DACSS 601 August 2022
The blog posts here are contributed by students enrolled in DACSS 601, Fundamentals of Data Science. The course provides students with an introduction to R and the tidyverse, scientific publishing, and collaboration through GitHub, building a foundation for future coursework. Students also are introduced to general data management and data wrangling skills, with an emphasis on best practice workflows and tidy data management.
Final Project
final-project
snapchat-political-ads
ggplot
dplyr
stringr
lubridate
janitor
Analyzing Snapchat Political Ads in the US in 2020
Final Project
hw2
Lai Wei
dataset
ggplot2
The University of Massachusetts Amherst (UMass Amherst, UMass) is a public research university in Amherst, Massachusetts and the sole public land-grant university in Commonwealth of Massachusetts. It was founded in 1863 as an agricultural college in the beginning. And the department of Mathematics and Statistics is an essential component of various researches and majors in Umass, which provides the student with broad exposure to the most important themes in mathematics and statistics and exposure to computing paradigms (whether in Java, Python, MATLAB, R, and/or SAS).
Final Project
final_project
The Arab Spring was a Revolutionary Social Movement that resulted in protests that in some instances led to civil war such as Syria and Libya, and autocratic leaders stepping down such as Egypt and Tunisia. This Final Project looks at the economic factors in Tunisia and Egypt to see what economic factors were relevant to the Arab Spring happening in Egypt and Tunisia. I also want to explore the how state-owned enterprises affected Egypt and Tunisia’s economy. In this project, I use data from the World Bank and the various economic indicators to explore economic metrics during the Arab Spring. As I am using the package(WDI), to fetch the using an API, there is no CSV file. If anyone wishes to look at the same data or run the same commands, all you have to do is install the (WDI) package.
Final Project - EDA of NIFTY50 Dataset
tidyverse
final_project
Adithya Parupudi
The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX. It is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly owned subsidiary of the NSE Strategic Investment Corporation Limited. NSE Indices had a marketing and licensing agreement with Standard & Poor’s for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty. The NIFTY 50 index has shaped up to be the largest single financial product in India, with an ecosystem consisting of exchange-traded funds (onshore and offshore), exchange-traded options at NSE, and futures and options abroad at the SGX. NIFTY 50 is the world’s most actively traded contract. WFE, IOM and FIA surveys endorse NSE’s leadership position.
Cancer Incidence and Mortality Analysis across States and Age Groups
Final Project
Animesh Sengupta
Census Data
Cancer USPS Data
Exploratory analysis of Cancer incidence and mortality rate against the US population data between the years 2000-2019, grouped by State and Age Groups
Final Project
final
My project aims to utilize some of the skills learned in DACSS 601 (Data Science Fundamentals) on a dataset of my choosing. With said dataset, the next goal is to ask a research question that can be looked into using tools learned within the course, as well as previous knowledge of statistics. The dataset I chose provides data on Formula 1 drivers, races, circuits, results, and more from every championship since 1950. The first task is to think of a question that can be asked to the dataset, knowing what kind of data exists within.
Reasons for Migration in Bangalore (2011)
Final
Migration
Reasons for Migration
Bangalore
Final Project
Final Project
final
tidyverse
summarytools
lubridate
sf
mapview
leaflet
Crime, Drug Usage and Demographics
Final Project - The Women Who Voted For Trump
final project
UMass Poll_April2021
Karen Detter
The common lore of the 2016 presidential election is that Donald Trump’s victory over Hillary Clinton was secured by the support of both privileged and disenfranchised white men trying to “make America great again” by reviving traditional conservative values, including gendered social roles. However, when the dust settled and the official post-election numbers were crunched by the Pew Research Center, this narrative was challenged by the fact that 47% of white women had voted for Trump (Pew Research Center, 2019, p.11). Instead of allying with a fellow female who ostensibly shared at least some of their individual priorities, these women supported a man well-known for his sexism and boorish behavior towards women.
Challenge 6 Instructions
challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_hh
abc_poll
Visualizing Time and Relationships
Challenge 5 Instructions
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Challenge 5 Akhilesh
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Final Project
Final Project
According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.
Challenge 6 Solutions
challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_hh
abc_poll
Visualizing Time and Relationships
Challenge 7 Instructions
challenge_7
hotel_bookings
australian_marriage
air_bnb
eggs
abc_poll
faostat
us_hh
Visualizing Multiple Dimensions
Challenge 7 Will Munson
challenge_7
hotel_bookings
australian_marriage
air_bnb
eggs
abc_poll
faostat
us_hh
Visualizing Multiple Dimensions
Challenge 7 Instructions
challenge_7
hotel_bookings
australian_marriage
air_bnb
eggs
abc_poll
faostat
us_hh
Visualizing Multiple Dimensions
Challenge 5 Solutions
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Challenge 6 Instructions
challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_hh
abc_poll
Visualizing Time and Relationships
Challenge 6 Will Munson
challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_hh
abc_poll
Visualizing Time and Relationships
Challenge 6
challenge_6
air_bnb
tidyverse
ggplot2
summarytools
ggridges
Visualizing Time and Relationships
Challenge 5 Instructions
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Challenge 5
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Challenge 5
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Challenge 5 Instructions
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Challenge 5 Will Munson
challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Homework2
hw2
ActiveDuty_MartialStatus
The data set is about the marital status of the active duty DOD service. The data set focuses on four types of services(Air Force, Marine Corps, Navy and Army). The data set gives us the detailed description of marital status of the DOD service people and divided them on basis of gender and four types of statuses(Single without Children(SWC), Single with Children(SC), Joint Service Marriage(JSM) and Civilian Marriage(CM)). The enrolled people are also divided on the basis of pay grade into 3 groups(Enlisted, Officer and Warrant).
Challenge 4
challenge_4
Adithya Parupudi
All are numeric columns and the last columns have NA values.
Challenge 4
challenge_4
FederalFundsRate
I know nothing about economics but the Federal Funds Rate appears to be set by the government to regulate lending by banks. The Federal Open Markets Committee (FOMC) sets an upper and lower limit target for the Federal Funds Rate. This data set includes a target rate, these upper and lower target limits, and the effective rate (I am assuming these are independent variables). So one observation includes: date, target rate, upper and lower limits, and effective rate). The data set also includes the GDP, Unemployment Rate, and inflation which I am assuming to be dependent variables because they are effected by the federal funds rate.
Challenge 3
challenge_3
Australian_marriage_law
The dimensions of the data set are
158 rows and 6 columns.
Challenge 3
challenge_3
organicpoultry
The dataset used was organicpoultry. It contains information about the quantity of different poultry types for all months across the years 2004 to 2013. Currently the month and year data all fall under a single column. In order to make the data easy to interpret, first, the column with the data of the month and year need to be separated into two columns.
Following which, the data needs to be pivoted in such a manner that the years become columns and the types of poultry become rows. This format will make it easier to select a subgroup within the types of poultry and compare the changes across years.
Following which, the data needs to be pivoted in such a manner that the years become columns and the types of poultry become rows. This format will make it easier to select a subgroup within the types of poultry and compare the changes across years.
Nick Boonstra Challenge 3
challenge_3
boonstra
week_1
In this challenge, I will be tackling the notorious Australian Marriage Data set!
Nick Boonstra Challenge 1 Resubmit
challenge_1
boonstra
week_1
birds
This challenge involves reading in and cleaning up data from the “birds” data set.
challenge 1
challenge_1
railroads_2012_clean_county.csv
This data is separated into three columns: state, county, and total employees and 2,930 rows.
Challenge 2 - Adithya Parupudi
challenge_2
hw3
faostat_cattle_diary.csv
dplyr
tidyverse
Data wrangling: using group() and summarise()
Challenge 2 Instructions
challenge_2
FAO
This dataset comes from the Food and Agriculture Association of the United Nations. They publish country-level data regularly and I am going to be looking at country-level estimates of the number of animals that are raised for livestock. We can see that there are 82116 rows in the livestock data.
Challenge 2
challenge_2
FAOSTAT_cattle_dairy.csv
As we read the data, there are many interesting variables and a lot of observations based on yearly analysis of the various cattle products.
Nick Boonstra Challenge 2
challenge_2
boonstra
week_1
hotels
For today’s challenge, I will be reading in and wrangling data from the “hotel_bookings” dataset.
Challenge 2 Instructions
challenge_2
railroad
question
There are three variable names: ‘state’, ‘county’, and ‘total_employees’.
Challenge 1
challenge_1
railroads
Data set contains the number of railroad employees in the United States in 2012, organized by county. Data was likely gathered reported to labor bureau either by counties or by each railroad station. Each row represents a county. Columns indicate state (or territory), county, and number of employees. There are 2930 counties as shown below:
Challenge 1
challenge_1
tidyverse
statecounty2012
I chose the State & County railroad employment dataset.
Challenge 4
challenge_4
tidyverse
readxl
lubridate
summarytools
debt_in_trillions
I will be working with the debt in trillions dataset.
hw 2
hw2
Lai Wei
dataset
ggplot2
Homework 2: Reading in Data -Read in a dataset from the _data folder in the course blog repository, or choose your own data. If you decide to use one of the datasets we have provided, please use a challenging dataset - check with us if you are not sure. -Clean the data as needed using dplyr and related tidyverse packages. -Provide a narrative about the data set (look it up if you aren’t sure what you have got) and the variables in your dataset, including what type of data each variable is. The goal of this step is to communicate in a visually appealing way to non-experts - not to replicate r-code. -Identify potential research questions that your dataset can help answer
Challenge 3
challenge_3
tidyverse
readxl
dplyr
tidyr
households
I will be working with the households dataset.
No matching items