International Conference on Smart Computing and Electronic Ent.docx

International Conference on Smart Computing and Electronic
Enterprise. (ICSCEE2018) ©2018 IEEE
Big Data Visualization: Allotting by R and Python
with GUI Tools
SK Ahammad Fahad
Faculty of Computer and Information Technology
Al-Madinah International University
Shah Alam, Malaysia
[email protected]
Abdulsamad Ebrahim Yahya
Faculty of Computing and Information Technology
Northern Border University
Rafha, KSA
[email protected]
Abstract—A tremendous amount of data comes with a vast
amount of knowledge. Decent use of the persistent information
can assist to overcome provocations and support to establish

further sophisticated judgment. Data visualization techniques
are
authenticated scientifically as thousand times reliable rather
than
textual representation. The premature data visualization system
met some difficulties and there has some solution for handle
this
kind of big quantity of data. Data science used two distinct
languages Python and R to visualize big data undeviatingly.
There also have a lot of tools in operating business. This paper
is
focused on the visualization technique of Python and R. R
appears including the extraordinary visualization library alike
ggplot2, leaflet, and lattice to defeat the provocation of the
extensive volume. Python has several particular libraries for
data
visualization. Commonly they are Bokeh, Seaborn, Altair,
ggplot
and Pygal. Also, with most modern, secure and powerful zero
coding GUI's accessories to describe big data visualization for
genuine recognition with practical determination. Method and

process of visual description of data are significant to recover
specific knowledge from the large-scale dataset.
Keywords—Big Data Visualization; Python Visualization; R
visualization; GUI Visualization; Zero coding Visualization;
Visualization Tools
I. INTRODUCTION
Data visualization narrates the illustration of substance
info in graphical appearance. Information visualization
complies us to identify sampling, propensity, and interrelation.
The human understanding prepares perceived visual data
60,000 times responsive than text. In fact, visible information
estimates for 90 % of the instruction spread to the brain [1]
[5]. Today’s enterprises have entrance to an enormous
quantity of knowledge generated from each within and out of
doors the organization. Knowledge visualization helps to
create a sense of it all. Human movement a specific purpose or
simplifying the complexities of mounds of information doesn't
require the utilization of knowledge visualization, however, in
a way; today's world would probably necessitate it. Scanning
different worksheets, spreadsheets, or reports are ordinary and
wearisome at the best whereas observing charts and graphs is
often sufficient easier on the eyes[4]. With massive
information obtaining bigger and wider, it's competent to
undertake the notion that the utilization of data visualization
can individually continue to grow, to evolve, and to be of
prominent worth. Additionally, though, one approaches the
method and observe of information visualization can have to
be constrained to grow and evolve additionally [2]. The first

benefit of Big Data visualization is that it allows decision-
makers to raise perceive advanced information, nonetheless at
intervals the umbrella-concept, there square measure many
more-specific benefits value reflecting. Suddenly method the
massive information is barely potential by correct data
visualization method. By visualization process, huge
information is obtainable in real time. With the method of
visualization, tremendous amount of data will recognize
information higher through interactivity. It will be thought of
that Big Data visualization method tells a story within Big
Data. Dispatching the data in a universal manner, information
allowing the viewers or purpose to immediately recognizable.
In this paper, Big data visualization techniques are
demonstrated with utmost contemporary and dynamic
computer languages scope by meta-analysis with mapping the
variations of tools. This comparison between available tools
for big data visualization help to non-programmers on the time
to adopt more functional tools.
II. BIG DATA VISUALIZATION
Big Data visualization requires the appearance of data of
regarding any character in a graphical pattern that addresses it
manageable to conjecture and represents. It belongs to the
implementation of further contemporaneous visualization
procedures to demonstrate the connections between data.
These instances curve incessantly from the use of hundreds of
lines, standards, and connects approaching a wider aesthetic
perceptible reproduction of the data. But it goes far behind
standard corporate graphs, histograms and pie charts to
numerous heterogeneous representations like heat maps and
fever charts, empowering decision-makers to examine data
sets to recognize correspondences or accidental trims [5].
Usually, when corporations demand to perform connections
between data, they apply graphs, bars, and charts to do it.
They can also obtain the aid of a variety of colors, phrases,

and figures. Data visualization uses more interactive, graphical
drawings - including personalization and animation - to
represent symbols and build relationships between bits of
knowledge [2].
A defining characteristic of Big Data visualization is scale.
Now enterprises accumulate and collect immense quantities of
data that would take years for a human to read, make
individual sense. But researchers have ascertained that the
human retina can broadcast data to the brain at a velocity of
approximately 10 megabits per second [4]. Big Data
visualization relies on persuasive computer operations to
ingest raw corporate data and prepare it to produce graphical
illustrations that permit humans to catch in and concede
enormous volumes of data in seconds. To do that decision-
maker must be capable to obtain, estimate, embrace and
operate on data in approaching real-time, including Big Data
visualization encourages a process to be qualified to do
exactly that. Big Data visualization procedures offer a secure
and powerful way to [5]:
– data displayed in
graphical form empowers decision-makers to take in
massive volumes of data and gain a recognition of
something it implies quite immediately – far more
instantly than poring over spreadsheets or explaining
logarithmic records.
– time-sequence data usually apprehend
bearings, but spotting biases dropped in data is

particularly difficult to do – particularly when the
origins are distinct and the amount of data is generous.
But the application of suitable Big Data visualization
techniques can make it obvious to recognize these
trends, and in industry terms, a bearing that is spotted
ahead is an occasion that can be performed against.
l connections –
One of the immense concentrations of Big Data
visualization is that allows users to investigate
information sets–not to gain solutions particular
mysteries, but to determine what wonderful
penetrations the data can expose. This can be done by
appending or excluding data collections, shifting scales,
eliminating outliers, and switching visualization
representations. Recognizing earlier conceived
exemplars and associations in data can fit concerns with
a large rival interest.
sent the information to others – An oft-overlooked
specialty of Big Data visualization is that, it presents a
deeply efficient process to reach any perspicacity that it
surfaces to others. That's because it can communicate
application really immediately and in a way that it is
clear to understand: exactly what is needed in both
intrinsic and obvious business offerings.
The human brain has developed to catch in and experience
visual knowledge, and it excels at the visible trim realization.
It is this technique that facilitates humans to spot hints of risk,
as well as to realize human appearances and distinct human
appearances such as family members. Big data visualization
procedures utilize this by proffering data in a visible form so it
can be concocted by this hard-wired human capacity virtually
immediately – rather than, for example, by scientific
investigation that has to be studied and laboriously involved.

The skill with Big Data visualization is deciding the usual
efficient method to visualize the data to surface any
penetrations it may include. In some situations,
uncomplicated business tools before-mentioned as pie charts
or histograms may explain the entire story, but with generous,
various and different data sets further arcane visualization
procedures may be more relevant.
III. CHALLENGES
Conventional visualization instruments have approached
their conclusions when confronted with very extensive
datasets and these data are emerging continuously. Though
there are some enlargements to conventional visualization
propositions they lag behind by distances. The visualization
apparatus should be able to provide us interactive visualization
with as low latency as desirable. To diminish the latency, Use
the preprocessed data, Parallelize Data Processing and
Rendering and Use an ominous middleware will be helpful to
overcome [1].
Big Data visualization apparatus must be able to deal with
semi-structured and unstructured data because big data usually
have this type of composition. It is recognized that to cope
with such enormous volume of data there is a need for
extensive parallelization, which is a provocation in
visualization. The challenge in parallelization algorithm is to
break down the puzzle into such unconventional task that they
can run autonomously.
The task of big data visualization is to identify exceptional
patterns and correspondences. It needs to discreetly choose the
dimensions of data to be reflected, if it reduces dimensions to
make our visualization low then we may end up missing
magnetic originals but if it uses all the dimensions we may end

up having visualization too thick to be beneficial to the users.
For precedent: “Given the general appearances (1-3 million
pixels), visualizing each data purpose can lead to over-
plotting, overlying and may overwhelm user’s perceptual and
cognitive capabilities” [1].
Due to enormous quantity and huge significance of big
data, it becomes difficult to visualize. Most of the
contemporary visualization tool have low representation in
scalability, functionality and rejoinder time. Lots of Systems
have been intended which not only visualizes data but
prepares at the same time. Certain methods use Hadoop and
storage solution and R programming, Python Programming
language as compiler context in the model.
Some other important big data visualization problems are
as follows;
Visible noise: Utmost of the contrivances in the dataset is
extremely relative to respectively. It enhances really difficult
to distribute them.
Information loss: To raise the response time it decreases
dataset discernibility, but drives to information destruction.
High vision perspicacity: Even behind obtaining solicited
standardized output it was restricted by environmental
understanding.
The high rate of image change: If the movement of change
to the image is too high it becomes impracticable to react to
the number.

Fig. 1. Bar chart and Line Chart
High-performance demands: While static visualization,
this circumstance ignored compared to a dynamic
visualization which requires more i.e. high execution.
Real-Time Scalability: is significant to equip users with
visual real-time data and it is also essential to make real-time
determinations based on available data. Nevertheless,
enormous quantities of data would be too comprehensive to
prepare in real-time. Most visualization schemes are only
intended to handle data beneath a particular size because many
data sets are too generous to fit in memory and query large
data could incur high latency. It is stimulating to overcome
restrictions like data connectivity and limited storage and data
processing aptitudes in real time.
Interactive Scalability: is expanding the advantages of data
visualization. Interactive data visualization can help assume
the perspicacity of data quickly and properly. It takes time to
prepare and examine data before visualization, particularly
enormous amounts of data. The visualization arrangement
may even halt for an elongated period of time or collision
while attempting to present huge volumes of data. Estimating
heterogeneous query processing procedures to terabytes while
permitting interactive acknowledgment times is a major open
research predicament today.
IV. VISUALIZE BIG DATA WITH R
R provides some satisfactory visualization library to establish
visualizations including simultaneous data handling. In R

visualization programming amongst libraries; ggplot2, [12]
Fig 2. Box plot Execution
Fig. 3. (a) Correlogram and (b) Heat Map
leaflet, lattice are the most accepted [6]. All the impressions to
generate the standard as well as high-level visualizations in R
Programming with the essential code with the figure.
For visualization procedure for R, all data are taken from
'HistData' package [8], in the other word the 'HistData'
package are the sample data for the segment for visualization
Big Data in R. The 'HistData' [8] package offers a delicate
data collections which are vital and meaningful for evaluating
statistics and data visualization. Determination of the sequence
is to perform certain advantageous for instructional and
research perspective. Exceptional individual contemporary
with new motives for graphics or representation in R. To
represent Big Data in R, this section organized with 9 distinct
type of visualization method. Some are essential and some are
suitable for the particular case of complexity.
A. Bar / Line Chart
Bar Plots are becoming for showing the relation among
increasing totals beyond individual accumulations. Stacked
Plots are practiced for bar plots for different sections. Line
Charts are generally fancied when investigations a trend
spread over a time duration. It also fit plots where the demand
to analyze relevant variations in quantities beyond some

variable like ‘time’ [6]. Line chart explaining the improvement
in air travelers over the distributed time interval. In fig. 1. (a)
Line chat and (b), (c), and (d) is three types of Bar chart.
Fig. 4. Histogram Visualization by R
Below codes are applied to ‘HistData’ [8] to get this
Visualization.
plot(AirPassengers,type="l")
barplot(iris$Petal.Length)
barplot(iris$Sepal.Length,col = brewer.pal(3,"Set1"))
barplot(table(iris$Species,iris$Sepal.Length),col=brewer.pal(3,"
Set1"))
B. Box plot
Box Plot notes five leading numbers- initial starting by
zero, the first quarter in 25%, the average in 50%, third
quarter on 75%% and the last point at 100%. Following code
applied in ‘HisData’, and following 4 unconventional graphic

visualizations is executed. Using the ~ sign, it can reflect
wherewith the measure is over multiple divisions [7]. The
color palette is practiced to produce the diagram (fig. 2.)
engaging and stimulating understand visual perfections.
data(iris) #dataset from HistData
par(mfrow=c(2,2))
boxplot(iris$Sepal.Length,col="red")
boxplot(iris$Sepal.Length~iris$Species,col="red")
oxplot(iris$Sepal.Length~iris$Species,col=heat.colors(3))
boxplot(iris$Sepal.Length~iris$Species,col=topo.colors(3))
C. Correlogram
Correlogram encourages us to visualize the data in
correlation matrices [11]. It's extremely accommodating to
GUI users. Fig. 3. (a) represent the below code.
cor(iris[1:4])
Sepal.LengthSepal.WidthPetal.LengthPetal.Width
Sepal.Length1.0000000 -0.1175698 0.8717538 0.8179411
Sepal.Width -0.1175698 1.0000000 -0.4284401 -
0.3661259
Petal.Length0.8717538 -0.4284401 1.0000000 0.9628654
Petal.Width0.8179411 -0.3661259 0.9628654 1.0000000

D. Heat Map
Heat maps allow data interpretation with the pair of XY
axis while the post dimensions determined by the
concentration of color. It requires proselyting the dataset to a
model construction [7] (fig. 3. (b)). It intention employ
tableplot performing from the tabplot sequence to rapidly
decrease the number of data as presented in fig. 3. (c).
heatmap(as.matrix(mtcars))
image(as.matrix(b[2:7]))
E. Histogram
Histogram is fundamentally a plot that disintegrates the
Fig. 5. (a)Map Visualization and (b) Mosaic Map
data on disagreements and presents the frequency spread of
those containers. It Fig. 5. (a)Map Visualization and (b)
Mosaic Map package replace this split similarly. These
directions are employed standard (mfrow=c(2,5)) lead to
implement complex graphs on the corresponding side to that
concern of clearness [10]. Fig. 4 has the accomplishment
visual data of code below;
library(RColorBrewer)

data(VADeaths)
par(mfrow=c(2,3))
hist(VADeaths,breaks=10, col=brewer.pal(3,"Set3"),main="Set3
3
colors")
hist(VADeaths,breaks=7, col=brewer.pal(3,"Set1"),main="Set1
3
colors")
hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8
colors")
hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8
colors")
F. Map Visualization
The latest erudition toward R holds extraordinary
visualization library Javascript. The leaflet uncomplicated by
open-source JavaScript visualization library for the map. [10].
Fig. 5. (a) Have the visualize result of following code for Map
visualization throw ‘leaflet’ library.
library(magrittr)
library(leaflet)
m <- leaflet() %>%
addTiles() %>%
addMarkers(lng=77.2310, lat=28.6560, popup="The delicious

food of
chandnichowk")
G. Mosaic plots
A mosaic plot (Marimekko diagrams) multidimensional
expansion graphically presents the data for the individual
variable. Also, practiced for two or more qualitative variables
in the area of displaying the related orders [11]. The following
code was represent the human hair and eye color relational
data with their gender in fig. 5 (b).
data(HairEyeColor)
mosaicplot(HairEyeColor)
H. Scatter plot
Scatter plots support for visualizing data efficiently and for
unadulterated data pageant. Matrix of scatter plot can improve
visualization involved variables capping specific. There have
several types of Scatter Plot. In the fig. 6. (a) Matrix type of
Fig. 6. Big Data Visualization by R in (a) Scatter plot and (b)
3D Graphs

Fig. 7. Python Visualization Library
Scatter Plot is shown the basis of code. There have more in
Scatterplot.
plot(iris,col=brewer.pal(3,"Set1"))
I. 3D Graphs
The generous supreme and exceptional inclinations of R in
fact of data visualization are producing 3D sketches (fig. 6.
(b)). One of the 3D representation of data was represented
according the code below with ‘HistData’ sample data.
“data(iris, package=’datasets’)
scatter3d(Petal.Width~Petal.Length+Sepal.Length|Species
data=iris, fit=’linear’, residuals=TRUE, parallel=FALSE
bg=’black’, axis.scales=TRUE, grid=TRUE, ellipsoid=FALSE)”
V. BIG DATA VISUALIZATION BY PYTHON
Primary determinations of Python for visualization method
in Big Data for its reliability among developers from a wide
scope of specialties. Invariably, all of the segments distribute
extensive amounts of data and presenting that information in
an obvious way. Python operates distinctive library for several
standards data and adjusted visualization method. Few

outstanding are noted in fig. 7. Independently those
visualization archives have its specific naive characteristics.
Determined by the conditions, distinct visualization library
may be decided for execution. Furthermore, there has some
library these are performed beside depend on the help of
additional libraries. Seaborn is an analytical data visualization
framework that works with the support of Matplotlib.
Fig. 8. Bokeh (a) (c) and Altair (b) (d) Sample Visualization.
Among the library, most popular and efficient selected library
was presented with a meta-analysis. Those are; Pygal, ggplot,
Seaborn, Bokeh, and Altair [12].
A. Bokeh
The Bokeh interactive visualization library is focused at
growing interactive graphical illustrations and targets modern
web browsers for presentation[15]. The theories associated
with elegant, concise construction of versatile graphics, and to
extend this capability. Bokeh contain Plot, Glyphs, Guides and
annotations, Ranges, Resources. Bokeh expedites combining
numerous factors of complex plots, which is related to an
associated planning [15]. Sample code for bokeh given below
and its outputs on Fig . 8. (a) (c).
“from bokeh.layouts import gridplot

from bokeh.plotting import figure, output_file, show
x = [1, 2, 3, 4, 5]
y = [23, 15, 7, 12, 21]” #Same for all
“p = figure(title=”Bokeh Demo for OSFY”, x_axis_label=’x’,
y_axis_label=’y’)
p.line(x, y, legend=”Age”, line_width=3)
show(p)” # Fig. 8. (a)
“N = 100
x = np.linspace(0, 4*np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)
y2 = np.sin(x) + np.cos(x)
output_file(“linked_panning.html”)” #same for Fig. 8. (b) (c)
(d)
“s1 = figure(width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color=”blue”, alpha=0.5)
s2 = figure(width=250, height=250, x_range=s1.x_range,
y_range=s1.y_range, title=None)
s2.triangle(x, y1, size=10, color=”firebrick”, alpha=0.5)
s3 = figure(width=250, height=250, x_range=s1.x_range,
title=None)
s3.square(x, y2, size=10, color=”green”, alpha=0.5)

p = gridplot([[s1, s2, s3]], toolbar_location=None)
show(p)” #Fig. 8. (b) (c) (d)
B. Altair
Altair is based on Vega and Vega-Lite, and it is a
declarative mathematical visualization library program for
Python. Declarative mean plotting any chart by declaring links
between data columns to the encoding channels [13]. Altair
facilitates the developer to build classic visualization with
smallest code. Altair is simple, friendly and consistent. It
produces beautiful and effective visualizations with the
minimal amount of code and saves time on setting the legends,
defining axes and so on [13]. Altair has fundamental object,
which takes data-frame as a single argument. Forms to invent
a Streamgraph in below and its output is shown in Fig. 8.(b)(d)
Chart (df).mark_point().encode (x='Item_MRP',
y='Item_Outlet_profit',
colore='Item_type')
C. Seaborn

The Seaborn library based on matplotlib and produces a
high-level interface for drawing charming demographic
graphics in Python. It including close succession besides the
PyData haystack [14]. To advance visualization seaborn have
built-in themes, tools for color pattern, the functions for
visualizing univariate and bivariate, regression models for
independent and dependent variables, matrices of data, statical
time series etc. It intends to explore and experience data. [14].
It grants rights to produce a quality of diagrams. The Hexbin
plot-building reference code is dispensed below and visual in
fig. 9. (a) (b).
x, y = np.random.multivariate_normal(mean, cov, 1000).T
with sns.axes_style(“white”):
sns.jointplot(x=x, y=y, kind=”hex”, color=”k”);
Following source code explained a Violin plot created by

Seaborn. The consequent finger is presented in fig. 8. (d).
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style=”whitegrid”)
df = sns.load_dataset(“brain_networks”, header=[0, 1, 2],
index_col=0)
used_networks = [1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 16, 17]
used_columns = (df.columns.get_level_values(“network”)
.astype(int)
.isin(used_networks))
df = df.loc[:, used_columns]
corr_df = df.corr().groupby(level=”network”).mean()
corr_df.index = corr_df.index.astype(int)
corr_df = corr_df.sort_index().T
f, ax = plt.subplots(figsize=(11, 6))
sns.violinplot(data=corr_df, palette=”Set3”, bw=.2, cut=1,
linewidth=1)
ax.set(ylim=(-.7, 1.05))
sns.despine(left=True, bottom=True)
D. Ggplot
Ggplot is a visualization library ggplot2 of R, built-in

function as ggplot2 of R [12]. It performed the plotting based
on Structural Graphics. An ignorant innovation of obtains
ggplot more enduring. Ggplot visualization on sample data
was subsequently and the figure is exhibited in in Fig. 9. (c)
from ggplot import *
ggplot(aes(x=’date’, y=’beef’), data=meat) +
geom_line() +
stat_smooth(colour=’blue’, span=0.2)
Fig. 9. (a)Seaborn Violin plot (b)Seaborn – Hexbin plot
(c)ggplot Sample
Plot (d)Pygal Bar Graph (e)Pygal – Dot chart
E. Pygal
Pygal is visualization library for Python which has 14
distinct varieties of charts for complex prototypes of data [9].
It holds built-in chart style and customizing opportunity with
prospect to configure charts.
Pygal have Line, Bar, Histogram, XY plane, Pie, Radar,
Box, Dot, Funnel, SolidGauge, Gauge, Pyramid, Treemap,
Maps for nearly every variety of data. [9]. An unadulterated
appearance is presented in fig 9. (d). Another code for
developing a dot chart in pygal is finally prepared in
underneath. The figure is exemplified in Fig. 9. (e).
dot_chart = pygal.Dot(x_label_rotation=30)
“dot_chart.title = ‘V8 benchmark results’”

“dot_chart.x_labels = [‘Richards’, ‘DeltaBlue’, ‘Crypto’,
‘RayTrace’,
‘EarleyBoyer’, ‘RegExp’, ‘Splay’, ‘NavierStokes’]”
“dot_chart.add(‘Chrome’, [7473, 8099, 11700, 2651, 6361,
1044, 3797,
9450])”
“dot_chart.add(‘Firefox’, [6395, 8212, 7520, 7218, 12464,
1660, 2123,
8607])”
“dot_chart.add(‘Opera’, [3472, 5810, 1828, 9013, 2933, 4203,
5229,
4669])”
“dot_chart.add(‘IE’, [43, 144, 136, 34,41, 59, 79, 102])”
“dot_chart.render()”
VI. VISUALIZATION TOOLS: ZERO CODING
A. Tableau
Tableau is the most familiar tools for extensive data
visualization in private and corporate both adjustment. It is
including the advanced business comprehension bearings with
association updates and merchandise description.Tableau has
the advantage to generate charts, graphs, maps and plenty of,
particularly visible graphics. Tableau has a desktop

application for obvious analytic. Tableau has the feature to
produce a different resolution for different types of
environment like mobile, web, slide etc.there also have the
option for cloud-hosted a service as additionally for the user
who wants the server resolution. Barclays, Pandora, and Citrix
are the selected customers of Tableau. If the work with R or
JSON, Tableau will facilitate to out. The canvas or dashboard
is easy and ‘drag and drop’ compatible, therefore, it creates a
homely atmosphere in any operating surroundings. Tableau
will connect all information from as very little as a
spreadsheet to as massive as Hadoop, painlessly, and analyze
deeply. Tableau is employed by bloggers, journalists,
researchers, advocates, professors, and students. Tableau
Desktop is free for students and instructors.
B. Infogram
Infogram links their visualizations and infographics to a
period of time massive information. And that’s an enormous
and a straightforward three-step method chooses among

several templates, alter them with further visualizations like
charts, map, pictures and even videos, and those square
measure prepared for visualization. Infogram supports team
accounts for media publishers and for journalists, branded
TABLE I. KEY FEATURE OF ZERO CODING TOOLS
styles for corporations and schoolroom accounts for
instructional projects.
C. ChartBlocks
ChartBlocks is an associate easy-to-use online tool that
needs no committal to writing, and builds visualizations from
spreadsheets, databases… and live feeds. A chart building
wizard will all the magic. Chartblocks essentially will an
equivalent factor that created Windows …
Several Big Data Visualization tools have been evaluated in this
week's paper. While the focus was primarily on R and Python
with GUI tools, new tools are being introduced every day.

Compare and contrast the use of R vs Python and identify the
pros and cons of each.
Please make your initial post and two response posts
substantive. A substantive post will do at least TWO of the
following:
Ask an interesting, thoughtful question pertaining to the topic
Answer a question (in detail) posted by another student or the
instructor
Provide extensive additional information on the topic
Explain, define, or analyze the topic in detail
Share an applicable personal experience
Provide an outside source (for example, an article from the UC
Library) that applies to the topic, along with additional
information about the topic or the source (please cite properly
in APA)
Make an argument concerning the topic.
At least one scholarly source should be used in the initial
discussion thread. Be sure to use information from your
readings and other sources from the UC Library. Use proper
citations and references in your post.

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