Data Mining for Business Intelligence (Shmueli) ISDS 474 CSUF Chapter 3 and 4 – Flashcards
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Data visualization and summary statistics help ______ data
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condense
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DV Supports data cleaning. What are examples of data cleaning?
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Identify missing values, outliers, incorrect values, duplicates
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DV supports exploring, which means you ____ some groups.
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combine
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DV Helps identify ____ variables
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suitable
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give 3 examples of Basic Plots
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Line Graphs, Bar Charts, and Scatterplots
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give 2 examples of Distribution Plots
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Boxplots and Histograms
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A bar chart helps you determine ____
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differences between subgroups
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A _____ might replace a category with a 1 or 0
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dummy variable
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A _____ displays relationship between two numerical variables
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scatterplot. For example, A decreases B increases
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Line Graphs, Bar Charts, and Scatterplots are examples of ___ plots
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basic
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___ plots help determine the potential methods and variable transformations
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distribution
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Boxplots and Histograms are examples of ___ plots
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distribution
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___ graphs are best for time series data
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line
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_____ plots are good for prediction tasks, or supervised learning
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distribution
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Histogram shows the distribution of the ____ variable.
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outcome. For instance, the median house value.
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___ plots are useful for comparing subgroups
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Side-by-side boxplots. For example, the distribution of outcome variable for two neighborhoods
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In a box plot, the top outliers defined as those above ____
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Quartile 3 + 1.5 times the difference of Q3 and Q1
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The wider the box, the greater the ____.
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variation
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____ are graphical displays where color is used to convey information
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Heat Maps
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Heat Maps are used to visualize ___ and ____.
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Correlation and Missing Data
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The correlation coefficient lies between __ and ___.
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+1 and -1
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The closer the correlation is to 1, the ___ the association.
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stronger
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A ____ table for p variables has the SAME number of rows and columns
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correlation
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A ____ table can have DIFFERENT number of columns/variables and of rows/records
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data
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How to build correlation table that looks like a basic heat map
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Highlight all, Data analysis, Home, conditional formatting
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What are some Common methods of pre-processing of data?
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Rescaling, Aggregation, Zooming and Panning, and Filtering
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What does Rescaling do?
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Can often enhance the plot and illuminate relationships
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What is Filtering?
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removing some "noise" from data to focus attention on certain data
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What is Zooming and Panning?
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- reveal patterns and outliers (Google maps - zoom certain areas of interest)
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What is Aggregation?
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temporal scale: by granularity (monthly, weekly), geographical (by zip codes)
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What are two ways of deriving new variables?
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binning and condensing categories
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______ removes crowding and allows a better view of the linear relationship between the two logged-scale variables
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Rescaling
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_______ plot Helps visualize and identify clusters and outliers, detect patterns.
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scatter plot with labeling
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Scatterplots for ____ can sometimes be ineffective
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large observations
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Some alternatives for using scatterplots in large observation are:
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Sampling....Reduce marker size....Breaking data down into subsets....Aggregation.....Jittering
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What is jittering?
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Slightly moving each marker by adding a small amount of noise
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___ are actors and relations between them, like "nodes", "edges"
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Network graphs
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____ plot is multiple scatterplots together for pairwise relationships
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Matrix
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Interactive visualization is often preferred over ___ graphs because all plots are on one screen
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"static"
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____ maps are good for hierarchical large-scale data
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Tree
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In ____ plots, the same record is highlighted in each plot
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Linked
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Bar charts, scatterplots Boxplots, histograms, multiple panels, color added Aggregation methods are examples of ___
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Prediction and Classification
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Line charts - temporal and seasonal aggregations and Zooming and panning are examples of _____ forecasting
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Time series
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Matrix plots / Heatmaps / Aggregation / zooming and panning Map charts / parallel coordinate plots are examples of visualization for ___ learning
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Unsupervised
