Section 2: INTRODUCTION TO TABLEAU SOFTWARE

Data Source Page

Below is the Data Source Page for the Sample - Superstore saved data. There are four portions to the Data Source Page. (The numbers 1, 2, 3 and 4 have been added to show you the components of the Data Source page.) Review what each numbered section contains below. 1. LEFT PANE The Left pane is broken down into two main components: Connections and Sheets. Connections - This contains information about the data source you're connected to. It will tell you the name of the data source as well as the data source type. Sheets - This section of the interface displays all of the tables (or sheets) that are included within the data source. Your data source can contain anywhere from one table to 30+ tables. You will need to add and edit which tables you'd like to access from your data source. This is also where the option to use the Data Interpreter is located. (You will learn more about this later in the lesson) 2. CANVAS This top-right block is where you choose and configure the tables you want to select for your analysis by dragging and dropping sheets from the Left Pane and right-clicking to further customize. For example, in the image above, the dataset Orders is currently selected and being viewed below, but you can have multiple tables selected at once. If you're connected to a server or non-static data source you can refresh your data to display the most up-to-date data. This is especially helpful when you're working with real-time company data. 3. DATA GRID This bottom-right block shows a configuration of raw data you have created to access during your analysis. The view displays the first 100 rows of your data source. A data grid has a similar structure to a table used in other traditional analysis. The biggest distinction is the terminology Tableau uses. Review what each lettered section contains below. a. Field A field is a column from your data source. In other words, a field is just a column. Throughout this course, table columns will only be referred to as fields. b. Field Name Each field has its field name bolded at the top. This is a part of the Tableau header. c. Table Name Each field has the table it belongs to indicated above the field name. This is especially helpful when you're using multiple tables in your Canvas. d. Data Type Every field is automatically assigned a data type. You will discuss data types in more detail later in the course. The Data Grid is also where you can make manipulations to the data source like sorting or hiding fields, renaming fields or resetting field names, creating calculations, changing the column or row sort, or adding aliases. If you right-click on any field, the following pop-up will appear with the manipulation options. Notice that these manipulations are a lot more limited than with other data wrangling tools you may have used. If your data source contains a lot of dirty data, you want to make sure to thoroughly clean it before connecting to Tableau. 4. METADATA GRID Depending on the type of data that you are connected to, click the metadata grid button to navigate to the metadata grid. The metadata grid displays the fields in your data source as rows so that you can quickly examine the structure of your Tableau data source and perform routine management tasks, such as renaming fields or hiding multiple fields at once. When connected to cube or some extract only data, the metadata grid displays by default. Whichever table is selected in the Canvas (Section 2) is the one whose details will be displayed in the Metadata grid Note that the Data Grid and the Metadata Grid have the same dropdown menu items when the Field Name is clicked.

Tableau vs. Excel

One of the biggest questions when first starting with Tableau is why not just use spreadsheet software like Excel. You may be thinking, if I can create visualizations in Excel, why use Tableau? Watch the video below to learn more about the distinction between Tableau and Excel. Then assess your understanding of the information in the video in the Knowledge Check that follows. https://youtu.be/bEO-6Fi4FGY Video Transcript: Tableau has grown to become one of the most popular business intelligence tools in the entire world. It is a BI software that allows non-technical users to visualize their data and work with it almost immediately, lowering know-how barriers dramatically. In the past, business analysts needed the help of IT personnel, who could assist them in gathering raw data and preprocessing it. Only then could business analysts start working on the visualization of such data. The advent of Tableau democratized this process, and allowed BI analysts to be independent. Non-technical people can easily load data into the program and start playing with it. Tableau's forte are meaningful, intuitive visualizations, and sometimes, that's really valuable. Analysts are able explore their data right away without spending too much time on numbers, which provided limited insights, and instead focus on data that matters. This is why we can confidently say Tableau is an indispensable tool in the arsenal of most corporate business intelligence analysts, data analysts, and data scientists. Many people are uncertain about the difference between Tableau and spreadsheet tools like Excel. And that's a reasonable doubt. Until we point out they serve different purposes. Using Tableau doesn't necessarily mean you can forget about Excel, and vice-versa. While Excel is not as powerful or intuitive as Tableau when it comes to data visualization, Tableau is not optimal when you would like to use it as a data creation tool. Although it has several database management functionalities, the program isn't the best solution when you would like to perform multiple operations with your data before you start analyzing it. Moreover, Tableau isn't great for multilayered calculations. It is able to calculate in its own fields, but it shouldn't be used as a spreadsheet tool for multilayered calculations, such as the preparation of a budget in Excel. Where Tableau surpasses the competition is in data visualizations. It is a very smart program that allows you to visualize data in a more powerful way compared to Excel. So, for example, when you work with geographical data, there is no way Excel could interpret the cells in your spreadsheet as a geographical location. On the other hand, Tableau recognizes that, and allows you to visualize such data and see how a variable is distributed geographically. Moreover, Tableau allows you to combine several types of charts and build up meaningful dashboards that are truly interactive and facilitate additional analysis. Once you visualize your data, you can easily dig deeper and explore its granularity: finding the reason for unusual spikes or investigating certain trends. Even novice Tableau users would save a significant amount of time if they transfer their predesigned, existing Excel dashboards to Tableau. Uploading new data and updating visuals is more rapid in Tableau. Therefore, we can agree that a competent analyst needs both Excel and Tableau, given that they serve different purposes. Tableau is superior when it comes to visuals and dashboards, and Excel is a spreadsheet tool we need in order to perform multilayered calculations. In the same way a combat soldier carries a rifle and a pistol at the same time and uses them under different circumstances, a business analyst should know how to work with both Excel and Tableau and apply each of them when needed.

The Datasaurus Dozen

*VIEW file named The Datasaurus Dozen AND Twelve Data Visualizations* What if I told you that VIZ I in the following image has the exact same statistical findings as VIZ II? In fact, what if I told you that VIZ I has the exact same statistical values as all the data visualizations below? Believe it or not, all of the visualizations above, including the dinosaur, all have the same summary statistics, the same X/Y Mean, X/Y Standard Deviation, and Correlation. This may sound familiar if you've learned about Anscombe's Quartet. For those who have not, Anscombe's Quartet is comprised of four separate tables that have the same descriptive statistics but display different data visualizations when the values are plotted. Take a look at the following drop-down if you'd like to see the quartet. Anscombe's Quartet is a famous example of why including data visualizations to support your insights is pivotal. This phenomenon happens all the time in data analysis. Take the dinosaur and the twelve data visualizations, for example. They all look entirely different, however, they share the same statistical data. Many times, analysts adopt bad practices and assume that by solely calculating the statistics or showing numbers they have an answer. Although statistics and numbers are very powerful and are used to support your insights, statistical/numerical data alone can be dangerous and misrepresent your insights. This is yet another reason why data visualizations are so important. They add another layer of information without actually having to add any new data. Also, if you'd like to learn more about the data and research it took to create the visualizations above, visit the article, The Datasaurus Dozen — Same Stats, Different Graphs by Autodesk Research. https://www.autodesk.com/research/publications/same-stats-different-graphs

Recap of Tableau Software Key Takeaways

- Tableau allows you to easily transform and shape your data for analysis from multiple sources, including from a spreadsheet, database, big data, data warehouse, or cloud data. - There are two categories that Tableau products can be split into: Development and Sharing. -- Development products focus on Tableau software designed to help you develop content such as data visualizations. -- Sharing products focus on Tableau software designed to help you share content created using the software. - Although Tableau is not the most robust tool for cleaning or wrangling your data, it does surpass other tools in its ability with data exploration and interactive analysis. This allows more time to focus on the inferences and insights produced from the data and less time on building or navigating a tool. - Tableau is not as powerful as Excel when it comes to creating data, pre-processing, and multi-layered calculations. - When opening Tableau Desktop, you are greeted by a start screen that is broken into three sections: -- 1. Connect - Used for connecting to data through a file, server, or saved data source. -- 2. Open - Used to interact with existing workbooks that you have previously saved or look at workbook samples from Tableau. -- 3. Discover - Used for discovering additional information directly about or related to Tableau.

Careers in Data Visualization

Although this course is geared towards business and data analysts, those are not the only types of careers that work with data visualization. Generally, there are three main career categories that data viz work could be split into: Data Visualization Engineer, Business/Data Analyst or Data Scientist, and Other. DATA VISUALIZATION ENGINEER A data visualization engineer is an expert in the design and development of data visualization that works collaboratively with software engineers, consultants, and many others to create data visualizations for the appropriate stakeholder(s). You should know how to develop data visualizations using both data visualization tools (e.g., Tableau) and coding them from scratch. A data visualization engineer is also an expert in the theory of design to apply to their work. As a data visualization engineer, you are considered the data viz expert of your company. BUSINESS/DATA ANALYST OR DATA SCIENTIST As a business/data analyst or data scientist, you will be using data visualization in work nearly every day. Although the objective of your work is more around creating insights from your analysis, not so much creating the data visualizations, knowing how to create and interpret data viz is still very important. As you've explored, one of the most effective ways to communicate discoveries with stakeholders is through data visualization. As a business or data analyst, you should know how to develop data visualization using data visualization tools (e.g., Tableau). Data scientists and some data analysts should also know how to develop data visualizations by coding them from scratch. Both a business/data analyst and data scientist should have a good understanding of visual design principles and best practices. OTHER "Other" is an umbrella for almost every other career out there. Every career may not create the data visualizations themselves, but they do rely on interpreting data visualization to do their job. You don't have to be in the data world to use data viz. Regardless if you choose to go into analytics or not, the ability to develop or analyze data visualization is a strong skillset to have in today's workforce. As an example, sectors such as marketing actually have digital marketers who have to create their own data visualization using tools like Tableau. Regardless of the field (e.g., marketing, education, entertainment, business), even if you decide later on that the analytics route is not for you, there is still a need for experience with data visualization in other fields. It is the "hot, new skill" that recruiters are looking for on your resume. Even more, traditional digital designers — like graphic designers — are not safe from the rise in data visualization. It is predicted that in the following years, graphic designers will be using data visualization in their everyday work as well as closely collaborating with data experts to improve the quality of data viz. Read the following article if you're curious to learn more about the collaboration between data experts and traditional designers, Is Data Visualization the Next Design Challenge? https://modus.medium.com/is-data-visualization-the-next-design-challenge-ccbd6741b989 So, why is it that data visualizations are so popular and effective, regardless of a person's career? To better answer this question, first, explore how humans process visualizations.

Data Viz and the Analysis Process

As a business or data analyst, you will be working primarily with exploratory (Explore) and explanatory (Explain) data visualizations. Why? In order to better understand why these are best, review the analysis process. As a reminder, analytics or analysis is the process of discovering and interpreting meaning from data results. The goal of the analysis is to discover patterns and trends that businesses can use to make informed actions. The actionable trends and patterns uncovered through data analysis are called insights. As an analyst, you want to be able to create these strong insights to then share with the business. However, in order to get to produce and share insights, you first have to get through the analysis process. The first step of the process focuses on data. You must navigate and wrangle the data to better explore what is happening in the jumble of numbers and text. This is where you want to produce data visualization with the goal to Explore. Exploratory data visualization is used to uncover a relationship in the data to later analyze for more clarity. As you move down the analysis process you get to analytics. This is the step where you will continue to wrangle data and calculate your statistics to get closer to determining your insights. You will continue to produce data viz to Explore in this part of the process, but it is also during this time in your analysis where you'll have identified which data visualizations you have created to Explore are actually strong enough to use to Explain your insights. Just be careful to make sure to clean up your initial data viz by following best practices before presenting it to stakeholders. In this step, you will create data visualizations both to Explore and Explain. Finally, by the time you get to the end of your analysis process, you will have produced insights that you will want to share with your stakeholders. This final portion of the process is focused on creating data visualization used to Explain, however, that doesn't always mean you will create new data viz in this step. Sometimes you will have already produced the data viz during the analytics step that you will use here to communicate insights. Every now and then, analysts have the opportunity to take another step and convert insights into data visualizations used to Exhibit. Exhibiting data viz is rarer, but nevertheless still possible as a part of the process. In this course, you will build data visualizations with the goal to Explore and Explain. Additionally, you will learn the appropriate skills to be able to create your own data visualizations with the goal to Exhibit, if/when you need to do so.

What is a User Interface?

As you learn a new piece of software, it's essential to gain familiarity with the software's user interface. The user interface (UI) is the visual layer between the user and the software. It's the place where a user can give commands for the software program to execute. Essentially, the UI is what you, the user, see and what allows you to interact with the software. Poorly-designed UIs make getting started with new software challenging because they might appear visually complex, making navigating the new software a bit overwhelming. On the other hand, well-designed UIs make getting started with new software a breeze. The Google Search UI is a simple but effective example of a well-designed UI. You do not need an in-depth tutorial on how to navigate Google's UI, which is indicative of how well-designed it is. When you, the user, type a question or word into the search bar and press enter, you're actually sending a search command to Google's search program. On the other hand, this UI is also not perfect because of its simplicity: if a user doesn't know how to interact with Google's text field by clicking into it and activating the cursor, this UI may be challenging to navigate. Luckily, most computer users have mastered this prerequisite skill before navigating internet browsers. Now that you're familiar with a general understanding of user interfaces, dive a bit deeper into the Tableau Desktop's user interface, specifically Tableau Desktop's Start Page user interface.

Range of Data Viz Goals 1). Explore

As you're initially exploring and analyzing your data, you create data visualizations to better understand what is happening in the data. These visualizations are part of your discovery process and are used to make sense of the large quantity of numbers and text found within datasets. If your analysis is unguided, you will find yourself creating many exploratory data visualizations to better guide your analysis. These are not made with the intention of showing to an audience, but instead for you, the analyst, to reference. This type of data visualization can look many ways, as there is no specific data visualization type dedicated to exploring data. Also, Explore visualizations do not need to follow visual design or data viz best practices as they are more of a "rough draft." This is another reason exploratory data visualization is intended for individual use versus used to explain data to others.

Recap of Connect to Data in Tableau Key Takeaways

Before creating visualizations you must connect to a data source. There are three main methods for connecting to data sources: - Connecting to a file - Connecting to a server - Connecting to a saved data source The most common file types you will connect to are Microsoft Excel and text. Excel files include extensions such as .*xls, *.xlsx, and *.xlsm. Text files can include files with extensions such as *.txt, *.csv, *.tab, and *.tsv. Connecting to a server allows for access to much larger databases that would otherwise not work with tools like Excel or Google Sheets. There are many server options available on Tableau, but a few are Tableau Server, Microsoft SQL Server, and MySQL. If you are looking to access a data connection that you recently saved on your computer, you should look in the "Saved Data Sources" tab in the "Connect" section. There are four main sections to the Data Source Page. - The Left pane contains information about the data source you're connected to and displays all the tables included within the data source. - The Canvas Pane is where you can configure the tables you want to select for analysis. - The Data grid shows the raw data of the configuration you have created and is where you can manipulate data through sorting, hiding, or additional filtering. - The Metadata grid displays the fields in your data source as rows.

Introduction to Connect to Data in Tableau

Before you start building visualizations in Tableau, you need to connect to a data source. Part of the benefit of using Tableau for your visualizations is the variety of data sources you can connect to. These data sources include Microsoft Excel files, text files, MySQL server, and much more. As you can imagine from the many connection options, you can connect to nearly any type of data source in Tableau. And if you don't see the data connection you are looking for right now, someone is likely already working on a customized solution that will help you connect (check the Tableau Forums to stay up-to-date with these and other solutions). Unlike many other tools, Tableau wants to make this task as simple as possible for you so you can get started on the important thing: analysis. In the previous lab, you simulated the data connection process but did not actually connect to a real data source. In this module, you will learn how to connect a functional data source. You will also navigate the data source page that Tableau creates on the platform. Once you've connected to the data, you are one step closer to creating your data visualization and analysis. Why This Matters Analysts are often stumped when it comes to file and tool compatibility. For an analysis to be effective, a file with the data you need for your analysis needs to correctly connect with the tool you are using for your analysis. If the file and the tool can't "talk to each other" or connect, analysis cannot happen. An analyst may have a great data source, but it might be in a unique file type that is not easily compatible with their analysis tools. Or in more common cases, the data source is just too large for any other visualization tool to handle. The analyst would then have to manipulate the original data source or change its file type to fit the tool. Typically this requires that the analyst be familiar with coding languages to be able to make those changes. In worst-case scenarios, the analyst might not be able to use the data at all because they do not have the skills to make the changes. Connecting to different data sources in Tableau is a fairly straightforward and fluid process. It allows for a lot more connection types with data sources of varying sizes. This is the reason why such a large group of businesses and individuals use Tableau as their tool of choice.

Recap of Data Visualization with Tableau Key Takeaways

Building data visualizations is an important step in the data analysis process and an important skill for business or data analysts to master. Your data is only as good as your ability to comprehend and communicate it. Human brains process complex data displayed in visualizations better than when it is displayed as numbers and text. The three data viz goals include Explore, Explain, and Exhibit. As a business or data analyst, you will be working primarily with exploratory and explanatory data visualizations. Exploratory data visualizations are used to better understand and make sense of the data you are working with. Explanatory data visualizations are used to help explain your insights to others. There are many tools that allow analysts to create data visualizations, including spreadsheet software. However, many employers specifically seek mastery of Tableau. Tableau mastery enables you to process, manipulate, and organize data into data visualizations.

Key Terms

Data Visualization: A visual representation of information and data — also referred to as data viz. Data Source: A container of data, which could be a database, spreadsheet in Excel, or a text file, among other things. Data Visualization Engineer: An expert in the design and development of data visualization that works collaboratively with software engineers, consultants, and many others to create data visualizations for the appropriate stakeholder(s). Anscombe's Quartet: Four separate tables that have the same descriptive statistics but display different data visualizations when plotted. This is a famous example that illustrates why including data visualizations to support your insights is pivotal. Analytics: The process of discovering and interpreting meaning from data results. The goal of analytics is to discover patterns and trends that businesses can use to take informed actions. Insights: The actionable trends and patterns uncovered through data analysis.

Key Terms

Data source: The location and container of the data you choose to access when you use Tableau. Dataset: The collected data itself, which you can manipulate and then analyze. It's also sometimes referred to as a table. Server: A computer connected to the internet that constantly runs to store data and facilitate communication. Field: A column from your data source.

Range of Data Viz Goals

Data visualization is great for presenting insights. Data is not powerful enough on its own. The actionable trends and patterns uncovered through data analysis need to be communicated to bring value to the analysis. One of the most important parts of an analyst's job is to communicate data-driven insights in a meaningful way. With data visualization, you can present information in a strategic way to highlight those patterns and trends. However, using data visualization to present insights to stakeholders is not the only goal. There are three ranges of goals for data visualization — to Explore, Explain, and Exhibit.

Global Data Produced Each Day

Guess how many gigabytes (GB) of data are produced each day around the world? As of 2016, 44 BILLION GB of data were produced each day. It is estimated that by 2025 there will be 463 Billion GB of data produced each day. That is A LOT of data being produced. That can be both a good and bad thing. Good because data analysts can get access to more information than they could previously, but also more challenging because analysts have to go through all of that information and interpret it. In this course, you will learn how data visualization can help navigate the plethora of data being produced at an extremely fast rate.

The Power of A Data Visualization

Imagine you're starting on a new analytics project and are given the dataset below to explore. You need to find out whether the company was more profitable this year than last year. However, you notice that the dataset below has 1,236 rows of data to look through. How long would it take you to manually go through and determine whether the company was more profitable than the year before? Way too long! Instead, take a look at the same data plotted on a line graph below. Now, try to determine whether the most recent year is more profitable than the previous year. Within seconds you know that the company's profit has been growing year over year. Visualizations, like the one above, are the fastest way to condense data and share information. Staring at hundreds, thousands, or millions of rows of data does not help you to understand trends in the data or answer questions for the company. Instead, a clear visualization of these rows of data can lead to immediate insights and raise important questions for the larger team, not to mention save you, the analyst, a lot of time reaching those conclusions. Why This Matters While datasets (and tables) are great, visualizations are a better format for communicating data. This is because data visualizations can help make large, complicated, or abstract insights easier to understand. Knowing how and when to create data visualizations is necessary for business and data analysts. Tableau is a visualization tool explicitly made for this purpose.

Introduction to Tableau Software

In the past, you may have developed data visualizations using spreadsheet software like Google Sheets or Excel. These tools are great for exploring your data and practicing new data viz skills but can be too simple to use for true analysis. True analysis requires you to create various data visualizations, ranging in complexity from static to dynamic, as well as easily share your work with stakeholders. In this course, you will use Tableau to create and interpret data visualizations specific to data analysis. Tableau is one of the most powerful data visualization tools in the analytics industry. A business or data analyst using Tableau can explore expert-level visual insights within a few clicks of connecting to a data source. One of Tableau's top-selling points is how accessible and user-friendly it is. You do not need to know how to code to be able to create powerful visualizations. Tableau's drag-and-drop functionality and built-in mathematical formulas and functions allow all users to quickly focus on what's most important: finding data-driven company insights through data visualizations. When you "play" the following GIF, you'll see the data visualization environment with Tableau. The user moves around a variety of fields of data to their corresponding areas in the environment to create the data visualization map. As the user drags-and-drops the information, the visualization appears or is manipulated. Why This Matters As a business or data analyst, you will be working with data visualization software to bring your data to life. Learning how to best harness the power of Tableau is an excellent step towards your future as an analyst. Regardless of how small or large a company you work for. If the company is collecting data, it will need data visualization to help better understand relevant insights from the data. If you want to be able to visualize data using Tableau, you will first need to know how to navigate the tool itself. You will start by navigating Tableau's user interface, connecting to a data source, and preparing the data for visualization. Do not worry if you are unfamiliar with what a user interface is; you will learn all about it in the following lesson.

Diversity of Data Viz

In this day and age, data visualizations (data viz) are all around us. When you hear the words "data visualization," your mind may automatically jump to think of more traditional, static data visualization, like the following bar chart. These are the data visualizations you would more frequently run into internally within a company or as professional infographics. Inside a company is not the only place you will see static data visualizations. Companies like Spotify use traditional data visualization as well. The following data visualization is a product of Spotify Wrapped, a personalized data analysis of the songs and podcasts each Spotify user has listened to most over the year. Spotify does not target data professionals to present this data visualization to, and yet every year users seem eager to examine their data presented through different visualizations. Data visualization can look many different ways. If a visualization was created from a data source to show information, then it is a valid data visualization. *A data source is a container of data such as a database, a spreadsheet in Excel, or a text file, among other things.* Take the following video as another example. In the video, Cruise shared the dynamic data visualizations their autonomous vehicles created as they drove through the streets. These data visualizations are produced live for those working in the company to quickly be able to determine the patterns and trends of the data. https://youtu.be/TrDHeCD9uzw The dynamic aspect and overall look of a data visualization depends on what it is used for. As a business or data analyst, you will be developing many traditional data visualizations, but as you advance your expertise you will be able to produce more non-traditional, dynamic visualizations. Throughout this course, you will explore examples of both traditional and non-traditional data visualization along with static and dynamic data visualization.

Range of Data Viz Goals 2). Explain

Once you've made discoveries in your data, you want to use data visualizations to help explain your insights to others. These data visualizations can help make large, complicated, or abstract insights easier to understand for your stakeholders. You should almost always use data visualizations to explain your insights Typically, data visualizations are not used by themselves but rather are paired with other mediums to amplify the strength of the insight and understanding (like emails and slide decks). A data visualization initially used to Explore can be updated appropriately to be used to Explain. The viz here should follow visual design best practices as well as general data visualization best practices. You will learn all of these throughout this course

Data Visualization and Human Perception

Our human brain loves data visualization. Human brains process complex data displayed in visualizations better than when it is displayed in numbers and text because of the balance in the human brain between perception and cognition. For example, traditional rows and columns of data require conscious thinking, therefore requiring more cognition. On the other hand, data visualization allows the brain to focus on visual perception, which our brain can then interpret and process faster. This is why you see data visualization all around you — in the news, social media, movies, emails, books, magazines — the list goes on and on. Regardless of the complexity of the data insight, a visual representation of data is a more efficient, simple way for most humans to process the intended information. For this reason, it is essential to use graphics and visualizations to communicate insights to stakeholders.

KEY TERMS

Structured Query Language (SQL): The written language used to communicate and retrieve data from a database. Tableau Desktop: The desktop version of the Tableau Software with a rich feature set. It has all the functionality of the software that Tableau Public lacks. Tableau Public is a more cost-efficient option (it's free), but it lacks the privacy that Tableau Desktop offers. Tableau Software Development Products: Development products focus on Tableau software designed to help you develop content such as data visualizations. Development products include Tableau Desktop and Tableau Public. Tableau Software Sharing Products: They focus on Tableau software designed to help you share content created using the software. Sharing products include Tableau Server, Tableau Online, and Tableau Reader. User Interface (UI): The visual layer between the user and the software. It's the place where a user can give commands for the software program to execute and, essentially, is what you, the user, see and what allows you to interact with the software. Workbook: A file that holds all of your work and contains one or more worksheets, dashboards, and/or stories. Workbooks allow you to organize, save, and share your results.

Tableau for Analytics

Tableau is marketed as "fast analytics for everyone." The tool's mission is to make spreadsheets, databases, and other data sources simpler for the everyday person to use. Now, you may be thinking, "Is this tool right for an analyst if its targeted towards the average person to use? Should an analyst be using a more complex tool in their work?" In short, no! Although Tableau is not the most robust tool for cleaning or wrangling your data, it does surpass other tools in its ability with data exploration and interactive analysis. This allows more time to focus on the inferences and insights produced from the data and less time on building or navigating a tool. Producing insights from your data is super important, regardless of the project size. Spreadsheet software (e.g., Google Sheets or Excel) has long been used for this type of analysis. Still, when it comes to visualizing data, creating interactive visualizations, and supporting real-time data discovery, spreadsheet software does not compare to Tableau. This does not mean you should no longer use other tools in your analysis. It is very common for business and data analysts to use multiple tools throughout different parts of their analysis. For example, they might use a combination of SQL, Excel, and Tableau with certain projects. (SQL to clean large datasets in a database, Excel to clean small datasets, and Tableau to present insights.) Other projects may only require you to use Tableau. This typically varies depending on where your data is stored and how much cleaning and wrangling it requires. SQL, or Structured Query Language, is the written language used to communicate and retrieve data from a database. With spreadsheet software, you can clean and wrangle data as well as create data visualizations, but it does not allow for large data manipulation. With SQL, you can pull, store, and wrangle large data from databases, but you cannot create visualizations. With Tableau, you can pull data from databases, manipulate large data, as well as create data visualizations, but it is limited in how much data cleaning and wrangling you can do in the tool itself. If your data is relatively clean, then Tableau could be the only tool you use in your analysis. This also demonstrates how Tableau is not just used to create data visualizations. Tableau believes in using the visual to drive your analysis versus it solely being the end product. *Overall, Tableau is a very powerful data visualization tool with many versions to better fit for various types of work. It is called "the gold standard" in visual analytics.*

Data Viz with Tableau

There are many tools that allow analysts to create data visualizations, including spreadsheet software such as Google Sheets and Excel. However, many employers seek mastery of data visualization tools, like Tableau. Tableau mastery enables you to process, manipulate, and organize data into data visualizations. This is why this Business & Data Analytics Certificate has an entire course dedicated to mastering data visualizations in Tableau, the data visualization tool of choice for many analysts.

Range of Data Viz Goals 3). Exhibit

This is the type of data visualization that takes on more of an art form. This type of viz does not always adopt all the general data viz best practices. Many times with this type of data visualization, the design is prioritized over the clarity/simplicity around communicating insights. This type of visualization is less commonly used by business and data analysts. If you'd like to see an example of this type of data visualization, check out this impressive example of Freddie Mercury's Voice Visualized. Notice that this page is more like an art form, as opposed to a simple way of communicating large, complicated, or abstract insights. https://julialedur.github.io/freddie-mercurys-voice/

Data Source Connections

Throughout this course, you will see the terms "data source" and "dataset" often. Although they sound very similar, there are slight but important differences between the two terms. - A data source is the location and container of the data you choose to access when you use Tableau. - A dataset is the collected data itself, which you can manipulate and then analyze. It's also sometimes referred to as a table. A dataset contains the actual data for analysis. A data source is the container for that dataset, which could be a spreadsheet in Excel, a text file, or a PDF, among other things. You can have one or multiple datasets within a data source. In this lesson, you will learn how to connect Tableau to a data source in order to access the dataset(s) within your data source(s). Before creating visualizations, you will need to connect to a data source. As covered previously, within Tableau Desktop, there are three main methods for connecting to data sources: connecting to a file, connecting to a server, or connecting to a saved data source. 1. Connecting to a File on Your Computer Microsoft Excel files and text files are the most common data files to connect to when managing most datasets. Microsoft Excel files include all Excel workbooks with file extensions such as *.xls, *.xlsx, and *.xlsm. Text files can include files with extensions such as *.txt, *.csv, *.tab, and *.tsv. You might also manage data from PDF files that contain tables. Tableau has special built-in technology that can scan the document for tables and connect to those tables as the data source. 2. Connecting to a Server A server is a computer connected to the internet that constantly runs to store data and facilitate communication. In Tableau, you are trying to connect to the server (computer) that hosts the data you want to access. To connect to a server like MySQL and Google Sheets, you will need to know the login and access key of the dataset. With this connection option, Tableau will send a structured "query" to the server, essentially asking it for a specified set of data. There are many server connection options for Tableau Desktop users. This option gives you access to connect to much larger datasets that would otherwise break using tools like Excel or Google Sheets. This also allows you to connect to the data manipulated in your MySQL database. 3. Connecting to a Saved Data Source Any data connections that you make either with a file or with a server can then be saved to your computer. Once saved to your computer, those connections will show up in the Saved Data Source option for data connections. The ability to save your data source connections, especially when you start customizing them, is especially valuable as a data analyst. You can share your saved data source connections with other teammates to make sure you are all accessing the same datasets. When you download Tableau Desktop to your computer, you also download two saved data sources as part of the Tableau Desktop package: Sample - Superstore and World Indicators. These two demo data sources provide users with a great foundation for exploring Tableau Desktop's capabilities. You will use these data sources often throughout the labs in this course.

The Importance of Data Visualization

Today, businesses gather data on everything, from external sales to internal employee productivity. By collecting this data, the company gains a huge advantage — they can uncover trends, address problem areas, and better serve their company. However, as you saw in the previous visual, as a company collects more and more data, someone needs to explore, summarize, and pull insights from all that data, which can be very time-consuming. One of the most efficient ways to do so is by creating data visualizations. *Data visualization, also referred to as data viz, is a visual representation of information and data. Visual elements like charts, graphs, plots, and maps are all examples of data visualizations and accessible ways to see and understand trends, outliers, and patterns in data.* For example, the following data visualization allows you to quickly compare CO2 Emissions from China and the United States to see how these two countries have changed over time. As you can see, China overtook the United States in CO2 Emissions sometime around 2005. Summarizing all of the information in the raw data would be much more challenging without data visualization. Building data visualizations is an essential step in the data analysis process and an important skill for business or data analysts to master.

Connection to a File/Server vs. Saved Data Source

Warning, not every data source connection automatically takes you to the Data Source page. If you connect to a file or server data source, you will be directed straight to the Data Source Page to further select and manipulate the datasets you'd like to analyze in your worksheet. You will know you are in the Data Source Page because of the highlight over the "Data Source" tab in the lower left-hand corner. If you connect to a saved data source, you will be directed to a page that looks like the image below. This page is called a worksheet. You will learn more about the key structures of a Tableau Worksheet in the following section. The reason you are directed straight to a worksheet is that Tableau assumes all data source customization has already been done for the saved data source. You will know you are in a worksheet because of the highlight over the "Sheet 1" tab in the lower left-hand corner. To the left of that tab is the Data Source tab. You are able to jump back and forth between the tabs. These are similar to the tabs within Google Sheets and Excel. Once you click the Data Source tab, you will see the data source connection you've selected in the top-left of the window under Connections. Your data source can contain anywhere from one table to 30+ tables. You will need to add and edit which tables you'd like to access from your data source to then start working on the worksheet.

Tableau Desktop's Start Page

When you first open Tableau Desktop, you are immediately greeted by a three-pane start screen, which you can see in the following image. (The numbers 1, 2, and 3 have been added to show you the components of the homepage.) Review what each numbered section contains: 1. CONNECT This pane displays a menu where users can select data with which they would like to connect for visualization and analysis. There are three methods of data connection available: - To a File - To a Server - Saved Data Sources Connecting to a data source is the first step in your data visualization workflow with Tableau. There are a variety of data sources you can connect to, including Microsoft Excel files, text files, MySQL server, and more. Once you've connected to the data you want to analyze, you can then move forward to your visualization and analysis. Without a data connection, you wouldn't have anything to analyze. The Connect pane is one of the main differentiators between Tableau Public and Tableau Desktop. Tableau Desktop has many more options for connecting to data sources compared to Tableau Public. Tableau Desktop allows users to connect to local data files stored on their computers and private servers. One of the main reasons a user would need to upgrade from Tableau Public to Tableau Desktop is the ability to connect to privately-hosted company data and to save those data workbooks privately. When using Tableau Public, a user can only publish their work to the open web. This means that all data, even the confidential data, is made public, which isn't ideal for most companies. 2. OPEN This pane displays existing workbooks you have saved on your computer, allows you to pin workbooks to the start page, and explore sample workbooks. A workbook is a file that holds all of your work, containing one or more worksheets, dashboards, or stories. Workbooks allow you to organize, save, and share your results. Open recent workbooks - The first time you use Tableau Desktop, this pane is empty. However, as you create and save new workbooks, the most recently opened will show here. Pin workbooks - You can pin a workbook to always appear on the start page, even if it wasn't recently opened. This is a way to easily access those workbooks, like a bookmark. Explore workbooks - You can open and explore the Sample Workbooks that Tableau provides for you. 3. DISCOVER This pane is primarily for discovering additional information directly about or related to Tableau and the latest in data visualization. You can see video links and tutorials created by the Tableau community to help jumpstart your Tableau learning experience. In addition, Tableau also advertises events that are related to the field of data visualization.

Tableau Software

Within the Tableau Software, there are different Tableau products dedicated to serving different data visualization purposes. These products can be split into two main categories: development products and sharing products. Development products focus on Tableau Software designed to help you develop content such as data visualizations, whereas sharing products focus on Tableau Software designed to help you share content created using the software. In this course, you will learn and use Tableau Desktop. Tableau Desktop is a desktop version of the Tableau Software with a rich feature set, focused on developing content. It has all the functionality of the software that Tableau Public lacks. Tableau Public is a more cost-efficient option (it's free), but it lacks the privacy that Tableau Desktop offers (with Tableau Public, when you save your work, it goes to the Tableau community page). Companies that use Tableau need privacy to protect sensitive data, which is why their analysts use Tableau Desktop. If you would like to go more in-depth into the other Tableau products, you can optionally visit Tableau's website to read more. https://www.tableau.com/products Note, throughout this course, "Tableau Desktop" will be used interchangeably with "Tableau."

What is Tableau? A Tableau Overview Video

https://youtu.be/YfE9jBq002s Video Summary - Tableau allows you to easily transform and shape your data for analysis from multiple sources, including from a spreadsheet, database, big data, data warehouses, or cloud data. - You can transform your data into powerful interactive dashboards and securely share your dashboards so everyone in your organization can ask and answer their questions. - Tableau's management tools give you control over user permissions, data source connectivity, and the visibility to support your deployment. - With Tableau you can easily scale from small teams to full organizations with thousands of users. As demonstrated in the video, Tableau is a very powerful data visualization tool that can serve many different data viz needs. Tableau Software has so many layers to it that it actually has entirely different products to serve the different needs. In the video, you may have heard them reference "Tableau Server" or "Tableau Online." These products are still part of Tableau Software but are each focused on a different viz need. Continue reading to learn more about these different Tableau products.