  • Tableau Public Sample Datasets Tableau Public is a platform for sharing data visualizations made in their desktop software. Sample data for learning. Free personal account needed. If you have the desktop version you can download the underlying datasets from the visualizations.
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6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

  • Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

  • data.boston.gov - City of Boston Open Data
  • MassData - State of Masachussets Open Data
  • data.gov - U.S. Government Open Datasets
  • U.S. Census Bureau - Census Datasets
  • IPUMS.org - Integrated Census & Survey Data from around the World
  • Federal Elections Commission - Campaign Finance & Expenditures
  • Federal Aviation Administration - FAA Data & Research
  • fivethirtyeight.com - Data and Code behind the Stories and Interactives
  • Buzzfeed News
  • Socrata Open Data
  • 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

  • Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
  • Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
  • Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
  • R , using the ggplot2 library or with R's built-in plotting functions.
  • Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

  • Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
  • Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
  • OpenRefine - A free, open source tool for working with messy data.

Programming Tools

  • JavaScript data utilities and/or the Datalib JS library .
  • Pandas - Data table and manipulation utilites for Python.
  • dplyr - A library for data manipulation in R.
  • Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

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How to Analyze a Dataset: 6 Steps

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  • 05 Apr 2017

In the modern world, vast amounts of data are created every day. The World Economic Forum estimates that by 2025, 463 exabytes of data will be created globally every day.

Rich data can be an incredibly powerful decision-making tool for organizations when harnessed effectively, but it can also be daunting to collect and analyze such large amounts of information.

Here’s a deeper look at the data analysis process and how to effectively analyze a dataset.

What Is a Dataset?

A dataset is a collection of data within a database.

Typically, datasets take on a tabular format consisting of rows and columns. Each column represents a specific variable, while each row corresponds to a specific value. Some datasets consisting of unstructured data are non-tabular, meaning they don’t fit the traditional row-column format.

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What Is Data Analysis?

Data analysis refers to the process of manipulating raw data to uncover useful insights and draw conclusions. During this process, a data analyst or data scientist will organize, transform, and model a dataset.

Organizations use data to solve business problems, make informed decisions, and effectively plan for the future. Data analysis ensures that this data is optimized and ready to use.

Some specific types of data analysis include:

  • Descriptive analysis
  • Diagnostic analysis
  • Predictive analysis
  • Prescriptive analysis

Regardless of your reason for analyzing data, there are six simple steps that you can follow to make the data analysis process more efficient.

6 Steps to Analyze a Dataset

1. clean up your data.

Data wrangling —also called data cleaning—is the process of uncovering and correcting, or eliminating inaccurate or repeat records from your dataset. During the data wrangling process, you’ll transform the raw data into a more useful format, preparing it for analysis.

It’s imperative to clean your data before beginning analysis. This is particularly important if you’ll be presenting your findings to business teams who may use the data for decision-making purposes . Teams need to have confidence that they’re acting on a reliable source of information.

2. Identify the Right Questions

Once you’ve completed the cleaning process, you may have a lot of questions about your final dataset. There’s so much potential that can be uncovered through analysis.

Identify the most important questions you hope to answer through your analysis. These questions should be easily measurable and closely related to a specific business problem. If the request for analysis is coming from a business team, ask them to provide explicit details about what they’re hoping to learn, what they expect to learn, and how they’ll use the information. You can use their input to determine which questions take priority in your analysis.

3. Break Down the Data Into Segments

It’s often helpful to break down your dataset into smaller, defined groups. Segmenting your data will not only make your analysis more manageable, but also keep it on track.

For example, if you’re attempting to answer questions about a specific department’s performance, you’ll want to segment your data by department. From there, you’ll be able to glean insights about the group that you’re concerned with and identify any relationships that might exist between each group.

4. Visualize the Data

One of the most important parts of data analysis is data visualization , which refers to the process of creating graphical representations of data. Visualizing the data will help you to easily identify any trends or patterns and obvious outliers.

By creating engaging visuals that represent the data, you’re also able to effectively communicate your findings to key stakeholders who can quickly draw conclusions from the visualizations.

There’s a variety of data visualization tools you can use to automatically generate visual representations of a dataset, such as Microsoft Excel, Tableau, and Google Charts.

5. Use the Data to Answer Your Questions

After cleaning, organizing, transforming, and visualizing your data, revisit the questions you outlined at the beginning of the data analysis process. Interpret your results and determine whether the data helps you answer your original questions.

If the results are inconclusive, try revisiting a previous step in the analysis process. Maybe your dataset was too large and should have been segmented further, or perhaps there’s a different type of visualization better suited to your data.

6. Supplement with Qualitative Data

Finally, as you near the conclusion of your analysis, remember that this dataset is only one piece of the puzzle.

It’s critical to pair your quantitative findings with qualitative information, which you may capture using questionnaires, interviews, or testimonials. While the dataset has the ability to tell you what’s happening, qualitative information can often help you understand why it’s happening.

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The Importance of Data Analysis

Virtually all business decisions made by organizations are informed by some type of data. Because of this, it’s crucial that businesses are able to leverage data that s available to them.

Businesses rely on the insights gained from data analysis to guide a myriad of activities, ranging from budgeting to strategy execution . The importance of data analysis for today’s organizations can't be understated.

Are you interested in improving your data science and analytical skills? Download our Beginner’s Guide to Data & Analytics to discover how you can use data to generate insights and tackle business decisions.

This post was updated on March 8, 2021. It was originally published on April 5, 2017.

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assignment data set

Data Set: Definition, Types, Examples & Public Data Sets

From a young age, we’re all exposed to data. You probably remember seeing data tables in science class as young as elementary school.

Any one of those data tables was probably called a data “set” at some point. Why? Because it’s an easy, intuitive way to speak about data.

But what is a data set really? Can any table be called a set, are there defining criteria? What are the different types of data sets? And how do they work across industries?

Unfortunately, there’s no official definition. Instead, I’ve analyzed 8 use cases to determine how the term “set” is used and provide a wholistic definition.

The use cases are:

  • industry definitions from data leaders such as IBM and Google,
  • linguistic definitions from dictionaries such as Oxford Languages and Webster,
  • technical forums,
  • use by governmental organizations such as Eurostat and data.gov,
  • traditional mathematical textbooks,
  • research papers,
  • healthcare leaders, and
  • my own experience as a financial analyst .

Strictly speaking, a data set is a collection of one or more tables, schemas, points, and/or objects that are grouped together either because they’re stored in the same location or because they’re related to the same subject. That said, in most cases the term simply refers to a table of data on a specific topic.

Don’t forget, you can get the free 67 data skills and concepts checklist to cover all the essentials (including data sets).

One more time: a data set is a collection of one or more tables, schemas, points, and/or objects that are grouped together either because they’re stored in the same location or because they’re related to the same subject.

Let’s break this down.

Most of us are familiar with data tables but less familiar with schemes, points, and objects. In a sentence these are just different formats for representing and storing information. But we’ll define these below under the Data Types section.

What’s important is that a data set can include tables whose contents are totally unrelated … as long as they’re stored in the same place .

To understand why, imagine you’re a database analyst . You manage a host of different tables within your data warehouse. Many of these tables have unrelated information, but they share a similar size. You decide to group them into sets to optimize storage. You’ve just created a data set of unrelated tables!

Nevertheless, unrelated data considered as a set occurs almost exclusively in the context of storage .

In virtually all other cases, data sets are made up of one or more tables that work together to provide information about the underlying subject.

How to Describe a Data Set

We’ve given a formal definition, but this is not usually how I like to describe them. Instead, the best way to describe data sets is as information. Data sets are collections of information that’s all related to the same topic, usually in the form of one table, although there is no limit to the number .

A data set is different from a data warehouse, data lake, and data mill because it focuses on a much narrower topic. For example, imagine you want to investigate the airplane industry. A data warehouse would contain information about transactions, flights, and individual companies. A data set, however, would describe only one of those items.

“Dataset” vs “Data Set”

The correct way to write it is with two words: data set. Much like the terms ice cream, living room, and roller coaster, data set is an open compound word . As one word, “dataset” does not appear in any dictionaries, including Webster.

Moreover, the sense of the term is correct in two stages. It is a set of data, each word carrying its own meaning and creating combined meaning as a whole. Unless a leading English dictionary adapts “dataset” as the correct form, “data set” will persist.

In reality, both are accepted in virtually any professional environment, so don’t get hung up on hitting the space bar!

List of 16 Awesome Public Data Sets

  • Kaggle . Kaggle has a good variety of data sets on machine learning. It requires registration but is worth it.
  • FiveThirtyEight . FiveThirtyEight is a news and sports site with data sets that are available on GitHub.
  • BuzzFeed . BuzzFeed is a news and entertainment site that publishes data used in its articles on GitHub.
  • NASA . NASA Earth observation data and much more is available on its website.
  • Amazon AWS . Amazon’s AWS provides loads of data sets on different topics.
  • Google . Google publishes many data sets on its BigQuery tool.
  • University of California Irvine . UCI is one of the oldest sources of public data sets on the web that covers topics ranging from cars to breast cancer.
  • Quandl . Quandl is a NASDAQ company with loads of financial data from stock prices to global indicators.
  • data.world . data.world is a common source for the famous Makeover Monday data visualization event.
  • Data.gov . Data.gov is the US government’s open data. This one is a must!
  • The World Bank . A great source for world development data.
  • Reddit . Reddit data sets from contributors.
  • Weather Underground . Wunderground allows you to manipulate weather forecast data via its API.
  • Socratas . Another great place for various data sets.
  • Academic Torrents . Academic torrents allows you to download data from academic papers published all over the world.
  • Data Is Plural . A weekly newspaper of insightful data sets.

Types of Data Sets

As explained in the definition section, data sets consist of one or more – tables, – schemas, – points, and/or – objects.

Each of these is a “type” of data set or component of a larger data set. Let’s give an example of each.

A data table consists of columns and rows, where columns represent variables and rows represent records of those variables.

Data Schema

A data schema shows relationships between different data units in a data set. For example, the above table showing color and weight for three cars could be related to another table showing price and purchase date for the same cars. A schema between the two could look like the following:

assignment data set

Data Points

A data point is one atomic unit of data. It can exist alone or within another data unit such as a table. In the car table example, Green and 2 tons are examples of data points.

Data Objects

A data object is a collection of one or more data points that create meaning as a whole. Data objects encompass data tables, arrays, pointers, records, files, sets, and scalar types.

In the hierarchy of data terms, data points are the smallest, data objects are larger, and data sets are larger still.

Common Examples of Data Sets

Common, everyday examples of data sets include:

  • Class schedule
  • Home working schedule
  • Student grades on an exam
  • Transactions on a website
  • Search terms in Google
  • Bank statement
  • Sport match results
  • Athlete statistics
  • Performance reviews

Each of these items represents a small data set in its own respect. All of them are usually shown as single data tables, although they can be stored and represented in multiple objects.

Original vs Aggregate Data Sets

I can say from experience that the leading cause of confusion regarding data sets is not knowing the difference between original and aggregate data sets. Most non-data professionals have a hard time intuitively understanding the difference, which can lead to frustration for specialists.

So what is the difference? An original data set is one that contains the most granular level of detail available in a normalized structure . By granular , I mean there is no way to “split” the data further. The way it is captured is the way it is represented. By normalized , I mean each line consists of one point of each variable for the given record — there is no crossover.

Take for example, this original data set:

It’s original because each line represents the most granular level of detail for each car, which also means it’s normalized.

However, we often see data tables formatted like the following:

This data is not original — it provides information about the original data set by aggregating number and weight at the “color” level of detail.

Common Confusion

The above example is easy to understand in theory, but when we’re dealing with huge databases that consist of complex dimensions, it can be difficult to identify the original set. Moreover, when we’re not familiar with the data set, or there are many data sets in an organization, non-data professionals can find it frustrating to keep track.

This frustration can spill over when data and non-data professionals work together. Imagine two data analysts named Sam and Joe, as well as a marketing professional named James. James asks for data concerning his marketing campaign. Sam provides an aggregate table with information. Sam leaves the company a few days later, but James is having a hard time understanding the data.

When James asks Joe for help, Joe insists on having the original data set during their meeting. However, James provides the table he has. Joe is frustrated because they loose time in the meeting in the absence of the original data set, which could have been avoided if James mentioned it earlier.

There is only one effective response to this challenge. Data professionals need to be sensitive to the perspectives of non-analytical colleagues and non-data professionals need to work at understanding the organization’s different original data sets.

Data Set in Math & Statistics

A data set in math is slightly different than the general definition. A math data set is a collection of numbers than can be described by mean, median, and mode calculations .

How is this different from “general” data sets? Mathematical data sets only have numbers, whereas general sets can have numbers and words, or any other data type for that matter. Strictly speaking, one numeric column in a data table could be considered a mathematical data set.

If you liked this article, fee free to check out more free content at the AnalystAnswers.com homepage !

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Introduction to Constructing Your Dataset

Steps to constructing your dataset.

To construct your dataset (and before doing data transformation), you should:

  • Collect the raw data.
  • Identify feature and label sources.
  • Select a sampling strategy.
  • Split the data.

These steps depend a lot on how you’ve framed your ML problem. Use the self-check below to refresh your memory about problem framing and to check your assumptions about data collection.

Self-check of Problem Framing and Data Collection Concepts

For the following questions, click the desired arrow to check your answer:

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2022-07-18 UTC.

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Protecting inventors and entrepreneurs fuels innovation and creativity, driving advances that can benefit society. as the federal agency that grants patents and registers trademarks, we hold a treasure trove of data. now we’re giving it to you - faster and easier than before..

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Trademark 24 hour box and supplemental

Trademark daily xml file (tdxf) applications, trademark daily xml file (tdxf) assignments, trademark daily xml file (tdxf) trademark trial and appeal board (ttab), patent application multi-page pdf images, patent application single-page tiff images, patent application data/xml, patent application full text data/xml, patent application bibliographic data/xml, patent assignment daily xml (front file).

MATH 1530 Assignment 1 WHO Data Set

  • Mathematics
  • Administering Oracle Fusion Analytics Warehouse
  • Manage Users, Groups, Application Roles, and Data Access

Manage Data Access through Security Assignments

As a security administrator, you need to map data security assignments to users to enable data level access.

Use the Security Assignments tab on the Security page to search for the currently set up data security assignments. You may either search for all records or narrow your search to a specific security context, security value, or user. You can remove a security assignment that you had set up or add new security assignments to a user.

Create a Security Assignment

Delete a security assignment, remove users from a security assignment, manage users for a security assignment, set exclusion rules for security assignments, update security assignments automatically.

Use these instructions to create a security assignment in a specific security context.

  • Sign in to your service.
  • In Oracle Fusion Analytics Warehouse Console , click Security under Service Administration . You see the Security page.
  • On the Security page, click the Security Assignments tab. You see all users who have been granted the security assignments in a specific security context.
  • Click New Assignment .
  • In New Security Assignment, under Select Security Assignments , select a security context, and then search for a security value or select from the displayed list.Move the selected security assignments to the column on the right.
  • Under Select Users , search for a user and select the user and move the user to the column on the right. Users are filtered based on the role associated with that context.
  • Click Add to Cart and then click View Cart .
  • In Security Assignments, click Apply Assignments . You can grant this security assignment to other users as required. Bulk assignments may take some time to process. See the Security Activity tab for details.

Use these instructions to delete a security assignment. When you delete a security assignment, Oracle Fusion Analytics Warehouse removes all users associated with the security assignment.

  • On the Security page, click the Security Assignments tab.
  • Select a security assignment from the displayed list of assignments or search for a security assignment and select it.
  • Click Delete Assignment .

You can revoke the security assignment granted to one or more users.

  • In the security assignment details region, select the users from the displayed list of users or search for and select the users.
  • Click Remove User .
  • In Revoke User Assignment, click Revoke Assignment .

As a security administrator, you can manage users for existing data security assignments. In the Manage Users dialog, you can revoke users for an existing assignment or add new users for that assignment.

  • In the security assignment details region, click Manage Users .
  • Under Add User , search for a user and select the user.
  • Under User , click the Delete icon to revoke the user from the assignment.
  • Click Save .

You can set up data security to exclude access for specific users within a security context for specific security assignments.

For example, you can grant access to all security assignments but the business unit ABC. This enables you to have a single rule for a single user within a security context. You can also remove the indirectly derived security assignments of the specific user. Ensure that the users for whom you want to exclude assignments are members of a group related to the security context. You can automate the application of the security exclusion rules by downloading the DataSecurityExclusionAssignments_csv.zip, making changes, and then uploading it; see Download and Upload Data Security Exclusion Rules .

  • In Oracle Fusion Analytics Warehouse Console , click Security under Service Administration.
  • On the Security page, click Security Assignments , and then click Exclusion Rules .

Set Exclusion Rules for Security Assignments page

As a security administrator, automate the updating of security assignments to effectively manage the regular security assignment changes in your organization.

If you want to automate the insertion and deletion of data in the format of USERNAME, SEC_OBJ_CODE, SEC_OBJ_MEMBER_VAL, Operation (to add or to remove the mapping), then configure the changes in the security assignments to be updated automatically and regularly.

To ensure that the changes in security assignment are updated automatically, you must create a table for the OAX_USER schema in Oracle Autonomous Data Warehouse associated with your Oracle Fusion Analytics Warehouse instance. Ensure that you name the table "CUSTOMER_FAW_CONTENT_AUTOSYNC_ASSIGNMENT". You must seed data into this table regularly with the timestamp in universal time (UTC) format in the "CREATION_DATE" column of the table. The CREATION_DATE column ensures that the same records aren't processed repeatedly and no record is missed. Oracle Fusion Analytics Warehouse periodically scans the synonym (2 hours once), pick up the values, and based on the "CREATION_DATE" criteria, populates the FAW_CONTENT_AUTOSYNC_ASSIGNMENT table in the OAX$INFRA schema in Oracle Autonomous Data Warehouse . Later, Oracle Fusion Analytics Warehouse processes the data and uploads the security assignments as per the FAW_CONTENT_AUTOSYNC_ASSIGNMENT table.

  • In Oracle Autonomous Data Warehouse associated with your Oracle Fusion Analytics Warehouse instance, create the CUSTOMER_FAW_CONTENT_AUTOSYNC_ASSIGNMENT table in OAX_USER schema using the following script: CREATE TABLE CUSTOMER_FAW_CONTENT_AUTOSYNC_ASSIGNMENT ( "USERNAME" VARCHAR2(256 CHAR), "SEC_OBJ_CODE" VARCHAR2(256 CHAR), "SEC_OBJ_MEMBER_VAL" VARCHAR2(4000 CHAR), "OPERATION_TYPE" VARCHAR2(65 CHAR), "CREATION_DATE" TIMESTAMP(6) ); -- Grant access from the schema OAX_USER GRANT SELECT ON CUSTOMER_FAW_CONTENT_AUTOSYNC_ASSIGNMENT TO OAX$INFRA; COMMIT;

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United States Patent and Trademark Office - An Agency of the Department of Commerce

USPTO Patent Assignment Dataset with 2019 data files now available

The latest update to the United States Patent and Trademark Office (USPTO) Patent Assignment Dataset, consisting of full data files from 1970 to 2019, is now available to the public for free download. The dataset is an organized relational database of patent assignment and other related transactions, such as name changes, business mergers, licensing agreements, security interests, and liens. The USPTO first published the dataset in 2014 and updates it annually. We derive this information from parties’ recording of their patent transfers with the USPTO, thus creating a complete history of claimed interests in a patent. With this addition, the dataset now contains detailed information on 8.6 million patent assignments and other transactions since 1970, involving roughly 14.9 million patents and patent applications.

For more information, visit the USPTO’s Patent Assignment Dataset webpage .

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  1. Fun, beginner-friendly datasets

    114.1 s. history Version 2 of 2. There are a lot of datasets on Kaggle, and sometimes it can be hard to find one to get started with. Below, I've pulled together some fun, beginner friendly datasets on a range of topics. Enjoy!

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    A dataset, or data set, is simply a collection of data. The simplest and most common format for datasets you'll find online is a spreadsheet or CSV format — a single file organized as a table of rows and columns. But some datasets will be stored in other formats, and they don't have to be just one file.

  3. 40 sample dataset for data analysis projects

    Assignment. Follow the video and download at least 40 Sample Data sets on your Machine. Put them in a folder. Follow the Web Scraping video and scrap COVID19 Dataset in Excel and save the file.

  4. Find Open Datasets and Machine Learning Projects

    Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion.

  5. Free Public Data Sets For Analysis

    Tableau For Everyone Try Tableau today for beautiful data visualizations. Try Tableau Today Free Government Data Sets State, local, and federal governments rely on data to guide key decisions and formulate effective policy for their constituents.

  6. PDF The USPTO Patent Assignment Dataset: Descriptions and Analysis

    a self-asserted "nature of conveyance" (e.g., assignment, merger, security agreement, or license). Because these assignment data have not been widely used in the research community, we provide here a comprehensive description of the Dataset and explain the institutional details necessary for understanding and using the data.

  7. Statistics Library Resources: Sample Datasets

    Resources for literature reviews and locating data sets for analysis; useful in STAT 220, 314, 320, 333, 360, and 460.

  8. Assignment 2: Exploratory Data Analysis

    Step 1: Data Selection First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from. However, if you would like to investigate a different topic and dataset, you are free to do so.

  9. Patent Assignment Dataset

    The Patent Assignment Dataset contains detailed information on 10.0 million patent assignments and other transactions recorded at the USPTO since 1970, involving roughly 17.8 million patents/applications. Updated annually.

  10. How to Analyze a Dataset: 6 Steps

    A dataset is a collection of data within a database. Typically, datasets take on a tabular format consisting of rows and columns. Each column represents a specific variable, while each row corresponds to a specific value. Some datasets consisting of unstructured data are non-tabular, meaning they don't fit the traditional row-column format.

  11. 26 Datasets For Your Data Science Projects

    Kaggle Titanic Survival Prediction Competition — A dataset for trying out all kinds of basic + advanced ML algorithms for binary classification, and also try performing extensive Feature Engineering. Fashion MNIST — A dataset for performing multi-class image classification tasks based on different categories such as apparels, shoes ...

  12. Data Set: Definition, Types, Examples & Public Data Sets

    Strictly speaking, a data set is a collection of one or more tables, schemas, points, and/or objects that are grouped together either because they're stored in the same location or because they're related to the same subject. That said, in most cases the term simply refers to a table of data on a specific topic.

  13. Trademark Assignment Dataset

    The 2022 update to the Trademark Assignment Dataset contains detailed information on more than 1.29 million assignments and other transactions recorded at the USPTO between March 1952 and January 2023, involving 2.28 million unique trademark properties (an individual application or registration). A working paper describing these data is ...

  14. data.world

    About data.world; Terms & Privacy © 2024; data.world, inc ... Skip to main content

  15. Introduction to Constructing Your Dataset

    To construct your dataset (and before doing data transformation), you should: Collect the raw data. Identify feature and label sources. Select a sampling strategy. Split the data. These steps depend a lot on how you've framed your ML problem. Use the self-check below to refresh your memory about problem framing and to check your assumptions ...

  16. Using pandas and Python to Explore Your Dataset

    Using the pandas Python Library Getting to Know Your Data Displaying Data Types Showing Basics Statistics Exploring Your Dataset Getting to Know pandas' Data Structures Understanding Series Objects Understanding DataFrame Objects Accessing Series Elements Using the Indexing Operator Using .loc and .iloc Accessing DataFrame Elements

  17. Dataset Search

    Dataset Search. Dataset Search. Try coronavirus covid-19 or water quality site:canada.ca. Learn more about Dataset Search.

  18. PDF 14.310x: Data Analysis for Social Scientists Joint, Marginal, and

    of the assignment so that you can print and work through the assignment offline. Good luck! In this problem set we will guide you through different ways of accessing real data sets and how to summarize and describe it properly. First we will go through some of the data that is collected by the World Bank.

  19. USPTO Datasets

    Dataset Categories. Historical patent data files (7); Issued patents (patent grants) (patent grant data) (16) Patent and patent application classification information (current) available bimonthly (odd months) (3) Patent assignment economics data for academia and researchers (8); Patent assignment XML (ownership) text (AUG 1980 - present) (2) Patent official gazettes (1)

  20. 3-1 SmartBook Assignment: Chapter 4 (Sections 4.1 through 4.5)

    1. typical or middle value; where the data values are concentrated. 2. spread of data values or dispersion. 3. symmetrical or skewed. The best measure of central location for a numerical data set when the data set contains outliers is the _____. median. True or false: The arithmetic mean is the average of a data set. TRUE.

  21. S24

    Data Analysis Assignment: Instructions The Excel file accompanying this assignment contains two data sets. The first data set (the "movie ratings" tab in the Excel file) consists of movie ratings of 100 movies released in 2012, alongside ratings of the next movie released by the same director. The ratings of each movie are taken from two websites.

  22. Solved This assignment asks you to perform several

    The data set is called 'Standardized Score Assignment Data Set' and is provided for you in the data set folder for this assignment. Data collected from bus driver applicants is provided. To test new employment measures, 100 bus drivers completed a focus test and This problem has been solved!

  23. MATH 1530 Assignment 1 WHO Data Set (docx)

    For the problems from the e-book, you may type your answers or complete the assignment by neatly handwriting your answers on notebook paper. Make sure you show your work. After you have completed the assignment, submit it to the "Assignments" folder labeled Assignment 1 in Brightspace.

  24. Updated Patent Datasets now available

    The Patent Examination Research Dataset (PatEx) now contains detailed information on over 16.5 million United States patent and Patent Cooperation Treaty (PCT) applications filed with the USPTO through April 2021. The dataset includes information on patent application characteristics, examination and continuation histories, and more.

  25. Manage Data Access through Security Assignments

    As a security administrator, you need to map data security assignments to users to enable data level access. Use the Security Assignments tab on the Security page to search for the currently set up data security assignments. You may either search for all records or narrow your search to a specific security context, security value, or user.

  26. PDF arXiv:2402.13669v1 [cs.CL] 21 Feb 2024

    preservation of historical data for replay (Scialom et al.,2022;Luo et al.,2023b), the computation of parameter importance (Kirkpatrick et al.,2017; Aljundi et al.,2018), or the assignment of distinct neurons to different tasks (Mallya and Lazebnik, 2018). However, fine-tuning LLMs is particularly challenging due to their extensive parameter and

  27. The quantum maxima for the basic graphs of exclusivity are not

    A necessary condition for the probabilities of a set of events to exhibit Bell non-locality or Kochen-Specker contextuality is that the graph of exclusivity of the events contains induced odd cycles with five or more vertices, called odd holes, or their complements, called odd antiholes. From this perspective, events whose graph of exclusivity are odd holes or antiholes are the building blocks ...

  28. USPTO Patent Assignment Dataset with 2019 data files now available

    February 26, 2020. The latest update to the United States Patent and Trademark Office (USPTO) Patent Assignment Dataset, consisting of full data files from 1970 to 2019, is now available to the public for free download. The dataset is an organized relational database of patent assignment and other related transactions, such as name changes ...