/Onyx-Data

BI Visualizations to the problems in website. All the Visualization can be found at the below link

GNU General Public License v3.0GPL-3.0

Onyx Data Visualization Challenges

BI Visualizations to the problems in website

Marvel Vs DC Dataset

  • This month we are analyzing Marvel Vs DC movies data!
  • We looked at some of the major movies in the DC and Marvel universes, can you answer the question: whose movies are better, DC or Marvel?
  • Acknowledgment : Kaggle Dataset

Structure

Primary

  • ID

Quantitative

  • IMDB_Score
  • Metascore
  • Votes
  • USA_Gross

Categorical

  • Year
  • Movie
  • Genre
  • Rating
  • Director
  • Actor
  • Description
  • Category

Fast Food Restaurant Dataset

  • This month we are analyzing Fast Food Restaurant data! This is a list of 10,000 fast-food restaurants provided by Datafiniti's

  • Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more. You can use this data to:

  • Rank cities with the most and least fast-food restaurants across the U.S. E.g.: Cities with the most and least McDonald's per capita

  • Fast food restaurants per capita for all states

  • Fast food restaurants with the most locations nationally

  • Major cities with the most and least fast food restaurants per capita

  • Small cities with the most fast-food restaurants per capita

  • States with the most and least fast food restaurants per capita

  • The number of fast food restaurants per capita

Structure

Primary

  • Numbers

Spatial

  • Longitude
  • Latitude

categorical

  • Address
  • Categories
  • City
  • Country
  • Name
  • Postal Code
  • Province

Acknowledgment

Kaggle Dataset

Ecommerce Dataset(August)

This month we are analyzing Ecommerce data! Analysis for this dataset could include time series, clustering, classification and more.

Structure

Primary

  • InvoiceNo

Quantitative

  • Quantity
  • UnitPrice

Categorical

  • StockCode
  • Description
  • InvoiceDate
  • CustomerID
  • Country

Acknowledgement

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Spotify Dataset(July)

  • This month we are analyzing the tracks of Spotify!
  • Can we identify what makes a hit track?

Structure

Primary

  • id (Id of track generated by Spotify)

Numerical

  • acousticness (Ranges from 0 to 1)
  • danceability (Ranges from 0 to 1)
  • energy (Ranges from 0 to 1)
  • duration_ms (Integer typically ranging from 200k to 300k)
  • instrumentalness (Ranges from 0 to 1)
  • valence (Ranges from 0 to 1)
  • popularity (Ranges from 0 to 100)
  • tempo (Float typically ranging from 50 to 150)
  • liveness (Ranges from 0 to 1)
  • loudness (Float typically ranging from -60 to 0)
  • speechiness (Ranges from 0 to 1)

Dummy

  • mode (0 = Minor, 1 = Major)
  • explicit (0 = No explicit content, 1 = Explicit content)

Categorical

  • key (All keys on octave encoded as values ranging from 0 to 11, starting on C as 0, C# as 1 and so on…)
  • timesignature (The predicted timesignature, most typically 4)
  • artists (List of artists mentioned)
  • artists (Ids of mentioned artists)
  • release_date (Date of release mostly in yyyy-mm-dd format, however precision of date may vary)
  • name (Name of the song)

Covid-19 Dataset(June)

The complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data. It is updated daily and includes data on confirmed cases, deaths, hospitalizations, testing, and vaccinations as well as other variables of potential interest.

  • Confirmed cases and deaths
  • Hospitalizations and intensive care unit (ICU) admissions
  • Testing for COVID-19
  • Vaccinations against COVID-19
  • Other variables

The data you find here and the data sources

  1. Confirmed cases and deaths: our data comes from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). We discuss how and when JHU collects and publishes this data here. The cases & deaths dataset is updated daily. Note: the number of cases or deaths reported by any institution—including JHU, the WHO, the ECDC and others—on a given day does not necessarily represent the actual number on that date. This is because of the long reporting chain that exists between a new case/death and its inclusion in statistics. This also means that negative values in cases and deaths can sometimes appear when a country corrects historical data, because it had previously overestimated the number of cases/deaths. Alternatively, large changes can sometimes (although rarely) be made to a country’s entire time series if JHU decides (and has access to the necessary data) to correct values retrospectively.
  2. Hospitalizations and intensive care unit (ICU) admissions: our data comes from the European Centre for Disease Prevention and Control (ECDC) for a select number of European countries; the government of the United Kingdom; the Department of Health & Human Services for the United States; the COVID-19 Tracker for Canada. Unfortunately, we are unable to provide data on hospitalizations for other countries: there is currently no global, aggregated database on COVID-19 hospitalization, and our team at Our World in Data does not have the capacity to build such a dataset.
  3. Testing for COVID-19: this data is collected by the Our World in Data team from official reports; you can find further details in our post on COVID-19 testing, including our checklist of questions to understand testing data, information on geographical and temporal coverage, and detailed country-by-country source information. The testing dataset is updated around twice a week.
  4. Vaccinations against COVID-19: this data is collected by the Our World in Data team from official reports.
  5. Other variables: this data is collected from a variety of sources (United Nations, World Bank, Global Burden of Disease, Blavatnik School of Government, etc.). More information is available in our codebook.

Disney Movies 1937 – 2016 Gross Income(May)

May

DashBoard Link

Walt Disney Studios is the foundation on which The Walt Disney Company was built. The Studios has produced more than 600 films since it's debut film, Snow White and the Seven Dwarfs in 1937. While many of its films were big hits, some of them were not. This dataset contains all the movies from 1937 to 2016 that were released by Disney. The data contains 579 Disney movies with six following attributes:

  • Movie Title
  • Release Date
  • Genre
  • MPAA Rating
  • Total Gross
  • Inflation Adjusted Gross

Inspiration

Some of the interesting questions (tasks) which can be performed on this dataset –

  1. Understanding what contributes to success of Disney movies
  2. Identifying gross rate from release date
  3. Analysis of Disney movie releases per year over time
  4. Which category is the most profitable for Disney and do they focus on that category