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Dsci 100 Final Exam Study Guide

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DSCI 100 FINAL EXAM STUDY GUIDE

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DSCI 100 FINAL EXAM STUDY GUIDE



different types of data questions - Answers - descriptive, exploratory, predictive,
inferential, causal, and mechanistic

descriptive question - Answers - Question that asks about summarized characteristics
of a data set without interpretation (i.e., report a fact)

ex: How many people live in each province and territory in Canada

exploratory question - Answers - A question that asks if there are patterns, trends, or
relationships within a single data set. Often used to propose hypotheses for future study

ex: Does political party voting change with indicators of wealth in a set of data collected
on 2000 people living in Canada

predictive question - Answers - A question that asks about predicting measurements or
labels for individuals (people or things). The focus is on what things predict some
outcome, but not what causes the outcome.

ex: What political party will someone vote for in the next Canadian election

inferential question - Answers - A question that looks for patterns, trends, or
relationships in a single data set and also asks for quantification of how applicable these
findings are to the wider population.

ex: Does political party voting change with indicators of wealth for all people living in
Canada

causal question - Answers - A question that asks about whether changing one factor will
lead to a change in another factor, on average, in the wider population

ex: Does wealth leading to voting for a certain political party in Canadian elections

mechanistic question - Answers - A question that asks about the underlying mechanism
of the observed patterns, trends, or relationships (i.e., how does it happen)

Summarization - Answers - computing and reporting aggregated values pertaining to a
data set; most often used to answer descriptive questions and help with answering
exploratory questions

, visualization - Answers - plotting data graphically; typically used to answer descriptive
and exploratory questions (can help with all questions)

classification - Answers - predicting a categorical class (sometimes called a label) for an
observation given its other variables (sometimes called features); used to answer
predictive questions

regression - Answers - predicting a quantitative value for a new observation; used to
answer predictive questions

clustering - Answers - finding previously unknown/unlabeled sub groups in a data set;
often used to answer exploratory questions

estimation - Answers - taking measurements for a small number of items from a
population and making a good guess for the average or proportion for the population;
used to answer inferential questions

tabular data - Answers - Data organized in rows and columns.

data frame - Answers - Table-like structure for storing data in R; consist of variables,
observations, and values

1. Each element itself must either be a vector or a list.
2. Each element (vector or list) must have the same length.

observations - Answers - rows; individual objects for which we collect data; all of the
measurements for a given entity

variables - Answers - columns; these are the characteristics/number/quantity that can
be measured

comma separated values (.csv) - Answers - A file format in which a comma is placed
between each data value in the file; loaded using read_csv

function - Answers - special word in R that takes instructions (arguments) and does
something

tidyverse - Answers - a collection of open source R packages that "share an underlying
design philosophy, grammar, and data structures" of tidy data

comments - Answers - using the hash symbol (#)

accessing documentation - Answers - ? symbol; provides an easy way to pull up the
documentation for most functions quickly

path - Answers - where files live on your computer

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