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Introduction to Data Science Disciplines

The term “Data Science” dates back to the 1960s, to describe the emerging field of working with large amounts of data that drives organizational growth and decision-making. While the essence has remained the same, the data science disciplines have changed a lot over the past decades thanks to rapid technological advancements. In this free introduction to data science practice exam, you will test your understanding of the modern day data science disciplines and their role within an organization.

Excel Basics

Ever since its first release in 1985, Excel continues to be the most popular spreadsheet application to this day- with approximately 750 million users worldwide, thanks to its flexibility and ease of use. No matter if you are a data scientist or not, knowing how to use Excel will greatly improve and optimize your workflow. Therefore, in this free Excel Basics practice exam you are going to work with a dataset of a company in the Fast Moving Consumer Goods Sector as an aspiring data analyst and test your knowledge on basic Excel functions and shortcuts.

Useful Tools in Excel

What Excel lacks in data visualization tools compared to Tableau, or computational power for analyzing big data compared to Python, it compensates with accessibility and flexibility. Excel allows you to quickly organize, visualize and perform mathematical functions on a set of data, without the need for any programming or statistical skills. Therefore, it is in your best interest to learn how to use the various Excel tools at your disposal. This practice exam is a good opportunity to test your excel knowledge in the text to column functions, excel macros, row manipulation and basic math formulas.

Fundamentals of Descriptive Statistics

Descriptive statistics helps us understand the actual characteristics of a dataset by generating summaries about data samples. The most popular types of descriptive statistics are measures of center: median, mode and mean. In this free practice exam you have been appointed as a Junior Data Analyst at a property developer company in the US, where you are asked to evaluate the renting prices in 9 key states. You will work with a free excel dataset file that contains the rental prices and houses over the last years.

Fundamentals of Inferential Statistics

While descriptive statistics helps us describe and summarize a dataset, inferential statistics allows us to make predictions based off data. In this free practice exam, you are a data analyst at a leading statistical research company. Much of your daily work relates to understanding data structures and processes, as well as applying analytical theory to real-world problems on large and dynamic datasets. You will be given an excel dataset and will be tested on normal distribution, standardizing a dataset, the Central Limit Theorem among other inferential statistics questions.   

Excel Functions

The majority of data comes in spreadsheet format, making Excel the #1 tool of choice for professional data analysts. The ability to work effectively and efficiently in Excel is highly desirable for any data practitioner who is looking to bring value to a company. As a matter of fact, being proficient in Excel has become the new standard, as 82% of middle-skill jobs require competent use of the productivity software. Take this free Excel Functions practice exam and test your knowledge on removing duplicate values, transferring data from one sheet to another, rand using the VLOOKUP and SUMIF function.

SQL SELECT Statement

SQL is one of the most popular and in-demand programming languages, because of its intuitive syntax, ubiquitous presence in the business world, and ability to query data and perform analysis. Therefore, possessing SQL skills will give you a significant career advantage in the modern data-driven workforce. Test your knowledge in the basics of SQL with this free SQL SELECT Statement practice exam designed by 365 Data Science instructor Martin Ganchev. These set of SQL questions will test your knowledge on creating databases, SQL Constraints, and the SQL Select Query by giving you a free .sql file containing company data, to work with.

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1(b).3 - visualization.

To understand thousands of rows of data in a limited time there is no alternative to visual representation. The objective of visualization is to reveal hidden information through simple charts and diagrams. Visual representation of data is the first step toward data exploration and formulation of an analytical relationship among the variables. In a whirl of complex and voluminous data, visualization in one, two, and three-dimension helps data analysts to sift through data in a logical manner and understand the data dynamics. It is instrumental in identifying patterns and relationships among groups of variables. Visualization techniques depend on the type of variables. Techniques available to represent nominal variables are generally not suitable for visualizing continuous variables and vice versa. Data often contains complex information. It is easy to internalize complex information through visual mode. Graphs, charts, and other visual representations provide quick and focused summarization.

• Tools for Displaying Single Variables
• Tools for Displaying Relationships Between Two Variables
• Tools for Displaying More Than Two Variables

Tools for Displaying Single Variables Section  

Histograms are the most common graphical tool to represent continuous data. On the horizontal axis, the range of the sample is plotted. On the vertical axis are plotted the frequencies or relative frequencies of each class. The class width has an impact on the shape of the histogram. The histograms in the previous section were drawn from a random sample generated from theoretical distributions. Here we consider a real example to construct histograms. 

The dataset used for this purpose is the Wage data that is included in the ISLR package in R. A full description of the data is given in the package. The following R code produces the figure below which illustrates the distribution of wages for all 3000 workers.

  Sample R code for Distribution of Wage

The data is mostly symmetrically distributed but there is a small bimodality in the data which is indicated by a small hump towards the right tail of the distribution.

The data set contains a number of categorical variables one of which is Race. A natural question is whether the wage distribution is the same across Race. There are several libraries in R which may be used to construct histograms across levels of categorical variables and many other sophisticated graphs and charts. One such library is ggplot2. Details of the functionalities of this library will be given in the R code below.

  Sample R code for Histogram of Wage by Race

In the following figures, histograms are drawn for each Race separately.

Because of the huge disparity among the counts of the different races, the above histograms may not be very informative. Code for an alternative visual display of the same information is shown below, followed by the plot.

  Sample R code for Histogram of Wage by Race (Alternative)

The second type of histogram also may not be the best way of presenting all the information. However further clarity is seen in a small concentration at the right tail.

Boxplot is used to describe the shape of data distribution and especially to identify outliers. Typically an observation is an outlier if it is either less than Q 1 - 1.5 IQR or greater than Q 3 + 1.5 IQR, where IQR is the inter-quartile range defined as Q 3 - Q 1 . This rule is conservative and often too many points are identified as outliers. Hence sometimes only those points outside of [Q 1 - 3 IQR, Q 3 + 3 IQR] are only identified as outliers. 

  Sample R code for Boxplot of Distribution of Wage

Here is the boxplot that results:

The boxplot of the Wage distribution clearly identifies many outliers. It is a reflection of the histogram depicting the distribution of Wage. The story is clearer from the boxplots drawn on the wage distribution for individual races.  Here is the R code:

  Sample R code for Boxplot Wage by Race

Tools for Displaying Relationships Between Two Variables Section  

Scatterplot.

The most standard way to visualize relationships between two variables is a scatterplot. It shows the direction and strength of association between two variables but does not quantify it. Scatterplots also help to identify unusual observations. In the previous section (Section 1(b).2) a set of scatterplots are drawn for different values of the correlation coefficient. The data there is generated from a theoretical distribution of multivariate normal distribution with various values of the correlation parameter. Below is the R code used to obtain a scatterplot for these data:

  Sample R Code for Relationship of Age and Wage

The following is the scatterplot of the variables Age and Wage for the Wage data.

It is clear from the scatterplot that the Wage does not seem to depend on Age very strongly. However, a set of points towards the top are very different from the rest. A natural follow-up question is whether Race has any impact on the Age-Wage dependency or the lack of it.  Here is the R code and then the new plot:

We have noted before that the disproportionately high number of Whites in the data masks the effects of the other races. There does not seem to be any association between Age and Wage, controlling for Race.

Contour plot

This is useful when a continuous attribute is measured on a spatial grid. They partition the plane into regions of similar values. The contour lines that form the boundaries of these regions connect points with equal values.  In spatial statistics, contour plots have a lot of applications.

Contour plots join points of equal probability. Within the contour lines concentration of bivariate distribution is the same. One may think of the contour lines as slices of a bivariate density, sliced horizontally. Contour plots are concentric; if they are perfect circles then the random variables are independent. The more oval-shaped they are, the farther they are from independence. Note the conceptual similarity in the scatterplot series in Sec 1.(b).2. In the following plot, the two disjoint shapes in the interior-most part indicate that a small part of the data is very different from the rest.

Here is the R code for the contour plot that follows:

  Sample R Code for Contour Plot of Age and Wage

Tools for Displaying More Than Two Variables Section  

Scatterplot matrix.

Displaying more than two variables on a single scatterplot is not possible. A scatterplot matrix is one possible visualization of three or more continuous variables taken two at a time.

The data set used to display the scatterplot matrix is the College data that is included in the ISLR package. A full description of the data is given in the package.  Here is the R code for the scatterplot matrix that follows:

  Sample R Code for Scatterplot Matrix of College Attributes

Parallel Coordinates

An innovative way to present multiple dimensions in the same figure is by using parallel coordinate systems. Each dimension is presented by one coordinate and instead of plotting coordinates at the right angle to one another, each coordinate is placed side-by-side. The advantage of such an arrangement is that many different continuous and discrete variables can be handled within a parallel coordinate system, but if the number of observations is too large, the profiles do not separate out from one another and patterns may be missed.

The illustration below corresponds to the Auto data from the ISLR package. Only 35 cars are considered but all dimensions are taken into account. The cars considered are different varieties of Toyota and Ford, categorized into two groups: produced before 1975 and produced in 1975 or after. The older models are represented by dotted lines whereas the newer cars are represented by dashed lines. The Fords are represented by blue color and Toyotas are represented by pink color.  Here is the R code for the profile plot of this data that follows:

  Sample R Code for Profile Plot of Toyota and Ford Cars

The differences among the four groups are very clear from the figure. Early Ford models had 8 cylinders, were heavy, and had high horsepower and displacement. Naturally, they had low MPG and less time to accelerate. No Toyota belonged to this category. All Toyota cars are built after 1975, have 4 cylinders (one exception only) and MPG performance belongs to the upper half of the distribution. Note that only 35 cars are compared in the profile plot. Hence each car can be followed over all the attributes. However had the number of observations been higher, the distinction among the profiles would have been lost and the plot would not be informative.

Interesting Multivariate Plots

Following are some interesting visualization of multivariate data. In Star Plot , stars are drawn according to rules as defined by their characteristics. Each axis represents one attribute and the solid lines represent each item’s value on that attribute. All attributes of the observations are possible to be represented; however, for the sake of clarity on the graph only 10 attributes are chosen. 

Again, the starplot follows the R code for generating the plot:

  Sample R Code for Starplot of College Data

Another interesting plot technique with multivariate data is Chernoff Face where attributes of each observation are used to draw different features of the face. A comparison of 30 colleges and universities from the College dataset is compared below.

Again, R code and then the plot follows:

  Sample R Code for Comparison of Colleges and Universities

For comparison of a small number of observations on up to 15 attributes, Chernoff’s face is a useful technique. However, whether two items are more similar or less, depends on interpretation.

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Mastering the Art of Data Visualization: Tips & Techniques

Data Visualization Modeling posted by ODSC Community October 5, 2023 ODSC Community

In the digital era, data visualization stands as an indispensable tool in the realm of business intelligence . It represents the graphical display of data and information, transforming complex datasets into intuitive and understandable visuals.

By implementing data visualization, businesses can reap multifaceted benefits:

• Simplified Data Interpretation : Complex data are converted into easily comprehensible visuals, enabling quick interpretation and understanding
• Enhanced Decision-making Process : Visual data representation aids in identifying patterns, trends, and outliers, fostering informed decision-making
• Improved Information Retention : Visuals increase information retention and recall, promoting long-term strategic planning

Remember that effective data visualization acts not only as a summary of your data but also as a guide towards informed decisions. By harnessing the power of visual grammar rules and frameworks like the McCandless Method or Kaiser Fung’s Junk Charts Trifecta Checkup, you can elevate your business intelligence strategy to new heights.

Key Principles and Best Practices for Data Visualization

Understanding the key principles and best practices for data visualization can significantly improve your ability to present data in a meaningful, digestible way.

Visual Grammar Rules

Visual grammar rules are fundamental to creating effective visual presentations. These include clarity, simplicity, and emphasis on the information rather than the graphic design itself.

• Clarity: The purpose of your visualization should be immediately clear to your audience. Avoid unnecessary complexity in charts, graphs, and diagrams.
• Simplicity: Keep designs as simple as possible while still conveying the necessary information. Extraneous elements can distract from the data you’re presenting.
• Emphasis on Information: The main focus should always be on the data, not on the aesthetics of the presentation. Avoid using overly flashy designs or effects that could detract from your message.

Consider these points when designing your own visualizations.

Consider a bar chart showing annual sales figures for your company’s product range. Making each bar a different color might make the chart look more appealing, but it can confuse the audience if there’s no clear reason for the color variation. Instead, use one color for all bars and differentiate them by labeling each one with the product name and sales figure.

This approach applies clarity , simplicity , and an emphasis on information , demonstrating how these visual grammar rules enhance data visualization best practices.

Incorporate these design principles into your own work to ensure your visualizations communicate effectively, keeping your audience engaged and informed without overwhelming them with unnecessary detail or confusing graphics.

Moving forward, let’s delve deeper into organizing visualization through popular frameworks that help structure data effectively. Using such frameworks will enable you to deliver insights more coherently, making it easier for your audience to understand complex datasets.

Frameworks for Organizing Visualization

Organizing data visualization is an art as much as it is a science. It’s where design principles meet visual grammar rules, creating effective and engaging visuals. Two popular frameworks that encapsulate data visualization best practices are the McCandless Method and Kaiser Fung’s Junk Charts Trifecta Checkup .

The McCandless Method, coined by David McCandless, advocates for a balance between information and design. This involves considering aspects like:

• Ensuring data accuracy
• Emphasizing clarity and precision
• Incorporating a meaningful color palette
• Utilizing pre-attentive attributes effectively to guide viewer attention

Meanwhile, Kaiser Fung’s Junk Charts Trifecta Checkup takes a more analytical approach to visualization. It encourages us to ask three key questions:

• What does the chart show?
• What does the data say?
• What relevant factor is missing?

By following these frameworks, you can create striking visuals that not only look good but also communicate your message effectively.

Effective Techniques for Data Visualization

Let’s dive into the world of Power BI to explore its features and learn some useful data visualization tips and tricks for data visualization. Power BI, a business analytics tool developed by Microsoft, offers interactive visualizations with self-service business intelligence capabilities.

Using Power BI Tips and Tricks

To begin with, Power BI stands out in the crowd due to its ability to produce beautiful reports with interactive visualizations. It allows sharing these reports directly within the platform or embedding them in an app or website. The tool’s drag-and-drop feature simplifies creating complex dashboards, making it user-friendly even for beginners.

One of the most potent features of Power BI is Quick Insights . This function finds patterns, trends, and correlations in the data automatically. To use it effectively:

• Select a dataset
• Click ‘Get Insights’
• Wait for Power BI to do its magic!

Another valuable feature is Natural Language Querying . With this feature, you can type in questions about your data in natural language and get immediate answers. For example, if you have an e-commerce business and want to know your best-selling product last month, just type “What was my top-selling product last month?” into the query box.

The Q&A Visual takes this one step further by allowing users to ask questions directly on the report page and presenting answers in a visual format. To harness this feature:

• Drag the Q&A button onto your report page
• Start asking questions!

Don’t forget about Bookmarking . This tool allows you to save a customized view of a report (filters and slicers) and return to it at any point. This is particularly useful when dealing with large datasets.

Lastly, consider using Data Drill Down for hierarchical data visualization. This technique helps users navigate from general overviews down to specific details in just a few clicks.

In the realm of data visualization, Power BI is a game-changer with its advanced features and user-friendly interface. Harness these data visualization tips and techniques to create compelling visualizations and gain valuable insights from your data. Up next, we’ll delve into more intriguing aspects of data visualization: keyboard shortcuts and custom themes. Stay tuned!

Utilizing Keyboard Shortcuts and Custom Themes

Efficiency in data visualization is crucial. One way to boost your productivity is through keyboard shortcuts . They provide a fast, seamless way to navigate your workspace, execute commands, and manipulate data. For instance, in Power BI, you can use Ctrl + M to start a new measure or Alt + Shift + F10 to access the context menu.

Complementing the use of shortcuts, custom themes are essential for enhancing visual appeal. They not only add color and style but also consistency across your visuals. You can create custom themes within Power BI by going to View > Themes > Customize current theme . From there, you can tweak colors, text properties, and visual elements to match your brand or preference.

Remember: The right combination of keyboard efficiency and design aesthetics elevates the impact of your data visualization techniques.

Data Modeling and Drill-Through Techniques

Data modeling plays a crucial role in data visualization. It’s the process of creating a visual representation of data, which can help to understand complex patterns and relationships. Using data modeling effectively allows you to uncover insights that would be difficult to grasp in raw, unprocessed data.

To illustrate, it’s like taking a jumbled pile of puzzle pieces and organizing them into an understandable image. When you organize your data in this way, it becomes easier for everyone to understand.

One of the most powerful techniques in data modeling is drill-through. This technique allows users to navigate from a summary view into detailed data. For instance, if you’re viewing sales data by region, a drill-through could allow you to click on one region and see the individual sales by city or even by store.

Here are some tips for implementing drill-through techniques:

• Plan Ahead : Define what detailed information would be useful before setting up your drill-throughs.
• Limit Your Layers : Too many layers can confuse users. Stick to a few key details.
• Use Clear Labels : Make sure it’s obvious what each layer represents.

These techniques, when used correctly, can dramatically enhance your ability to communicate complex information through your visualizations.

Real-Time Dashboards and Explaining Data

Access to real-time dashboards in data visualization provides a game-changing advantage. These dynamic tools compile and display data as it enters the system, allowing for immediate analysis and action. Benefits of utilizing real-time dashboards include:

• Keeping stakeholders informed with up-to-the-minute data
• Enabling rapid response to emerging trends or issues
• Facilitating ongoing optimization of strategies based on live data

To extract maximum value from your real-time dashboards, it’s essential to adequately explain the data they present. Densely packed data or complex graphs can be overwhelming without clear, concise explanations. Here are some techniques to effectively communicate complex data:

• Simplicity : Distil complex ideas into simple, understandable terms. Avoid jargon where possible.
• Context : Provide relevant background information to help readers understand why the data matters.
• Visual Aids : Use charts, graphs, and infographics to represent data visually, making it easier to digest.
• Narrative : Weave a story around the data to make it more engaging and relatable.

By employing these techniques, you can ensure that your audience not only sees the numbers but also comprehends their implications. Remember that balancing real-time insight with effective explanations can greatly improve your decision-making process in any online business venture.

Through this article, we’ve unlocked the potential of data visualization , from understanding its benefits to adopting best practices. We’ve dived into Visual Grammar Rules and explored frameworks like the McCandless Method and Kaiser Fung’s Junk Charts Trifecta Checkup . Power BI has been our companion, guiding us through data visualization tips, tricks, and techniques such as keyboard shortcuts, custom themes, data modeling, and drill-through techniques.

We encourage you to leverage these insights in your quest for effective data visualization. Remember, the key is to keep experimenting and learning. Let’s transform complex data into understandable visuals together. After all, a picture is worth a thousand words.

About the author on data visualization tips:

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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1.3: Visual Representation of Data II - Quantitative Variables

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• Page ID 7789

• Jonathan A. Poritz
• Colorado State University – Pueblo

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Now suppose we have a population and quantitative variable in which we are interested. We get a sample, which could be large or small, and look at the values of the our variable for the individuals in that sample. There are two ways we tend to make pictures of datasets like this: stem-and-leaf plots and histograms .

Stem-and-leaf Plots

One somewhat old-fashioned way to handle a modest amount of quantitative data produces something between simply a list of all the data values and a graph. It’s not a bad technique to know about in case one has to write down a dataset by hand, but very tedious – and quite unnecessary, if one uses modern electronic tools instead – if the dataset has more than a couple dozen values. The easiest case of this technique is where the data are all whole numbers in the range \(0-99\) . In that case, one can take off the tens place of each number – call it the stem – and put it on the left side of a vertical bar, and then line up all the ones places – each is a leaf – to the right of that stem. The whole thing is called a stem-and-leaf plot or, sometimes, just a stemplot .

It’s important not to skip any stems which are in the middle of the dataset, even if there are no corresponding leaves. It is also a good idea to allow repeated leaves, if there are repeated numbers in the dataset, so that the length of the row of leaves will give a good representation of how much data is in that general group of data values.

Example 1.3.1. Here is a list of the scores of 30 students on a statistics test: \[\begin{matrix} 86 & 80 & 25 & 77 & 73 & 76 & 88 & 90 & 69 & 93\\ 90 & 83 & 70 & 73 & 73 & 70 & 90 & 83 & 71 & 95\\ 40 & 58 & 68 & 69 & 100 & 78 & 87 & 25 & 92 & 74 \end{matrix}\] As we said, using the tens place (and the hundreds place as well, for the data value \(100\) ) as the stem and the ones place as the leaf, we get

[tab:stemplot1]

One nice feature stem-and-leaf plots have is that they contain all of the data values , they do not lose anything (unlike our next visualization method, for example).

[Frequency] Histograms

The most important visual representation of quantitative data is a histogram . Histograms actually look a lot like a stem-and-leaf plot, except turned on its side and with the row of numbers turned into a vertical bar, like a bar graph. The height of each of these bars would be how many

Another way of saying that is that we would be making bars whose heights were determined by how many scores were in each group of ten. Note there is still a question of into which bar a value right on the edge would count: e.g., does the data value \(50\) count in the bar to the left of that number, or the bar to the right? It doesn’t actually matter which side, but it is important to state which choice is being made.

Example 1.3.2 Continuing with the score data in Example 1.3.1 and putting all data values \(x\) satisfying \(20\le x<30\) in the first bar, values \(x\) satisfying \(30\le x<40\) in the second, values \(x\) satisfying \(40\le x<50\) in the second, etc. – that is, put data values on the edges in the bar to the right – we get the figure

Actually, there is no reason that the bars always have to be ten units wide: it is important that they are all the same size and that how they handle the edge cases (whether the left or right bar gets a data value on edge), but they could be any size. We call the successive ranges of the \(x\) coordinates which get put together for each bar the called bins or classes , and it is up to the statistician to chose whichever bins – where they start and how wide they are – shows the data best.

Typically, the smaller the bin size, the more variation (precision) can be seen in the bars ... but sometimes there is so much variation that the result seems to have a lot of random jumps up and down, like static on the radio. On the other hand, using a large bin size makes the picture smoother ... but sometimes, it is so smooth that very little information is left. Some of this is shown in the following

Example 1.3.3. Continuing with the score data in Example 1.3.1 and now using the bins with \(x\) satisfying \(10\le x<12\) , then \(12\le x<14\) , etc. , we get the histogram with bins of width 2:

If we use the bins with \(x\) satisfying \(10\le x<15\) , then \(15\le x<20\) , etc. , we get the histogram with bins of width 5:

If we use the bins with \(x\) satisfying \(20\le x<40\) , then \(40\le x<60\) , etc. , we get the histogram with bins of width 20:

Finally, if we use the bins with \(x\) satisfying \(0\le x<50\) , then \(50\le x<100\) , and then \(100\le x<150\) , we get the histogram with bins of width 50:

[Relative Frequency] Histograms

Just as we could have bar charts with absolute (§2.1) or relative (§2.2) frequencies, we can do the same for histograms. Above, in §3.2, we made absolute frequency histograms. If, instead, we divide each of the counts used to determine the heights of the bars by the total sample size, we will get fractions or percents – relative frequencies. We should then change the label on the \(y\) -axis and the tick-marks numbers on the \(y\) -axis, but otherwise the graph will look exactly the same (as it did with relative frequency bar charts compared with absolute frequency bar chars).

Example 1.3.4. Let’s make the relative frequency histogram corresponding to the absolute frequency histogram in Example 1.3.2 based on the data from Example 1.3.1 – all we have to do is change the numbers used to make heights of the bars in the graph by dividing them by the sample size, 30, and then also change the \(y\) -axis label and tick mark numbers.

How to Talk About Histograms

Histograms of course tell us what the data values are – the location along the \(x\) value of a bar is the value of the variable – and how many of them have each particular value – the height of the bar tells how many data values are in that bin. This is also given a technical name

[def:distribution] Given a variable defined on a population, or at least on a sample, the distribution of that variable is a list of all the values the variable actually takes on and how many times it takes on these values.

The reason we like the visual version of a distribution, its histogram, is that our visual intuition can then help us answer general, qualitative questions about what those data must be telling us. The first questions we usually want to answer quickly about the data are

• What is the shape of the histogram?
• Where is its center ?
• How much variability [also called spread ] does it show?

When we talk about the general shape of a histogram, we often use the terms

[def:symmskew] A histogram is symmetric if the left half is (approximately) the mirror image of the right half.

We say a histogram is skewed left if the tail on the left side is longer than on the right. In other words, left skew is when the left half of the histogram – half in the sense that the total of the bars in this left part is half of the size of the dataset – extends farther to the left than the right does to the right. Conversely, the histogram is skewed right if the right half extends farther to the right than the left does to the left.

If the shape of the histogram has one significant peak, then we say it is unimodal , while if it has several such, we say it is multimodal .

It is often easy to point to where the center of a distribution looks like it lies, but it is hard to be precise. It is particularly difficult if the histogram is “noisy,” maybe multimodal. Similarly, looking at a histogram, it is often easy to say it is “quite spread out” or “very concentrated in the center,” but it is then hard to go beyond this general sense.

Precision in our discussion of the center and spread of a dataset will only be possible in the next section, when we work with numerical measures of these features.

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

Data Visualization Test 1

What types of data visualization are most common.

Explanation: Data visualization comes in a variety of forms. Scatter plots, line graphs, pie charts, bar charts, heat maps, area charts, choropleth maps, and histograms are among the most frequent.

What is the definition of data visualization?

Explanation: The graphical depiction of information and data is known as data visualization. Data visualization tools make it easy to examine and comprehend trends, outliers, and patterns in data by employing visual elements like charts, graphs, and maps.

The use of data visualization tools makes it easier to observe and understand _______ in the data

Explanation: A graphical depiction of information and data is known as data visualization. Data visualization tools make it easy to examine and comprehend trends, outliers, and patterns in data by employing visual elements like charts, graphs, and maps.

What are some of the advantages of data visualization?

Explanation: The ability to track links between operations and overall business performance is one of the advantages of big data visualization. In a competitive climate, establishing a link between corporate activities and market success is critical.

Data visualization is also a broader element

Explanation: Data visualization is part of the broader data presentation architecture (DPA) discipline, which strives to efficiently identify, find, modify, prepare, and transmit data. Almost every occupation requires data visualization.

Which of the complex techniques isn't utilized to visualize data?

Explanation: This is an intricate technique that is not used in data visualization.

What are some specific examples of data visualization methods?

Explanation: While the strategies are some of the most prominent, there are many other ways to display data to improve your communication skills.

In a nested format, which approach displays hierarchical data?

Explanation: Treemapping is a method for showing hierarchical data using nested figures, usually rectangles, in information visualization and computing. Treemaps are a group of nested rectangles that represent hierarchical (tree-structured) data.

The significance of data visualization is evident in the following paragraphs.

Explanation: With dynamic visual representations of data, data visualization has a positive impact on an organization's decision-making process. Because they can analyze data in graphical or pictorial representations, businesses can now recognize trends more quickly.

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Data visualization test 2.

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Unit 14: Represent and interpret data

About this unit.

So you've collected lots of cool data? Time to learn how to make it look even cooler! In this unit, you'll explore different types of graphs - from picture graphs to bar graphs to line plots. With these tools in your back pocket, you'll be able to show everyone just what your data means.

Picture graphs

• Creating picture and bar graphs (Opens a modal)
• Creating picture graphs (Opens a modal)
• Solving problems with picture graphs (Opens a modal)
• Reading picture graphs (Opens a modal)
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• Creating bar graphs (Opens a modal)
• Reading bar graphs: movies (Opens a modal)
• Reading bar graphs (Opens a modal)
• Interpreting bar graphs: colors (Opens a modal)
• Reading bar graphs: multi-step (Opens a modal)
• Create bar graphs Get 5 of 7 questions to level up!
• Read bar graphs Get 5 of 7 questions to level up!
• Read bar graphs (2-step problems) Get 3 of 4 questions to level up!

Line plots with fractions

• Measuring lengths to nearest 1/4 unit (Opens a modal)
• Graphing data on line plots (Opens a modal)
• Interpreting line plots with fractions (Opens a modal)
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• Represent and interpret data: FAQ (Opens a modal)
• Measure lengths to nearest 1/4 unit Get 3 of 4 questions to level up!
• Graph data on line plots Get 3 of 4 questions to level up!
• Read line plots (data with fractions) Get 3 of 4 questions to level up!

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