1. What is data visualization?
Data Visualization is a form of visual communication. At its core, it’s about encoding aggregations and
representing data visually, in order to gain insight from the data. Data visualization allows for immediate
insight by tapping into our mind’s powerful visual processing system.
A primary goal of data visualization is to communicate information clearly and efficiently via graphs and
charts. Effective visualization helps users analyze and reason about data and evidence. It makes complex
data more accessible, understandable and usable.
Think of all the popular data visualization works out there—they all tell an interesting story. Maybe the
story was to convince you of something. Maybe it was to compel you to action, enlighten you with new
information, or force you to question your own preconceived notions of reality. Whatever it is, the best
data visualization, big or small, helps you see what the data has to say.
2. How do you explain data visualization to a layman?
Data visualization is the process of displaying data in graphical charts so that businesses can find insights
quickly and easily to make better decisions for good outcomes.
3. Why did you choose data visualization?
Data Visualization helps us solve business problems faster or better. They bring some new insights which
often have monetary value associated with it. Visualization often enables problems with the data to
become immediately apparent.
A visualization commonly reveals things not only about the data itself but also about the way it is
collected. With an appropriate visualization, errors and artifacts in the data often jump out at you. For this
reason, visualizations can be invaluable in quality control too.
By practicing data visualization, it gives me an opportunity to make a direct impact to the businesses
bottom line.
4. Why Do We Visualize Data?
Humans respond to and process visual data better than any other type of data. In fact, the human
brain processes images 60,000 times faster than text, and 90 percent of information transmitted to the
brain is visual.
Source
We visualize data to harness the incredible power of our visual system to spot relationships and trends.
Seeing the numbers visually gives us a quick understanding of how business is performing so we can
make data backed decisions.
5. How do you define a dashboard?
A dashboard is a visual display of information that helps us visually display, track and analyze key
performance indicators (KPI) and metrics to monitor the health of a business.
Dashboard is also defined as a visual display of the most important information needed to achieve one or
more objectives that has been consolidated on a single screen so it can be easily monitored and
understood at a glance.
Dashboard gives the ability is to spot the exception, highs and lows and then drilling in to the specifics all
in one place.
6. Briefly explain the process you follow in a data visualization project from start to finish?
- First begin with understanding client business, technical and business requirements.
- Understand the context of the visualization and audience who we intend to build.
- Capture user stories/business problems they are trying to address.
- Understand the data model, data analysis and data preparation.
- Storyboard/sketch initial prototype to solve the above problems.
- Choose an appropriate visual that effectively brings the story live and answer the business questions.
- Remove unnecessary elements, keep it clean and simple.
- Showcase the visualization to stakeholders, seek feedback and iterate.
- Data reconcile to make sure the numbers in the visualization are matching to the numbers in the
source.
- Unit testing, System testing, UAT, approval and deploying to production.
- User training, support and maintenance.
7. How do you choose a visual to represent data?
First, we have to think about what it is that we want our audience to be able to do with the data (function)
and then create a visualization (form) that will most efficiently and most accurately convey the data’s
meaning.
When you display the data visually using the right chart, you'll be able to easily uncover meaningful
patterns, and correlations from a set of otherwise indecipherable numbers.
Below are the four data relationships that are used when visualizing the data:
A comparison tries to set one set of variables apart from another and display how those two variables
interact, like the number of visitors to five competing websites in a single month.
A composition tries to collect different types of information that make up a whole and display them
together, like the search terms that those visitors used to land on your site, or how many of them came
from links, search engines, or direct traffic.
A distribution tries to lay out a collection of related or unrelated information simply to see how it
correlates, if at all, and to understand if there's any interaction between the variables, like the number of
bugs reported during each month of a beta.
A relationship tries to show a connection or correlation between two or more variables through the data
presented, like the market cap of a given stock over time versus overall market trend.
Here is a handy diagram created by Dr.Andrew Abela that can be used to determine the right chart type
to use in your data visualization.
8. What is a Column chart?
A column chart visualizes data as a set of rectangular columns, their lengths being proportional to the
values they represent. The vertical axis shows the values, and the horizontal axis shows the categories
they belong to. In multi-series column charts, values are grouped by categories.
9. What is a Bar chart?
A bar chart visualizes data as a set of rectangular bars, their lengths being proportional to the values they
represent. The horizontal axis shows the values, and the vertical axis shows the categories they belong to.
So, the bar chart is a vertical version of the column chart. In multi-series bar charts, values are grouped
by categories. They are also preferred when the category names are long or also to show ranking.
10.What is a Line chart?
A Line Chart is the most popular type of the data visualization. As a rule, it is used to emphasize trends in
data over equal time intervals, such as months, quarters, fiscal years, and so on.
It displays information as a series of data points called 'markers' connected by straight line segments. The
X axis holds the categories while the Y axis holds the values.
11.What is a Stacked Bar chart?
The Stacked Bar Chart is composed of multiple Bar series stacked horizontally one after another. The
length of each series depends on the value in each data point. Stacked Bar Charts make it easier to follow
the variation of all the variables presented, side by side, and watch the change in their total.
12.What is a Stacked Column Chart?
The Stacked Column Chart comprises several Column series stacked vertically, one on another. Each
series' length is determined by the value in each data point. Stacked Column Charts are a great option if
you need to observe the change in each of several variables simultaneously and in their sum.
You should pick this type of chart only in case the number of series is higher than two. With just one
series, it would be the Column Chart.
13.What is a Scatter Plot?
A scatter plot shows the relationship between two different values. This is particularly useful to identify
outliers or to understand the correlation in the data. The data sets need to be in pairs with a dependent
variable and an independent variable. The dependent becomes the y axis and the independent, the x.
When the data is distributed on the plot, the results show the correlation to be positive, negative or
nonexistent. Adding a trend line will help show the correlation and how statistically significant the
correlation is. Example: With trend lines, you can answer such questions as whether profit is predicted by
sales, or whether average delays at an airport are significantly correlated with the month of the year.
Read more here
14.What is an Area Chart?
An area chart is a chart type based on the line chart: it also shows information as a series of data points
connected by straight line segments, but the area between the X-axis and the line segments is filled with
color or a patt
ern.
The area chart emphasizes the magnitude of change over time and can be used to highlight the total value
across a trend. For example, an area chart displaying profit over time can emphasize the total profit.
15.What is a Choropleth Map?
Choropleth Map displays divided geographical areas or regions that are colored, shaded or patterned in
relation to a data variable. This provides a way to visualize values over a geographical area, which can
show variation or patterns across the displayed location.
16.What is a Tree Map?
A tree map is a visualization that displays hierarchically organized data as a set of nested rectangles,
parent elements being tiled with their child elements. The sizes and colors of rectangles are proportional
to the values of the data points they represent.
17.What is a Slope Graph?
A slope graph is a lot like a line graph, in that it plots change between points. However, a slope graph
plots the change between only two points, without any kind of regard for the points in between.
It is based on the idea that humans are fairly good at interpreting changes in direction. Decreases and
quickly rising increases are easily detected.
18.What is a Waterfall chart?
The Waterfall Chart type is generally used to understand the influence of several positive and negative
factors on the initial value. Points are utilized to display the process of the initial value change and can be
in one of the three states: increase, decrease, or total (subtotal).
The first and last columns in a Waterfall Chart usually represent totals. The intermediate ones stand for
the changes
19.What is a Heat Map?
A heat map is a visual representation of data that uses color-coding to represent different values in a
matrix.
Essentially, this type of chart is a data table with rows and lines denoted by different sets of categories.
Each table cell can contain a numerical or logical value that determines the cell color based on a given
color palette.
Heat maps are convenient data visualization for comparing categories, using color to emphasize
relationships between data values that would be much harder to understand in a simple table with
numbers.
20.What is a Bullet graph?
A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet
graph is useful for comparing the performance of a primary measure to one or more other measures. It
shows a distribution showing progress towards a goal behind the bar.
21.What is a Gantt Chart?
The Gantt Chart is a type of bar diagram used to illustrate plans and activity schedules of any project. It is
a project management tool. Gantt Chart consist of bars stretched along the time axis.
In this type of diagram, each bar represents a certain task within the framework of the project in
question. The ends of the bar stand for the task's start and finish time, and the length reflects duration.
The Gantt Chart's vertical axis is a task list.
22.What is a Histogram chart?
A Histogram visualizes the distribution of data over a continuous interval or certain time period. Each bar
in a histogram represents the tabulated frequency at each interval/bin. The total area of the Histogram is
equal to the number of data.
Histograms help give an estimate as to where values are concentrated, what the extremes are and
whether there are any gaps or unusual values. They are also useful for giving a rough view of the
probability distribution.
23.What are some of the charts you avoid and why?
3d charts- They skew the visual perception of the numbers making them difficult or impossible to
interpret or compare. It also introduces unnecessary chart elements like side and floor panels which is not
only a cognitive load on the brain, but also inaccurate.
Pie charts – This is not being as accurate as bar charts or position-based visuals. With pie, we are
judging areas and angles which is much more difficult than length in a bar chart. Our eyes cannot ascribe
quantitative values to areas and angles properly.
Donut charts- This is similar to pie chart, but with a hole cut out in the middle so that it looks like a
donut. Because there’s a hole in the middle, we don’t judge values by angle anymore. Instead we have to
compare one arc length with to another arc length. Our eyes cannot ascribe quantitative values to arc
length properly.
24.Can you explai
24.Can you explain some Data Visualization design best practices?
From Storytelling with data book.
- Form follows function: First, we want to think about what it is we want our audience to be able to do
with the data (function) and then create a visualization (form) that will allow for this with ease.
- Don’t assume that two different people looking at the same data visualization will draw the same
conclusion.
- Leverage pre-attentive attributes to make those important words stand out.
- Offer your audience visual affordances as cues for how to interact with your visualization.
- Highlight the important stuff and eliminate distractions.
- Make it legible and clean.
- Label and title as appropriate, so there’s no work going back and forth between a legend and the data
to decipher what is being graphed.
- Whatever data is required for context, but doesn’t need to be highlighted, push it to the background.
- Employ attributes like color, thickness, size, position, labeling, text and annotation to emphasize and
de-emphasize components throughout the visual.
- If there is a conclusion you want your audience to reach, state it in words. From The Functional Art book
- Adding tons of special effects to a graphic will not make it any better if it lacks good information. The
special effects take away space that could have been used to highlight other angles of the story.
- Don’t create fancy visualizations with tons of bubbles, lines, bars and filters and expect readers to
figure the story out by themselves and draw conclusions from the data. That’s not an approach to
information graphics. Not all readers are data analysts.
- Don’t just show stuff; explain the main points, focusing the reader’s attention on the most interesting
parts of the information.
- From Good Charts book
- Include four elements in all charts: title, subtitle, visual field, and source line. Within the visual field
include axes, labels, and sometimes captions and legends.
- Make all the elements support the visual. Use them to highlight the idea, not to describe the chart’s
structure.
- Remove ambiguity. Make sure each element has a single purpose that can’t be misinterpreted.
- Minimize the number of colors you use. Gray works for contextual and second-level information and for
structural elements such as grid lines.
- Limit eye travel. Place labels and legends in close proximity to what they describe.