This thread will function as a space to share all the projects from Alteryx Associates during Bootcamp training. It will consist of two aspects that will complement each other. Projects will be posted by a member from each team allowing community members to offer feedback, as well as vote on their favorite projects by liking those posts. ASSOCIATES * Choose a representative to post on behalf of your team * Share your project and any background you'd like in your post, and make sure to @mention each teammate and include files as well as links to data sources you've used (Format example below) Team Number | Date | Team Members -Project Description - Use this space to tell your story about what you wanted to accomplish with your project, what questions you asked, etc -Data Sources used -Files for your workflow * Keep an eye on any responses you get, you might learn some really great stuff from the community at-large COMMUNITY MEMBERS * If you have time and interest, take a look at the projects submitted by the new Associates * If you're impressed, like their post and give them any feedback you have, just make sure to @ mention the team members. This is a great chance to start a dialogue! We hope that this thread will be a fun way for new Associates to get involved in the community and make some connections with all of you that are here already, and we also hope that it allows our thriving community members a chance to meet some new folks at Alteryx! If you have any questions, feel free to reach out to me and I'll be more than happy to answer.

@Jana_Gouda @ritalpatel Project description: We wanted to answer a question everyone thought of at least once... What should we watch tonight? With my teammates, we realized that we have many criteria and so many streaming platform to choose from. So we decided we wanted a model that would rank movies according to our criteria and show us where we could watch the movies. Our criteria can be edited! Databases we used (found on Kaggle): * Movies On Streaming Platforms * Movies on Rotten Tomatoes What should we watch tonight.yxzp

Team 5 | 5/13/2021| @jpowellsd @elainebusby @kgiblin @joesaperstein Project Description: We wanted to analyze data on Colorado 14ers and determine what factors contributed to average visitors at a particular 14er peak. Since most of our group is in Colorado, we also wanted to find the peaks with the least traffic so they could go explore. We looked at distance from Denver and route difficulty as two fields of interest. We determined that distance from Denver had the highest correlation to average number of visitors, so we used the average visitors as the theme and color coded it on the map. It did in fact show that the further from Denver you get, the fewer the visitors. Data Source: The dataset came from Kaggle: Colorado Fourteeners | Kaggle Group5_Project_14ers.yxzp

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New Associate Project Thread

WillM

This thread will function as a space to share all the projects from Alteryx Associates during Bootcamp training. It will consist of two aspects that will complement each other. Projects will be posted by a member from each team allowing community members to offer feedback, as well as vote on their favorite projects by liking those posts.

ASSOCIATES

Choose a representative to post on behalf of your team
Share your project and any background you'd like in your post, and make sure to @mention each teammate and include files as well as links to data sources you've used (Format example below)

Team Number | Date | Team Members
-Project Description - Use this space to tell your story about what you wanted to accomplish with your project, what questions you asked, etc
-Data Sources used
-Files for your workflow
Keep an eye on any responses you get, you might learn some really great stuff from the community at-large

COMMUNITY MEMBERS

If you have time and interest, take a look at the projects submitted by the new Associates
If you're impressed, like their post and give them any feedback you have, just make sure to @ mention the team members. This is a great chance to start a dialogue!

We hope that this thread will be a fun way for new Associates to get involved in the community and make some connections with all of you that are here already, and we also hope that it allows our thriving community members a chance to meet some new folks at Alteryx! If you have any questions, feel free to reach out to me and I'll be more than happy to answer.

Topic of Interest

Community

Accepted answers

All comments

bensilv

Team Dragons | 13/08/2020 | @bensilv , @julia_mateva , @nige77 , @lkoenig

-Project Description:

We wanted to explore data related to Game of Thrones to see if any variables were statistically significant in relation to IMDB rating.

We focused on dialogue and kill count.

-Data Sources used:

1. IMDB episode ratings (Kaggle + manual input)
2. Script breakdown (Kaggle)
3. Character deaths (washington post)

We are posting this mainly because we'd like to see how we could have done this better/correctly when it comes to the predictive analytics. There are additional data points we did not use as we were not sure how to handle them. E.g. death of character importance rating. I'm sure there are huge volumes of data online with extra variables related to episodes.

We hope you at least enjoy the idea!

GOT Rating Analysis.yxzp

RebekahS

First team to upload their Boot Camp workflow! Congrats!!

RenB

Team PawPatrol | 13/08/2020 | @Tom_Lee @MichelleP

-Project Description:

We wanted to discern the best time to adopt a dog from animal shelters around the United States.

-Data Sources were collected from animal shelters from following cities

Louisville, KY
Austin, TX
Sonoma, CA
Los Angeles, CA

paw-patrol-dogs.yxzp

TuvyL

Love this project, @RenB, @Tom_Lee, and @MichelleP! Our community is chock FULL of animal lovers 🙂

cc: @BradWerner, @MarqueeCrew, @Nick612Haylund, @LaurenU

MarqueeCrew

Rosie know how to relax

tamargrey

Team 3 | 13/08/2020 | @travis_brown , @rschaffer , @davidmartin

-Project Description:

We wanted to see where shark attacks occur and be able to suggest beaches that one could avoid to decrease their risk of shark attacks.

-Data Scource:

The Global Shark Attack File dataset found on Kaggle containing information from shark attacks.

Team 3 Workflow.yxzp

RenB

@WingFung also worked on this. Thought I would be able to edit the main post but looks like it isn't allowing it.

miller_aaron

Team 3 | Sep. 14, 2020 | @AlysonW, @bryanchen, @miller_aaron

Project Description:

Where can I find a safe, cheap (<$100 USD) place to stay in NYC, where I get the whole place, it is available more than half of the year, and there are at least 20 user reviews?

Data Sources used:

2019 NYC Airbnb Listings

2014-15 NYC Crimes

Note:

With mismatched time periods, this POC only proves the concept of identifying "safer" areas to stay in a city you may be unfamiliar with. Given the large amount of crime in NYC, we limited crimes to only Felonious Assaults that occurred in Sep.

Airbnb.yxzp

FelixK

@richardtimperlake @IgorK look at our Bootcamp project in which we started to look closer at numbers of migration in relation to GDP of different countries.

Bootcamp Team 2.yxzp

MartyFlanders

Team 5 | 14 September 2020 | @cshutto, @fbrunholi @MartyFlanders

Project Description: We wanted to find what are the three highest counties per state for Covid-19 cases per capita. We often hear about where there are high numbers of cases, but we wondered about the cases per capita for a county.

We found that the counties with the highest cases per capita don't always correlate to the counties with the highest population.

Bootcamp project T5.yxzp

gfilla

Team 4 | September 14, 2020 | @AndreiKarabelnikau @gfilla

Project Description - Understanding usage patterns and trends for the bike sharing service in Chicago (Divvy) . We explored 2019 data which provided observations for each ride / rental along with information on the type of rider such as birth year, gender, and user type. For our project, we sought to answer these questions:

Most popular starting station
Most popular ending station
Differences in trip length by Age, Gender, Customer type
Visualize a heat map of popular bike locations to understand usage patterns
Create a Predictive Model for forecasting rides from each station by month

Data Sources used

Note: This project was originally completed with full 2019 data and reduced to 2H (Q3/Q4 data) due to data size limitations for uploading and sharing to the community. If you are interested in analyzing the full 2019 data set, you can easily download the Q1/Q2 2019 files from the link above and connect them to the Join tool in the workflow.

Team 4 Bootcamp Project.yxzp

oliviaburgener

10/12/2020 | @chase-zieman @ErinColton

Our team chose a data set from the UC Irvine machine learning repository of red and white wines and their corresponding attributes. Within the datasets, there are 12 variables including one on quality. Based on this quality attribute (scale of 1-10), we decided to create a binary classifier of Good (>=7) or Bad. This workflow utilizes data ingestion, feature engineering, the R toolkit, and the python SDK to assess different classification algorithms and their prediction performances (using AUC).

Datasets: https://archive.ics.uci.edu/ml/datasets/wine+quality

bootcamp_wine_workflow_models.yxmd

Sergiy

Team: @Sergiy

- Project description:

Based on solar power plants generation data find out the following:

1. Predict generation for next 2 weeks for both plants

2. Is there any correlation between air temperature and generation productivity

3. Are there solar panels behaving abnormally (e.g. generation was two times less than average that day on that plant)

- Data source used:

kaggle.com

bootcamp_solar_plant_generation_analysis.yxzp

tonygagliardi

Team 1 | 9 November 20 | @brianflynn @tonygagliardi @sstall , @matt_griffis

-Project Description -

For our project we chose to answer the question posed in Weekly Challenge #106: https://community.alteryx.com/t5/Weekly-Challenge/Challenge-106-How-Long-were-the-Lights-On/td-p/142023

Initially our workflow squirreled away from us, we had four nasty formulas in a row just to get the times into a different format, but then we discovered the Date-Time tool. It was pretty much smooth sailing from there. We ended up with two solutions and both are posted below.

-Data Sources used-

Provided by challenge.

-Files for your workflow-

See attached.

challenge_106_Complete_V1.yxmd

challenge_106_Complete_V2.yxmd

AshleyM

Team 3| Dec. 3, 2020| @bonej , @AshleyM

Project Description:

We wanted to look at data to determine which movie streaming service has the best movies based on ratings from Rotton Tomato and IMDb. We then wanted to explore if this changed with age (over 18, and under 18).

Data Sources Used:

MoviesOnStreamingPlatforms from Kaggle

beta bootcamp flow.yxwz

hjimbu

- Project Description : Summarizing Top 100 Best Soccer Players' Nationality and adding columns of "Number of Players", "Average Overall Score" and "Average Value".

- Data Sources used : FIFA 19 complete player dataset (https://www.kaggle.com/karangadiya/fifa19)

workflow.yxmd

data.csv

markandre

Team 7 | 1/14/12021 | Mark Andre Luke Haberer Michelle Walker Steve Tso

Posting our boot camp project here.... hopefully the data comes along....

If not original data can be found here: https://www.kaggle.com/rohanrao/formula-1-world-championship-1950-2020

The goal of the workflow is to predict how a driver may have performed had they continued racing.

I was not able to get this to actually function, but if anyone in the community wants to take a stab please feel free.

F1-Predictive.yxzp

Jan68

2021/01/14

Bootcamp project Analytic application for Forecast of Covid cases in US, specific state is selected by user.

Team:

Artem Pakhomov

Evgeniy Stadnik

Herman Kyrii

Jakub Dvorak II

Jan Curda

CovidCasesForecast.yxzp

AndyNeal

Team 5 | 1/14/2021 | Regina Brown, Andy Neal, William Jette, Frank Lanasa, Andrew Crane

Project Description
- Looking for relationships between poverty levels and voter turnout using state-level data
Data Sources used
- US Census Poverty rates
- US Census Population data
- 2020 General election turnout rates
Files for your workflow
- attached

@reginabrown @AndyNeal @Will_Jette @fjlanasa @AndrewC988

20200309-RegressionScatterplot.yxzp

Boot Camp Project_StatesVotingandPovertyRates_011321.yxzp

Andrew Crane Workflow.yxzp

Poverty_v_Voter_Turnout.yxzp

Voting Turnout Rate vs Poverty Rate.yxzp

JJI

Team Number 1. | Jan. 14, 2021 | @Staon, @TomasS, @VojtechFried, @JJI

- How the opening move influence your change to win in chess

- Output to html and pdf

- 20000 chess matches from Kaggel

BootCampChess.yxzp

mztokyo

@JinLiThong

-Project Description:
UK and US Best Selling Albums

We enjoy music and wanted to compare the best selling/highest ranked albums in the UK and the USA.

-Data Sources used:

USA Best Selling Albums
https://www.digitalmusicnews.com/2018/12/26/100-best-selling-albums-artists-2019/

UK Best Selling Albums
https://www.officialcharts.com/chart-news/the-best-selling-albums-of-all-time-on-the-official-uk-chart__15551/

We are posting this because we would love to learn how to handle the data cleansing with fewer REGEX statements.
Or a completely different way to handle the data.

Lots of Learning opportunities for data preparation and blending.

1. A hyphen in the album name (same as other delimiter)
2. US Artist name included "The". But the UK artist name did not in "The". Ex. "The Beatles" and "BEATLES".
3. UK Artist name was all caps. US Artist name was in Title format
4. Some hidden spaces and delimiters were not easily/automatically found
5. Regex functionality is very powerful but still requires some effort.
6. It takes a little effort to properly word/phrase your search in community.

Interesting observations:
Surprised that the below UK artists did not make the UK Ranking.

Led Zeppelin (four albums in the US), Elton John, Def Leppard, The Rolling Stones, Eric Clapton, AC/DC

There is a large difference between the ranking the artist achieved in the UK and the US.

We hope all of you enjoy and discover some other surprises.

UK and US Best Selling Albums.yxzp

RyanRo

Team 3 | Feb 11th, 2021 | @TylerH @sarahwelch

Project Description: How have the price + quality/rating of red wine trended globally over the last 10 years?

Data Sources:

Global wine price + rating data (Kaggle)
Country-level spatial data (ESRI)

WineQualityAnalysis_vF.yxzp

pcan415

Team 5 | 2/11/2021 | @johnfree , @victoriadimarco , @pcan415 , @Nelson8

-Project Description:

Challenge #15: Warehouse Shipped Miles

We calculated the total shipped miles per item. The products were available from 3 different warehouses, lat/lon data is provided for each warehouse and each store location.

Our goal was to find the total distance travelled in miles for each item based on it being shipped from the closest warehouse

Enjoy!!

Distance_Traveled_workflow.yxzp

gouthamiunnava

@OleksandrVlasov @PavelPankov

Team# 1 | 11/02/2021 | Pavel Pankov, Oleksandr Vlasov and Gouthami Unnava

Project Description:

We extracted the tweets mentioning Alteryx in the last 7 days and did a sentiment analysis on the data

Data Sources:
Twitter data extracted using twitter API

BootCamp Project.yxzp

matthewdemas

Authors: @ElleBain, @GeoffT, @jlazerus, and @matthewdemas
Data Sources:
- Spotify Dataset 1921-2020, 160k+ Tracks
- The Top 25 Metal Sub-Genres According To Spotify…
Question:
- Were More Metal Songs Released during the Trump Administration?
- An investigation into the qualities of music released during the previous president's time in office (as of February 2021).
Workflow (attached)

spotify_metal_music_analysis.yxzp

jvansickle

Team 4 | 03/11/2020 | @Gabrie11a, @Angela_Menegassi, @Marootian , @jvansickle

We decided to investigate whether there is a correlation between economic freedom and happiness of a country. We also investigate some specifics of the happiness metrics seeking a more clear indicator of happiness. We also mapped the top 15 happiest countries to visualize whether the happiest countries are or are not in the same region.

We have four separate data sources:

- Happiness Data

- Economic Freedom Data

- GDP per capita

- world_coordinates

Group 4 - Bootcamp.yxzp

marisabaptista

Team 3 | 3.11.2021

This project was completed by @JPKribs, @marisabaptista, @aihua and @DanM-CO.

Between Hulu, Netflix, Disney+, and Amazon Prime, which is the best streaming service based on several personal preferences?

What is the best streaming platform based on the following:

Workstream:

Language requirement
TV vs. movie preference
Genres
Chosen recognized actors/actresses
New content vs classic content
IMDB rating minimum
Family content requirement

Based on the preferences chosen above, the output would reflect the total content that fits your requirements and how well the average content fits your requirements. This analytic app will determine the best streaming service for you!

Group-Project-Final.yxzp

vladimirkroupa

Team 1 - Igor Nikonenko, Jakub Mahnert, Lukas Sladky, Vladimír Kroupa

Sentiment analysis of movie plots

Questions asked:

Given only "famous" movies - movies where at least one of the actors is an academy awards winner:

What are the most common words in all the movie plots?
What are the most common words in movie plots with positive sounding plots?
What are the most common words in movie plots with negative sounding plots

Which movie directors are associated with the most positive sounding plots and which are associated with the most negative sounding ones?
Which movie genres in general contain movies with the most positive sounding plots and which contain the most negative sounding ones?

Main tools used:

Sentiment analysis
Word cloud

Input data:

Wikipedia dataset of movie title transcripts
IMDB ratings dataset
IMDB movies dataset

edward_vetterdrake

# Team 7 4/15/2021:

@edward_vetterdrake, @theglenjamin, @harperschmidt, Haig Douzdjian

# Project Description:

Spotify metadata provides a lot of information about their tracks describing different aspects of songs such as acousticness, liveness, and danceability, among others. We decided to answer the question of "What country has the "danciest" music?".

# Data Sources used:

- SpotifyTopSongsByCountry - May 2020.csv

- Spotify 1.2 M Songs.csv

music_artists_danceability.yxzp

Gentle

Team #9 / Class of 12 Apr 2021 / @stefaniesmith @spencerhong @Shawna_Lehmann8 @Gentle

Description: We were performing exploratory data analysis and wanted to tell a story that involved being able to play with some of the mapping functionality.

Data Source: The US Department of Agriculture - Economic Research Service publishes county-level data

Our Process: In the end, we decided to combine the county level data for poverty and education to come up with maps to display the counties with the best and worst proportions for the percentage of adults who have college degrees, and those who are in poverty.

Results: The top 25 counties with a large percentage of college-educated adults and also high marks of adult poverty were heavily concentrated in the midwest, particularly western Nebraska. The bottom 25 counties were primarily in the south-east, particularly Georgia, Tennessee, and the Carolinas.

Questions/Observations: The data includes detailed splits for ages 0-4, 5-17, and 0-17. However, there is no corresponding breakouts for seniors -- this seems like a major gap in the data where we provide high visibility into youth poverty, but are entirely blind to elderly in the official government data sets.

Bootcamp Team Project.yxmd

HubertusVHaller

Here is our project for the April 2021 Bootcamp.
Thank you @JoaoTiagoP, @vojtav, @Debbie_N, @GeorgiaShearman and @Clara_Stegmaier for your input and inspiration.

Unfortunately I can"t upload all data and workflow, but here are the most important parts.

Countries happiness_Final.yxmd

KatieP

MarcelaAbrach

Using the World Happiness report, we placed countries in a scattered chart with life expectancy and happiness level in the X and Y axis.

Then, using a second data set, we created bubbles for each country, where the size of the bubble represented the meat consumption.

The workflow output has two charts: one with total consumption per country, and one with average consumption per person.

Based on the size and location of the bubbles, we could present data to show that higher meat consumption results in longer life expectancy and higher happiness levels.

The debate will remain on whether increasing meat consumption increases happiness and longevity, or if happy long-living people just happen to eat more meat. In any case, the resulting graph is an asset for meat lovers who can now back up their meat eating habits with actual data. Thank you Alteryx!

Team: @rnues Russ Nues and Marcela Abrach

MeatLoversAreHappierAndLiveLonger.yxzp

meat_consumption.csv

world-happiness-report.csv

andrew_mendoza

Team 3 | 05/13/2021 | @andrew_mendoza_213 @AmandaG @alex_moran
-Project Description - Our team worked on a weekly challenge that we found in the Community! The challenge we picked with analyzing popular baby names by year.
-Data Sources used: We used the names.yxdb file from the Community Weekly Challenge.
-Files for your workflow: Our workflow outputs a Tableau .hyper file which we used to build a dashboard.

Popular Baby Names by Year.yxzp

names.yxdb

mdespirt

Team 2 | May 2021 | @davidmoon @Scott_McLeod @komlan
- Our team explored the Interactive Chart tool and Assisted Modeling tool using datasets to assess the impacts of key demographics on a student's final grade in their Math or Portuguese language class.
-Data Sources were from kaggle: https://www.kaggle.com/impapan/student-performance-data-set

Team 2 Bootcamp Project.yxzp

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