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get which of the following cannot be interpreted through dashboard. data discovery and experimentation identify the patterns extracting the insights highly specific presentation from screen.
Using Data Visualization to Find Insights in Data
A useful resource for anyone interested in getting started with data journalism and becoming a data journalist.
Using Data Visualization to Find Insights in Data
Written by: Gregor Aisch
visualization is critical to data analysis. It provides a front line of attack, revealing intricate structure in data that cannot be absorbed in any other way. We discover unimagined effects, and we challenge imagined ones.
Data by itself, consisting of bits and bytes stored in a file on a computer hard drive, is invisible. In order to be able to see and make any sense of data, we need to visualize it. In this chapter I’m going to use a broader understanding of the term visualizing, that includes even pure textual representations of data. For instance, just loading a dataset into a spreadsheet software can be considered as data visualization. The invisible data suddenly turns into a visible ‘picture’ on our screen. Thus, the questions should not be whether journalists need to visualize data or not, but which kind of visualization may be the most useful in which situation.
In other words: when does it makes sense to go beyond the table visualization? The short answer is: almost always. Tables alone are definitely not sufficient to give us an overview of a dataset. And tables alone don’t allow us to immediately identify patterns within the data. The most common example here are geographical patterns which can only be observed after visualizing data on a map. But there are also other kinds of patterns which we will see later in this chapter.
Using visualization to Discover Insights
It is unrealistic to expect that data visualization tools and techniques will unleash a barrage of ready-made stories from datasets. There are no rules, no ‘protocol’ that will guarantee us a story. Instead, I think it makes more sense to look for ‘insights’, which can be artfully woven into stories in the hands of a good journalist.
Every new visualization is likely to give us some insights into our data. Some of those insights might be already known (but perhaps not yet proven) while other insights might be completely new or even surprising to us. Some new insights might mean the beginning of a story, while others could just be the result of errors in the data, which are most likely to be found by visualizing the data.
In order to make the finding of insights in data more effective, I find the following process very helpful:
Figure 71. Data insights: a visualization (Gregor Aisch)
Each of these steps will be discussed further in this section.
How To Visualize Data
Visualization provides a unique perspective on the dataset. You can visualize data in lots of different ways.
Tables are very powerful when you are dealing with a relatively small number of data points. They show labels and amounts in the most structured and organized fashion and reveal their full potential when combined with the ability to sort and filter the data. Additionally, Edward Tufte suggested including small chart pieces within table columns, for instance one bar per row or a small line chart (since then also known as a sparkline). But still, as mentioned in the introduction, tables clearly have their limitations. They are great to show you one-dimensional outliers like the top 10, but they are poor when it comes to comparing multiple dimensions at the same time (for instance population per country over time).
Figure 72. Tips from Tufte: sparklines (Gregor Aisch)
Charts, in general, allow you to map dimensions in your data to visual properties of geometric shapes. There’s much written about the effectiveness of individual visual properties, and the short version is: color is difficult, position is everything. In a scatterplot, for instance, two dimensions are mapped to the to the x- and y-position. You can even display a third dimension to the color or size of the displayed symbols. Line charts are especially suited for showing temporal evolutions while bar charts are perfect for comparing categorical data. You can stack chart elements on top of each other. If you want to compare a small number of groups in your data, displaying multiple instances of the same chart is a very powerful way (also referred to as small multiples). In all charts you can use different kinds of scales to explore different aspects in your data (e.g., linear or log scale).
In fact, most of the data we’re dealing with is somehow related to actual people. The power of maps is to re-connect the data to our very physical world. Imagine a dataset of geo-located crime incidents. Crucially, you want to see where the crimes happen. Also maps can reveal geographic relations within the data, e.g. a trend from North to South or from urban to rural areas.
Figure 73. Cloropeth (Gregor Aisch)
Speaking of relations, the fourth most important type of visualization is a graph. Graphs are all about showing the inter-connections (edges) in your data points (nodes). The position of the nodes is then calculated by more or less complex graph layout algorithms which allow us to immediately see the structure within the network. The trick about graph visualization in general is to find a proper way to model the network itself. Not all datasets already include relations and even if they do, it might not be the most interesting aspect to look at. Sometimes it’s up to the journalist to define edges between nodes. A perfect example of this is the U.S. Senate Social Graph, whose edges connect senators that voted the same in more than 65% of the votes.
A Complete Introduction to Data Exploration. With this comprehensive guide, learn more about: What is Data Exploration, Tools and Advantages of Data Exploration, Data Exploration in Machine Learning and more.
Data Exploration - A Complete Introduction
Data Exploration - A Complete Introduction What is Data Exploration?
Data exploration definition: Data exploration refers to the initial step in data analysis in which data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, in order to better understand the nature of the data.
Data exploration techniques include both manual analysis and automated data exploration software solutions that visually explore and identify relationships between different data variables, the structure of the dataset, the presence of outliers, and the distribution of data values in order to reveal patterns and points of interest, enabling data analysts to gain greater insight into the raw data.
Data is often gathered in large, unstructured volumes from various sources and data analysts must first understand and develop a comprehensive view of the data before extracting relevant data for further analysis, such as univariate, bivariate, multivariate, and principal components analysis.
Data Exploration Tools
Manual data exploration methods entail either writing scripts to analyze raw data or manually filtering data into spreadsheets. Automated data exploration tools, such as data visualization software, help data scientists easily monitor data sources and perform big data exploration on otherwise overwhelmingly large datasets. Graphical displays of data, such as bar charts and scatter plots, are valuable tools in visual data exploration.
A popular tool for manual data exploration is Microsoft Excel spreadsheets, which can be used to create basic charts for data exploration, to view raw data, and to identify the correlation between variables. To identify the correlation between two continuous variables in Excel, use the function CORREL() to return the correlation. To identify the correlation between two categorical variables in Excel, the two-way table method, the stacked column chart method, and the chi-square test are effective.
There is a wide variety of proprietary automated data exploration solutions, including business intelligence tools, data visualization software, data preparation software vendors, and data exploration platforms. There are also open source data exploration tools that include regression capabilities and visualization features, which can help businesses integrate diverse data sources to enable faster data exploration. Most data analytics software includes data visualization tools.
Why is Data Exploration Important?
Humans process visual data better than numerical data, therefore it is extremely challenging for data scientists and data analysts to assign meaning to thousands of rows and columns of data points and communicate that meaning without any visual components.
Data visualization in data exploration leverages familiar visual cues such as shapes, dimensions, colors, lines, points, and angles so that data analysts can effectively visualize and define the metadata, and then perform data cleansing. Performing the initial step of data exploration enables data analysts to better understand and visually identify anomalies and relationships that might otherwise go undetected.
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Exploratory Data Analysis Example
What is Exploratory Data Analysis?
Exploratory Data Analysis (EDA), similar to data exploration, is a statistical technique to analyze data sets for their broad characteristics. Visualization tools for exploratory data analysis such as HEAVY.AI's Immerse platform enable interactivity with raw data sets, giving analysts increased visibility into the patterns and relationships within the data.
Data Exploration in GIS
GIS (Geographic Information Systems) is a framework for gathering and analyzing data connected to geographic locations and their relation to human or natural activity on Earth. With so much of the world's data now being location-enriched, geospatial analysts are faced with a rapidly increasing volume of geospatial data.
Advanced GIS software solutions and tools can facilitate the incorporation of spatio-temporal analysis into existing big data analytics workflows, enabling data analysts to easily create and share intuitive data visualizations that will aid in spatial data exploration. The ability to characterize and narrow down raw data is an essential step for spatial data analysts who may be faced with millions of polygons and billions of mapped points. For example, learn about the ways GIS technologies are improving disaster response operations.
Data Exploration in Machine Learning
A Machine Learning project is as good as the foundation of data on which it is built. In order to perform well, machine learning data exploration models must ingest large quantities of data, and model accuracy will suffer if that data is not thoroughly explored first. Data exploration steps to follow before building a machine learning model include:
What is data visualization and why is it important?
Discover what data visualization is, what it does and why it's important. Explore different examples of data visualization and learn about the different visualization tools and vendors.
Kate Brush Ed Burns
Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets. The term is often used interchangeably with others, including information graphics, information visualization and statistical graphics.
Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made. Data visualization is also an element of the broader data presentation architecture (DPA) discipline, which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible.
Data visualization is important for almost every career. It can be used by teachers to display student test results, by computer scientists exploring advancements in artificial intelligence (AI) or by executives looking to share information with stakeholders. It also plays an important role in big data projects. As businesses accumulated massive collections of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were a natural fit.
Visualization is central to advanced analytics for similar reasons. When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.
A timeline depicting the history of data visualization
Why is data visualization important?
Data visualization provides a quick and effective way to communicate information in a universal manner using visual information. The practice can also help businesses identify which factors affect customer behavior; pinpoint areas that need to be improved or need more attention; make data more memorable for stakeholders; understand when and where to place specific products; and predict sales volumes.
Other benefits of data visualization include the following:
the ability to absorb information quickly, improve insights and make faster decisions;
an increased understanding of the next steps that must be taken to improve the organization;
an improved ability to maintain the audience's interest with information they can understand;
an easy distribution of information that increases the opportunity to share insights with everyone involved;
eliminate the need for data scientists since data is more accessible and understandable; and
an increased ability to act on findings quickly and, therefore, achieve success with greater speed and less mistakes.
Data visualization and big data
The increased popularity of big data and data analysis projects have made visualization more important than ever. Companies are increasingly using machine learning to gather massive amounts of data that can be difficult and slow to sort through, comprehend and explain. Visualization offers a means to speed this up and present information to business owners and stakeholders in ways they can understand.
Big data visualization often goes beyond the typical techniques used in normal visualization, such as pie charts, histograms and corporate graphs. It instead uses more complex representations, such as heat maps and fever charts. Big data visualization requires powerful computer systems to collect raw data, process it and turn it into graphical representations that humans can use to quickly draw insights.
While big data visualization can be beneficial, it can pose several disadvantages to organizations. They are as follows:
To get the most out of big data visualization tools, a visualization specialist must be hired. This specialist must be able to identify the best data sets and visualization styles to guarantee organizations are optimizing the use of their data.
Big data visualization projects often require involvement from IT, as well as management, since the visualization of big data requires powerful computer hardware, efficient storage systems and even a move to the cloud.
The insights provided by big data visualization will only be as accurate as the information being visualized. Therefore, it is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources.
Examples of data visualization
In the early days of visualization, the most common visualization technique was using a Microsoft Excel spreadsheet to transform the information into a table, bar graph or pie chart. While these visualization methods are still commonly used, more intricate techniques are now available, including the following:
infographics bubble clouds bullet graphs heat maps fever charts time series charts
Some other popular techniques are as follows.Line charts. This is one of the most basic and common techniques used. Line charts display how variables can change over time.