"Summer 21 (Su21) Ending at 5th December 2021 And Winter 22 (WI22) Starts 9th December 2021" For more information join our telegram https://t.me/salesforcekeeda

Monday, February 8, 2021

Data Visualization In Tableau

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 lin


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.

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? 

  1.  First begin with understanding client business, technical and business requirements.
  2.  Understand the context of the visualization and audience who we intend to build. 
  3.  Capture user stories/business problems they are trying to address.
  4.  Understand the data model, data analysis and data preparation.
  5.  Storyboard/sketch initial prototype to solve the above problems. 
  6. Choose an appropriate visual that effectively brings the story live and answer the business questions.
  7.  Remove unnecessary elements, keep it clean and simple.
  8.  Showcase the visualization to stakeholders, seek feedback and iterate.
  9. Data reconcile to make sure the numbers in the visualization are matching to the numbers in the source. 
  10. Unit testing, System testing, UAT, approval and deploying to production.
  11. 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.

 https://vizard.co Page 17 of 69 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 pattern.

 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. https://vizard.co Page 24 of 69

 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 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.