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Data Literacy Cheat Sheets

<p>Tableau is a business intelligence tool that allows you to effectively report insights through easy-to-use customizable visualizations and dashboards. This cheat sheet is designed to get you started with Tableau and covers creating your first visualization, creating a dashboard, a data story, and more.&nbsp;</p>
<p><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Tableau+Cheat+Sheet.pdf"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://res.cloudinary.com/dyd911kmh/image/upload/v1651236733/tableau_cheat_sheet_5f7309124a.png" alt="" width="500" /></a></p>
<p style="text-align: center;"><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Tableau+Cheat+Sheet.pdf"><em><strong>For a printable version of this cheat sheet, click on the image above</strong></em></a></p>
<h2>Why use Tableau?</h2>
<p>While there are many reasons to use Tableau &mdash; here are our top 3 reasons:</p>
<ul>
<li>Easy to use&mdash;no coding involved</li>
<li>Integrates seamlessly with any data source</li>
<li>Fast and can handle large datasets</li>
</ul>
<h2>Tableau Versions</h2>
<p>There are two main versions of Tableau:</p>
<ul>
<li><strong>Tableau Public:</strong> A free version of Tableau that lets you connect to limited data sources, create visualizations and dashboards, and publish dashboards online</li>
<li><strong>Tableau Desktop:</strong> A paid version of tableau which lets you connect to all types of data sources, allows you to save work locally, and has unlimited data sizes</li>
</ul>
<h2>Getting started with Tableau</h2>
<p>When working with Tableau, you will work with Workbooks. Workbooks contain sheets, dashboards, and stories. Similar to Microsoft Excel, a Workbook can contain multiple sheets. A sheet can be any of the following and can be accessed on the bottom left of a workbook.</p>
<ul>
<li><strong>Worksheet</strong>: A worksheet is a single view in a workbook. You can add shelves, cards, legends, visualizations, and more to a worksheet</li>
<li><strong>Dashboard</strong>: A collection of multiple worksheets used to display multiple views simultaneously</li>
<li><strong>Story</strong>: A story is a collection of multiple dashboards and/or sheets that describe a data story</li>
</ul>
<h2>The anatomy of a worksheet</h2>
<h3>The sidebar</h3>
<p>In the sidebar, you&rsquo;ll find useful panes for working with data</p>
<ul>
<li><strong>Data:</strong> The data pane on the left-hand side contains all of the fields in the currently selected data source</li>
<li><strong>Analytics:</strong> The analytics pane on the left-hand side lets you add useful insights like trend lines, error bars, and other useful summaries to visualizations</li>
</ul>
<h3>Tableau data definitions</h3>
<p>When working with data in Tableau, there are multiple definitions to be mindful of</p>
<ul>
<li><strong>Fields:</strong> Fields are all of the different columns or values in a data source or that are calculated in the workbook. They show up in the data pane and can either be dimension or measure fields</li>
<li><strong>Dimensions:</strong> A dimension is a type of field that contains qualitative values (e.g. locations, names, and departments). Dimensions dictate the amount of granularity in visualizations and help reveal nuanced details in the data</li>
<li><strong>Measures: </strong>A measure is a type of field that contains quantitative values (e.g. revenue, costs, and market sizes). When dragged into a view, this data is aggregated, which is determined by the dimensions in the view</li>
<li><strong>Data types:</strong> Every field has a data type which is determined by the type of information it contains. The available data types in Tableau include text, date values, date &amp; time values, numerical values, boolean values, geographical values, and cluster groups</li>
</ul>
<h3>The canvas</h3>
<p>The canvas is where you create your data visualizations</p>
<ul>
<li><strong>Tableau Canvas:</strong> The canvas takes up most of the screen on Tableau and is where you can add visualizations</li>
<li><strong>Rows and columns:</strong> Rows and columns dictate how the data is displayed on the canvas. When dimensions are placed, they create headers for the rows or columns while measures add quantitative values</li>
<li><strong>Marks card:</strong> The marks card allows users to add visual details such as color, size, labels, etc. to rows and columns. This is done by dragging fields from the data pane into the marks card&nbsp;</li>
</ul>
<h2>How to Create a Data Vizualization</h2>
<p class="p1"><strong>Upload a dataset&nbsp;</strong></p>
<ol class="ol1">
<li class="li1">Launch Tableau</li>
<li class="li1">In the <em>Connect</em> section, under <em>To a File</em>, press on the file format of your choice</li>
<li class="li1">For selecting an Excel file, select <em>.xlsx</em> or <em>.xlsx</em></li>
</ol>
<p class="p1"><strong>Creating your first visualization</strong></p>
<ol class="ol1">
<li class="li1">Once your file is uploaded, open a <em>Worksheet</em> and click on the <em>Data</em> pane on the left-hand side</li>
<li class="li1">Drag and drop at least one field into the <em>Columns</em> section, and one field into the <em>Rows</em> section at the top of the canvas</li>
<li class="li1">To add more detail, drag and drop a dimension into the <em>Marks</em> card (e.g. drag a dimension over the color square in the marks card to color visualization components by that dimension)</li>
<li class="li1">To a summary insight like a trendline, click on the <em>Analytics</em> pane and drag the trend line into your visualization</li>
<li class="li1">You can change the type of visualization for your data by clicking on the <em>Show Me button</em> on the top right</li>
</ol>
<h2>Data Visualization Types in Tableau</h2>
<p>Tableau provides a wide range of data visualizations. Here is a list of the most useful visualizations you have in Tableau:</p>
<ul>
<li><strong>Bar Charts:</strong> Horizontal bars used for comparing specific values across categories (e.g. sales by region)</li>
<li><strong>Stacked Bar Charts:</strong> Used to show categorical data within a bar chart (e.g., sales by region and department)</li>
<li><strong>Side by side chart: </strong>Used to compare values across categories in a bar chart format (e.g., sales by region comparing product types)</li>
<li><strong>Line Chart:</strong> Used for looking at a numeric value over time (e.g., revenue over time)</li>
<li><strong>Scatter Plot:</strong> Used to identify patterns between two continuous variables (e.g., profit vs. sales volume)</li>
<li><strong>Histogram:</strong> Used to show a distribution of data (e.g., Distribution of monthly revenue)</li>
<li><strong>Box-and-Whisper Plot:</strong> Used to compare distributions between categorical variables (e.g., distribution of revenue by region)</li>
<li><strong>Heat map: </strong>Used to visualize data in rows and columns as colors (e.g., revenue by marketing channel)</li>
<li><strong>Highlight table: </strong>Used to show data values with conditional color formatting (e.g., site traffic by marketing channel and year)</li>
<li><strong>Symbol Map:</strong> Used to show geographical data (e.g., Market size opportunity by state)</li>
<li><strong>Treemap:</strong> Used to show hierarchical data (e.g., Show how much revenue subdivisions generate relative to the whole department within an organization)</li>
<li><strong>Dual Combination:</strong> Used to show two visualizations within the same visualization (e.g., profit for a store each month as a bar chart with inventory over time as a line chart)</li>
</ul>
<h2>How to Customize Visualizations in Tableau</h2>
<p>Tableau provides a deep ability to filter, format, aggregate, customize, and highlight specific parts of your data visualizations</p>
<h3>Filtering data with highlights</h3>
<ul>
<li>Once you&rsquo;ve created a visual, click and drag your mouse over the specific portion you want to highlight</li>
<li>Once you let go, you will have the option to <em>Keep Only</em> or <em>Exclude</em> the data</li>
<li>Open the <em>Data</em> pane on the sidebar. Then, you can drag-and-drop a field into the filters card just to the left of the pane</li>
</ul>
<h3>Filtering data with filters</h3>
<ul>
<li>Open the <em>Data</em> pane on the left-hand side</li>
<li>Drag-and-drop a field you want to filter on and add it to the <em>Filters</em> card</li>
<li>Fill out in the modal how you would like your visuals to be filtered on the data</li>
</ul>
<h3>Aggregating data</h3>
<ul>
<li>Right-click on a measure field in the <em>Data</em> pane</li>
<li>Go down to <em>Default</em> properties, <em>Aggregation</em>, and select the aggregation you would like to use&nbsp;</li>
</ul>
<h3>Changing colors</h3>
<ul>
<li>Create a visualization by dragging fields into the <em>Rows</em> and <em>Columns</em> section at the top of the screen</li>
<li>Drag dimensions into the <em>Marks</em> field, specifically into the <em>Color</em> square</li>
<li>To change from the default colors, go to the upper-right corner of the color legend and select <em>Edit Colors</em>. This will bring up a dialog that allows you to select a different palette</li>
</ul>
<h3>Changing fonts</h3>
<ul>
<li>In the <em>Format</em> menu on the top ribbon, press on <em>Select Workbook</em>. This will replace the <em>Data</em> pane and allow you to make formatting decisions for the Workbook</li>
<li>From here, select the font, font size, and color</li>
</ul>
<h2>How to Create Dashboards in Tableau</h2>
<ol>
<li>Launch Tableau</li>
<li>In the <em>Connect</em> section under <em>To A File</em>, press on your desired file type</li>
<li>Select your file</li>
<li>Click the <em>New Sheet</em> at the bottom to create a new sheet</li>
<li>Create a visualization in the sheet by following the steps in the previous sections of this cheat sheet</li>
<li>Repeat steps 4 and 5 until you have created all the visualizations you want to include in your dashboard</li>
<li>Click the <em>New Dashboard</em> at the bottom of the screen</li>
<li>On the left-hand side, you will see all your created sheets. Drag sheets into the dashboard</li>
<li>Adjust the layout of your sheets by dragging and dropping your visualizations</li>
</ol>
<h2>How to Create Stories in Tableau</h2>
<ol>
<li>Click the <em>New Story</em> at the bottom of the screen</li>
<li>Change the size of the story to the desired size in the bottom left-hand corner of the screen under <em>Size</em></li>
<li>Edit the title of the story by renaming the story. To do this, right-click on the story sheet at the bottom and press <em>Rename</em></li>
<li>A story is made of story points, which lets you cycle through different visualizations and dashboards</li>
<li>To begin adding to the story, add a story point from the left-hand side. You can add a blank story point</li>
<li>To add a summary text to the story, click <em>Add a caption</em> and summarize the story point</li>
<li>Add as many story points as you would like to finalize your data story</li>
</ol>
<h2>Learn more about Tableau</h2>
<p>Here are a few extra Tableau resources to get you started:</p>
<ul>
<li><a href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau Fundamentals Skills track</a>&nbsp;</li>
<li><a href="https://www.datacamp.com/blog/5-tableau-tips-and-tricks-for-streamlining-your-workflow">5 Tableau Tips and Tricks for Streamlining Your Workflow</a></li>
<li><a href="https://www.datacamp.com/blog/all-about-tableau">All about Tableau</a></li>
</ul>
<p>Check out DataCamp's <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib">Introduction to Data Visualization with Matplotlib</a> course.</p>

In this Tableau Cheat Sheet, you'll get the download on creating your first visualization, top visualizations in Tableau, creating dashboards, stories, and more.

Tableau Cheat Sheet

In this data visualization cheat sheet, you'll learn about the most common data visualizations to employ, when to use them, and their most common use-cases. 

Data Visualization Cheat Sheet

<p>SQL, also known as Structured Query Language, is a powerful tool to search through large amounts of data and return specific information for analysis. Learning SQL is crucial for anyone aspiring to be a data analyst, data engineer, or data scientist, and helpful in many other fields such as web development or marketing.</p>
<p>In this cheat sheet, you'll find a handy list of functions covering querying data, filtering data, aggregation, and more&mdash;all collected from our <a href="https://www.datacamp.com/tracks/sql-fundamentals">SQL Fundamentals Skill Track</a>.</p>
<p><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/SQL+for+Data+Science.pdf"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1649681885/download_d2f88d7c8d.png" alt="" /></a></p>
<h2 style="text-align: left;">The Different Dialects of SQL</h2>
<p>Although SQL languages all share a basic structure, some of the specific commands and styles can differ slightly. Popular dialects include MySQL, SQLite, SQL Server, Oracle SQL, and more. PostgreSQL is a good place to start &mdash;since it&rsquo;s close to standard SQL syntax and is easily adapted to other dialects.&nbsp;</p>
<h2 style="text-align: left;">Sample Data</h2>
<p>Throughout this cheat sheet, we'll be using the sample data <code>airbnb_listings</code>&mdash;denoting rental apartments on Airbnb.</p>
<table style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif; width: 99.992%; height: 89.6024px;" border="1"><colgroup><col style="width: 19.9687%;" /><col style="width: 19.9687%;" /><col style="width: 19.9687%;" /><col style="width: 19.9687%;" /><col style="width: 19.9687%;" /></colgroup>
<tbody>
<tr style="height: 22.4006px;">
<td style="height: 22.4006px;"><strong>id</strong></td>
<td style="height: 22.4006px;"><strong>city</strong></td>
<td style="height: 22.4006px;"><strong>country</strong></td>
<td style="height: 22.4006px;"><strong>number_of_rooms</strong></td>
<td style="height: 22.4006px;"><strong>year_listed</strong></td>
</tr>
<tr style="height: 22.4006px;">
<td style="height: 22.4006px;">1</td>
<td style="height: 22.4006px;">Paris</td>
<td style="height: 22.4006px;">France</td>
<td style="height: 22.4006px;">5</td>
<td style="height: 22.4006px;">2018</td>
</tr>
<tr style="height: 22.4006px;">
<td style="height: 22.4006px;">2</td>
<td style="height: 22.4006px;">Tokyo</td>
<td style="height: 22.4006px;">Japan</td>
<td style="height: 22.4006px;">2</td>
<td style="height: 22.4006px;">2017</td>
</tr>
<tr style="height: 22.4006px;">
<td style="height: 22.4006px;">3</td>
<td style="height: 22.4006px;">New York</td>
<td style="height: 22.4006px;">USA</td>
<td style="height: 22.4006px;">2</td>
<td style="height: 22.4006px;">2022</td>
</tr>
</tbody>
</table>
<h2 style="text-align: left;">Querying tables</h2>
<h3>Get all the columns from a table</h3>
<pre class="language-sql"><code>SELECT * 
FROM airbnb_listings;</code></pre>
<h3>Return the <code>city</code> column from the table</h3>
<pre class="language-sql"><code>SELECT city 
FROM airbnb_listings;</code></pre>
<h3>Get the <code>city</code> and <code>year_listed</code> columns from the table</h3>
<pre class="language-sql"><code>SELECT city, year_listed
FROM airbnb_listings;
</code></pre>
<h3>Get the listing <code>id</code>, <code>city</code>, ordered by the <code>number_of_rooms</code> in ascending order</h3>
<pre class="language-sql"><code>SELECT city, year_listed 
FROM airbnb_listings 
ORDER BY number_of_rooms ASC;
</code></pre>
<h3>Get the listing <code>id</code>, <code>city</code>, ordered by the <code>number_of_rooms</code> in descending order</h3>
<pre class="language-sql"><code>SELECT city, year_listed 
FROM airbnb_listings 
ORDER BY number_of_rooms DESC;
</code></pre>
<h3>Get the first 5 rows from <code>airbnb_listings</code></h3>
<pre class="language-sql"><code>SELECT * 
FROM airbnb_lisitings
LIMIT 5;
</code></pre>
<h3>Get a unique list of cities where there are listings</h3>
<pre class="language-sql"><code>SELECT DISTINCT city
FROM airbnb_lisitings;
</code></pre>
<h2 style="text-align: left;">Filtering on numeric columns</h2>
<h3>Get all the listings where <code>number_of_rooms</code> is more or equal to 3</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms &gt;= 3;</code></pre>
<h3>Get all the listings where <code>number_of_rooms</code> is more than 3</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms &gt; 3;</code></pre>
<h3>Get all the listings where <code>number_of_rooms</code> is exactly 3</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms = 3;</code></pre>
<h3>Get all the listings where <code>number_of_rooms</code> is&nbsp;lower or equal to 3</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms &lt;= 3;</code></pre>
<h3>Get all the listings where <code>number_of_rooms</code> is lower than 3</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms &lt; 3;</code></pre>
<h3>Filtering columns within a range&mdash;Get all the listings with 3 to 6 rooms</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms BETWEEN 3 AND 6;</code></pre>
<h2 style="text-align: left;">Filtering on text columns</h2>
<h3>Get all the listings that are based in <code>'Paris'</code></h3>
<pre class="language-sql"><code>SELECT * 
FROM airbnb_listings 
WHERE city = &rsquo;Paris&rsquo;;
</code></pre>
<h3><strong>Filter one column on many conditions&mdash;Get the listings based in the <code>'USA'</code> and in <code>&lsquo;France&rsquo;</code></strong></h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE country IN (&lsquo;USA&rsquo;, &lsquo;France&rsquo;);</code></pre>
<h3>Get all listings where <code>city</code> starts with <code>"j"</code> and where it does not end with <code>"t"</code></h3>
<pre class="language-sql"><code>SELECT * 
FROM airbnb_listings 
WHERE city LIKE &lsquo;j%&rsquo; AND city NOT LIKE &lsquo;%t&rsquo;;
</code></pre>
<h2 style="text-align: left;">Filtering on multiple columns</h2>
<h3>Get all the listings in <code>"Paris"</code> where <code>number_of_rooms</code> is bigger than 3</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE city = &rsquo;Paris&rsquo; AND number_of_rooms &gt; 3;
</code></pre>
<h3>Get all the listings in <code>"Paris"</code> OR the ones that were listed after 2012</h3>
<pre class="language-sql"><code>SELECT * 
FROM airbnb_listings
WHERE city = 'Paris' OR year_listed &gt; 2012;</code></pre>
<h2 style="text-align: left;">Filtering on missing data</h2>
<h3>Get all the listings where <code>number_of_rooms</code> is missing</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms IS NULL; 
</code></pre>
<h3>Get all the listings where <code>number_of_rooms</code> is not missing</h3>
<pre class="language-sql"><code>SELECT *
FROM airbnb_listings 
WHERE number_of_rooms IS NOT NULL; 
</code></pre>
<h2 style="text-align: left;">Simple aggregations</h2>
<h3>Get the total number of rooms available across all listings&nbsp;</h3>
<pre class="language-sql"><code>SELECT SUM(number_of_rooms) 
FROM airbnb_listings; </code></pre>
<h3><strong>Get the average number of rooms per listing across all listings</strong></h3>
<pre class="language-sql"><code>SELECT AVG(number_of_rooms) 
FROM airbnb_listings;
</code></pre>
<h3>Get the listing with the highest number of rooms across all listings</h3>
<pre class="language-sql"><code>SELECT MAX(number_of_rooms) 
FROM airbnb_listings;</code></pre>
<h3><strong>Get the listing with the lowest number of rooms across all listings</strong><span style="font-weight: 400;"><br /></span></h3>
<pre class="language-sql"><code>SELECT MIN(number_of_rooms) 
FROM airbnb_listings;
</code></pre>
<h2 style="text-align: left;">Grouping, filtering, and sorting&nbsp;</h2>
<h3>Get the total number of rooms for each country</h3>
<pre class="language-sql"><code>SELECT country, SUM(number_of_rooms)
FROM airbnb_listings
GROUP BY country;</code></pre>
<h3>Get the average number of rooms for each country</h3>
<pre class="language-sql"><code>SELECT country, AVERAGE(number_of_rooms)
FROM airbnb_listings
GROUP BY country;</code></pre>
<h3>Get the listing with the maximum number of rooms for each country</h3>
<pre class="language-sql"><code>SELECT country, MAX(number_of_rooms)
FROM airbnb_listings
GROUP BY country;</code></pre>
<h3>Get the listing with the lowest amount of rooms per country</h3>
<pre class="language-sql"><code>SELECT country, MIN(number_of_rooms)
FROM airbnb_listings
GROUP BY country;</code></pre>
<h3>For each country, get the average number of rooms per listing, sorted by ascending order</h3>
<pre class="language-sql"><code>SELECT country, AVG(number_of_rooms) AS avg_rooms
FROM airbnb_listings
GROUP BY country
ORDER BY avg_rooms ASC;</code></pre>
<h3>&nbsp;</h3>
<h3>For Japan and the USA, get the average number of rooms per listing in each country</h3>
<pre class="language-sql"><code>SELECT country, MAX(number_of_rooms)
FROM airbnb_listings
WHERE country IN (&lsquo;USA&rsquo;, &lsquo;Japan&rsquo;);
GROUP BY country;</code></pre>
<h3>Get the number of cities per country, where there are listings</h3>
<pre class="language-sql"><code>SELECT country, COUNT(city) AS number_of_cities
FROM airbnb_listings
GROUP BY country;</code></pre>
<h3>Get all the years where there were more than 100 listings per year</h3>
<pre class="language-sql"><code>SELECT year_listed
FROM airbnb_listings
GROUP BY year_listed
HAVING COUNT(id) &gt; 100;</code></pre>

With this SQL cheat sheet, you'll have a handy reference guide to basic querying tables, filtering data, and aggregating data

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