Contains different types of Data Visualizations, best practices to follow for each case and what type of visualization should be made for different kinds of datasets.
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Chapter 2: Exploring Data with Tables and Graphs
2.2: Histograms
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Chapter 2: Exploring Data with Tables and Graphs
2.3: Graphs that Enlighten and Graphs that Deceive
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Chapter 2: Exploring Data with Tables and Graphs
2.4: Scatterplots, Correlation, and Regression
Topic: Dot Plot Presentation
Student Name: Misbah
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Please Subscribe to this Channel for more solutions and lectures
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Chapter 2: Exploring Data with Tables and Graphs
2.2: Histograms
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 2: Exploring Data with Tables and Graphs
2.3: Graphs that Enlighten and Graphs that Deceive
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 2: Exploring Data with Tables and Graphs
2.4: Scatterplots, Correlation, and Regression
Topic: Dot Plot Presentation
Student Name: Misbah
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
done by : ( ABCD'S &G )
alaa ba-jafar
abrar alshahranii
sahab filfilan
nada alharbi
shahd rajab
Ghadeer suwaimil
I hope that you enjoy and you benefit❤
Let's understand 7QC tool and basic of Graph / Presentation what to use and when to use. It will enable you to apply graph and present your data in more graphical format.
The presentation gives an introduction to statistics and tries to show the importance of statistics for planners. It talks about the various ways in which the data is categorized and also explains on how to select the chart type to be used depending on what kind of information you want to present.
Top 8 Different Types Of Charts In Statistics And Their UsesStat Analytica
Are you confused about various Types Of Charts In Statistics? In this blog, you will get to learn about the various Types Of Charts In Statistics in detail.
Wireless charging using electromagnetic induction, and resonance magnetic coupling. Effects and limitations, cheallenges faced and meathods to overcome. Success Case study. References included.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. “Most of us need to listen to the
music to understand how
beautiful it is. But often that’s
how we present statistics: we just
show the notes, we don’t play the
music.”
- Hans Rosling
“You can have piles of facts and
still fail to resonate. Its not the
information itself that is
important but the emotional part
of that information.”
- Nancy Duarte
5. data visualization
a primary goal of data visualization is to communicate information clearly and efficiently to
users via the statistical graphics, plots, information graphics, tables, and charts selected
the visual representation of data
the purpose of visualization is insight, not pictures
7. Bar Charts
- One of the most common ways to visualize data.
- It’s quick to compare information, revealing highs and lows at a glance.
Bar charts are especially effective when you have numerical data that splits nicely into different categories so you
can quickly see trends within your data.
When to use bar charts: Comparing data across categories.
Examples: Volume of shirts in different sizes, website traffic by origination site, percent of spending by department.
What types of data can be displayed using a bar chart? - Nominal or Ordinal categories.
- Nominal data are categorised according to descriptive or qualitative information such as county of birth, or
subject studied at university.
- Ordinal data are similar but the different categories can also be ranked, for example in a survey people may be
asked to say whether they thought something was very poor, poor, fair, good or very good.
8. Types of Bar Charts
1) Simple bar charts:
The chart is constructed such that the lengths of the different bars are proportional to the size of the category they
represent. The x-axis represents the different categories and so has no scale. In order to emphasise the fact that the
categories are discrete, a gap is left between the bars on the x-axis.
9. Types of Bar Charts
2) Horizontal bar charts:
The chart is constructed so that the bars are horizontal which means that the longer the bar, the larger the category.
This is a particularly effective way of presenting data when the different categories have long titles that would be
difficult to include below a vertical bar, or when there are a large number of different categories and there is
insufficient space to fit all the columns required for a vertical bar chart across the page.
10. Types of Bar Charts
3) Grouped bar charts:
Grouped bar charts are a way of showing information about different sub-groups of the main categories. A separate
bar represents each of the sub-groups (e.g. civic amenity sites) and these are usually coloured or shaded differently
to distinguish between them.
But care needs to be taken to ensure that the chart does not contain too much information making it complicated
to read and interpret.
11. Types of Bar Charts
3) Stacked bar charts:
Stacked bar chars are similar to grouped bar charts in that they are used to display information about the sub-
groups that make up the different categories. In stacked bar charts the bars representing the sub-groups are placed
on top of each other to make a single column, or side by side to make a single bar.
The overall height or length of the bar shows the total size of the category whilst different colours or shadings are
used to indicate the relative contribution of the different sub-groups.
12. Types of Bar Charts
3) Stacked bar charts:
Stacked bar charts can also be used to show the percentage contribution different sub-groups contribute to each
separate category
13. Whats wrong with this Bar Chart?
One rule of data visualization that is broken too often: when it comes to bar charts, the y-axis must begin at zero.
When our eyes interpret bar charts, we are comparing the relative heights of the bars. When we cut the height off
at something greater than zero, it skews this visual comparison, over-emphasizing the difference between the bars
in a way that simply isn't honest.
14. Whats wrong with this Bar Chart?
Problems with the chart:
1) Unnecessary visual clutter of tiny gridlines and
strange chart borders
2) the y-axis isn't labelled (Maybe it's Top Tax Rate, as
noted by the subtitle, but this would be a lot clearer
if the axis itself were labelled)
3) Y-axis is placed on the right-hand size of the visual,
so it's the last thing we see as our eyes scan across
from left to right, making it even less likely that I see
the biggest issue with the graphic:
4) the fact that the y-axis starts at 34%. This makes
the difference between Now (35%) and Jan 1, 2013
(39.6%) appear to be way bigger than it actually is.
17. Line Charts
Line charts connect individual numeric data points. When to use line charts:
Viewing trends in data over time.
Examples: stock price change over a five year period, website page views during a month, revenue growth by
quarter. (Multiple lines can be drawn on the same plot)
18. Line Charts
S.S. Stevens mentioned, we can consider that line graphs can be used only with those categorical variables that:
1. Have an intrinsic order,
2. The change (difference) between consecutive items makes sense, and
3. All the changes between consecutive items have a similar meaning.
A short list of examples of categories that can or cannot be used with a line chart:
- January, February, March (it works, intrinsic order, difference between any two consecutive items is 1 month)
- January, February, September (it doesn’t work, intrinsic order is there, yet the difference between February and
January is 1 month, but between September and February the difference is 7 months)
- Monday, Tuesday, Wednesday (it works, intrinsic order, difference between any two consecutive items is 1 day)
- 1, 2, 3 (it works)
- 2, 1, 3 (it doesn’t work, not ordered)
- 1, 2, 4 (it does not work, order exists, but the difference between consecutive elements is different)
- 3, 2, 1 (it still works, descending order, the difference between consecutive elements is -1)
- Apple, Oranges, Pineapples (doesn’t work, no intrinsic order)
- 1st Oranges, 2nd Apples, 3rd Pineapples (it works, the rank is actually the categorical variable)
- 1, 10, 100, 1000 (it works, logarithmic scale, but does not fit into S.S. Stevens' classification)
- 1, 1/2, 1/3, 1/4 (it also works, fractional scale, but does not fit into S.S. Stevens' classification)
19. Line Charts – Good Practices
1. Right baseline
You can get away with a non-zero baseline in a line graph. With line graphs, we compare the lines to each other more
than their height from the x-axis.
Below left graph shows the original line (blue) on the primary y-axis (ranging from 0 to 20) and the line rescaled onto
a secondary axis (red; with axis ranging from 5 to 20).
The next graph shows the same initial line on the primary axis (blue) and the line rescaled onto a secondary axis (red)
that ranges from 5 to 105.
20. Line Charts – Good Practices
1. Right baseline – Dramatic Effects
21. Line Charts – Good Practices
1. Right baseline – Dramatic Effects
28. Scatter Plots
A scatter plot is a two-dimensional data visualization that uses dots to represent the values obtained for two
different variables - one plotted along the x-axis and the other plotted along the y-axis.
Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to
where you want to focus your investigation efforts further.
For example this scatter plot shows the height and weight of a fictitious set of children.
29. Scatter Plots
Scatter Plots are used when you want to show the relationship between two variables. Scatter plots are sometimes
called correlation plots because they show how two variables are correlated.
In the height and weight example, the chart wasn’t just a simple log of the height and weight of a set of children,
but it also visualized the relationship between height and weight - namely that weight increases as height increases.
Notice that the relationship isn’t perfect, some taller children weight less than some shorter children, but the
general trend is pretty strong and we can see that weight is correlated with height.
30. Scatter Plots
Additionally, the size, shape or color of the dot could represents a third (or even fourth variable). For example, this
chart shows the height and weight data but adds in the information of the gender of the child as the color of the
dot.
32. Box Plots
Sometimes also called Box and Whisker plot are an important way to show distributions of data.
The name refers to the two parts of the
plot:
the box,
which contains the median of the data
along with the 1st and 3rd quartiles
(25% greater and less than the median),
and the whiskers,
which typically represents data within
1.5 times the Inter-quartile Range (the
difference between the 1st and 3rd
quartiles).
34. Box Plots - Interpretation
There Box plots are used to show overall patterns of
response for a group of students.
Observations:
• If box plot is comparatively short – see example
(2). This suggests that overall students have a high
level of agreement with each other.
• If box plot is comparatively tall – see examples (1)
and (3). This suggests students hold quite different
opinions about this aspect or sub-aspect.
• The 4 sections of the box plot are uneven in size –
See example (1). This shows that many students
have similar views at certain parts of the scale, but
in other parts of the scale students are more
variable in their views. The long upper whisker in
the example means that students views are varied
amongst the most positive quartile group, and very
similar for the least positive quartile group.
• Same median, different distribution – See
examples (1), (2), and (3).
36. Pie Charts
Pie charts should be used to show relative proportions – or percentages – of information. That’s it.
They are most commonly misused chart types.
Key conclusion: If you are trying to compare data, leave it to bars or stacked bars. Use pie charts, if you are
comparing percentages or showing proportions, and there are just 2-3 categories to be compared.
37. Pie Charts – The Good
Good things –
• Nice title. Gives up the conclusion. Easier to find meaning in
the viz.
• Total percentages highlighted.
• Clear distinction of American money from the rest of the
world.
• Nice categorical distribution.
• Nice use of colors.
38. Pie Charts – The Bad
Bad things –
• Very bad categorical distribution.
• Very bad color management. Everything looks
highlighted.
• No title
• Labels too small to read. Some categories have no
labels.
• No percentages or proportions to compare.
39. Pie Charts – The Ugly
Ugly things –
• See how Apple’s 19.5% share looks more than
21.2% market share of other category?
• 3-D pie charts are bad.
• This was intentional.
44. Tree Maps
These charts use a series of rectangles, nested within other rectangles, to show hierarchical data as a proportion to
the whole.
As the name of the chart suggests, think of your data as related like a tree: each branch is given a rectangle which
represents how much data it comprises. Each rectangle is then sub-divided into smaller rectangles, or sub-branches,
again based on its proportion to the whole.
Use this when: Showing hierarchical data as a proportion of a whole
45. Tree Maps
This treemap shows all of a
company’s support cases, broken by
case type, and also priority level.
You can see that Document,
Feedback, Support and
Maintenance make up the lion
share of support cases. However, in
Feedback and Support, P1 cases
make up the most number of cases,
whereas most other categories are
dominated by relatively mild P4
cases.
Here we are colouring the
rectangles by a category different
from how they are hierarchically
structured, different from the
previous case.
47. Heat Maps
Heat maps are a great way to compare data across two categories using colour. The effect is to quickly see where
the intersection of the categories is strongest and weakest.
There can be many ways to display heat maps, but they all share one thing in common -- they use colour to
communicate relationships between data values that would be would be much harder to understand if presented
numerically in a spreadsheet.