https://datacamp-community-prod.s3.amazonaws.com/e63a8f6b-2aa3-4006-89e0-badc294b179c

This tidyverse cheat sheet for beginners will help you to find your way around the well-known packages dplyr and ggplot2!

Tidyverse Cheat Sheet For Beginners

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Tidyverse+Cheat+Sheet.pdf

https://datacamp-community-prod.s3.amazonaws.com/65076e3c-9df1-40d5-a0c2-36294d9a3ca9

This PySpark SQL cheat sheet is your handy companion to Apache Spark DataFrames in Python and includes code samples. 

PySpark Cheat Sheet: Spark DataFrames in Python

https://www.datacamp.com/community/podcast/spreadsheets-data-science

<p>How can spreadsheet workflows be incorporated into more general data science flows in sustainable and healthy ways?</p>

How can spreadsheet workflows be incorporated into more general data science flows in sustainable and healthy ways?

Spreadsheets in Data Science

https://www.datacamp.com/community/blog/spreadsheets-data-science-transcript

https://www.datacamp.com/community/news/word-frequency-the-difference-between-simple-and-weighted-counting-cc241l8k5qf

We go through the process of analyzing movie titles, and uncover some insights from two main metrics: 
- Absolute word frequency: which words are most frequently used in movie titles? 
- Weighted word frequency: which words are most associated with box-office revenue? 

Hope you like it! 

Word Frequency: The Difference Between Simple and Weighted Counting

https://www.datacamp.com/community/news/how-to-become-a-data-scientist-in-one-week-sr4leab48c

How to Become a Data Scientist in One Week?

https://www.datacamp.com/community/news/getting-started-with-the-tidyverse-tutorial-hf369yzqzar

Start analyzing titanic data with R and the tidyverse: learn how to filter, arrange, summarise, mutate and visualize your data with dplyr and ggplot2!

Getting Started with the Tidyverse: Tutorial

https://www.datacamp.com/community/news/web-scraping-in-r-rvest-tutorial-43z6wf5u86

A short case study that shows how to webscrape using rvest. Trustpilot reviews for individual organisations are scraped and exploratively analysed for fraudulent activity.

Web Scraping in R: rvest Tutorial

https://www.datacamp.com/community/news/data-science-for-search-engine-marketing-sem-se9be6ygiqr

As a heavy user of DataCamp since two years, I learned quite a lot. My main business is in online marketing, and I have benefited a lot from the data science skills I learned. I'm now able to manage way larger and more complex campaigns, and efficiently maintain them. 

Data Science for Search Engine Marketing (SEM)

<center><a href="https://www.datacamp.com/tracks/tableau-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1610375720/TableauCTA_v2qbt7.png" /></a></center><br>

<p>Tableau is a business intelligence (BI) platform that functions like a supercharged Microsoft Excel. Its user-friendly, drag-and-drop functionality makes it possible for anyone who interacts with data to analyze and create presentation-ready visualizations—from data newbies to analysts, and even business leaders. You definitely don’t need to be a seasoned data scientist to use Tableau!</p>

<h2>Which companies use Tableau?</h2>
<p>Tableau’s roster of customers is a who’s who of the business world, including well-known brands such as Amazon, Experian, and Unilever. But it’s not just global giants using Tableau to democratize their data—over <a href="https://techcrunch.com/2019/06/10/salesforce-is-buying-data-visualization-company-tableau-for-15-7b-in-all-stock-deal/">86,000 businesses</a> are using Tableau to extract and share insights from their data.</p>

<h2>Certification overview</h2>

<center><CALLOUT BOX>
<a href="https://www.tableau.com/learn/certification/desktop-specialist">Tableau Desktop Specialist</a><br>
Fee: $100<br>
Title valid for: No expiration<br>
</CALLOUT BOX></center><br>


<p>Tableau recommends that you have at least three months’ product experience before taking the Desktop Specialist exam. If you don’t yet have this experience, DataCamp recommends that you prepare for the exam by completing our online <a href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau Fundamentals</a> skill track.</p>

<p>The exam will take place on GoToMeeting or Zoom, and you will be asked to set up your workspace using Google Chrome and confirm that your connection speed meets Tableau’s requirements. Additionally, you will be required to:</p>

<ul>
<li>Present photo identification to the exam proctor via webcam</li>
<li>Close programs, tabs, and remove any paperwork that may help during your exam</li>
<li>Demonstrate to the proctor that you are alone in your room via webcam</li>
<li>Agree to being monitored via webcam for the duration of the exam</li>
</ul>

<p><a href="https://mkt.tableau.com/files/Tableau-Certification-4-steps-to-exam-success.pdf">View the full exam setup requirements</a></p>

<p>Just like the Tableau learning experience <a href="https://www.datacamp.com/tracks/tableau-fundamentals">on DataCamp</a>, the exam is completed using a Windows virtual machine that has Tableau Desktop installed—this may involve a certain amount of toggling between the question and virtual desktop screens which may result in a slightly smaller screen size than you may be used to. It’s also worth noting that you may not have any other windows or programs open for the duration of the test other than those required for the assessment—just like exams in school, you can’t have any notes hidden in your calculator sleeve. The exam is scored automatically and a passing score of 70% or more is required to obtain your certification.</p>

<p>You can register and book your exam on the <a href="https://www.tableau.com/learn/certification/desktop-specialist">Tableau Product Certification Exam center</a>, managed by Loyalist Exam Services (LES). You can also reschedule your exam using this site.</p>

<h2>Training Resources</h2>
<p>At DataCamp, all our instructor-led courses are fully interactive, with no installation or prior experience required. You will also receive a statement of accomplishment which you can also share on your LinkedIn profile upon completion. Please note that DataCamp’s statement of accomplishment is not the same as the Tableau Desktop Specialist certification:</p>

<p><a href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau Fundamentals</a>: 14 hours. 4 Courses</p>
<p>Gain the essential skills you need to confidently use Tableau and prepare to pass the Tableau Desktop Specialist certification. No prior experience required.</p>

<ul>
<li><a href="https://www.datacamp.com/courses/introduction-to-tableau">Introduction to Tableau</a>: 4 hours<br>
Learn how to use Tableau’s user-friendly drag-and-drop functionality to quickly clean, analyze, and visualize your team’s data.</li>
<li><a href="https://datacamp.com/courses/analyzing-data-in-tableau">Analyzing data in Tableau</a>: 4 hours<br>
Create detail-rich map visualizations, configure date and time fields to show trends over time, and extend your data using Calculated Fields.</li>
<li><a href="https://datacamp.com/courses/creating-dashboards-in-tableau">Creating Dashboards in Tableau</a>: 4 hours<br>
Apply dashboard best practices, add interactive or explanatory elements, and use actions to make your dashboard fully interactive.</li>
<li><a href="https://www.datacamp.com/courses/connecting-data-in-tableau">Connecting Data in Tableau</a>: 4 hours<br>
Discover how  to connect Tableau to different data sources and prepare the data for a smooth analysis.</li>
</ul>

<p>Tableau also provides an excellent choice of learning resources you can use to prepare for the exam:</p>

<ul>
<li><a href="https://www.tableau.com/learn/training/20204">Tableau Training Videos</a></li>
<li><a href="https://mkt.tableau.com/files/DesktopSpecialist_ExamGuide.pdf">Desktop Specialist Exam Prep Guide (PDF)</a></li>
<li><a href="https://community.tableau.com/s/explore-forums?_ga=2.195627941.886887347.1610106578-1596736181.1607685378">Tableau community forums</a> and <a href="https://community.tableau.com/s/groups?_ga=2.195627941.886887347.1610106578-1596736181.1607685378">user groups</a></li>
</ul>

<p>Found this article useful? If so please share on <a href="https://www.linkedin.com/company/datacampinc/">LinkedIn</a>, <a href="https://twitter.com/DataCamp">Twitter</a>, or <a href="https://www.facebook.com/datacampinc/">Facebook</a>.</p>

<br><center><a href="https://www.datacamp.com/tracks/tableau-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1610375720/TableauCTA_v2qbt7.png" /></a></center>


https://www.datacamp.com/community/blog/Tableau-desktop-specialist-certification

Add Tableau to your data analyst skill set. Learn to use this BI platform and pass the Desktop Specialist Certification exam with DataCamp.

Pass Tableau’s Desktop Specialist Certification | DataCamp

Tableau Desktop Specialist Certification: Learn How to Use Tableau and Ace the Exam

<p>pandas is one of the most popular Python data science packages and is the standard tool for working with tabular data in Python. Luckily, a complete beginner can learn and start programming in pandas within a couple of weeks. Here’s how to get started.</p>
<h2 id="what-to-know-before-getting-started">What to know before getting started</h2>
<h3 id="what-is-pandas-">What is pandas?</h3>
<p>pandas is one of the first Python packages you should learn because it’s easy to use, open source, and will allow you to work with large quantities of data. It allows fast and efficient data manipulation, data aggregation and pivoting, flexible time series functionality, and more.</p>
<p>pandas has a lot of the same functionality as SQL or Excel, and can take in various data types—from CSV and text files to Microsoft Excel files, SQL databases, and more. It has the added benefit of allowing you to convert them to a DataFrame object, which simply represents columns and rows that resemble tabular data in Excel—but in Python.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1606929736/pandasdataframe_ihqxej.png" /></a></p>
<h5 id="the-pandas-dataframe-in-action">The pandas DataFrame in action</h5>
<h3 id="how-does-pandas-fit-in-the-pydata-ecosystem-">How does pandas fit in the PyData ecosystem?</h3>
<p>When analyzing data with Python, pandas is often used in conjunction with other packages and tools. These packages range from machine learning packages for producing predictions on data like <a href="https://scikit-learn.org/">scikit-learn</a>, data visualization packages like <a href="https://matplotlib.org/">Matplotlib</a> and <a href="https://seaborn.pydata.org/">seaborn</a>, and tools like <a href="https://jupyter.org/">Jupyter Notebooks</a> for creating and sharing reports that contain code, visualizations, and text. All of these tools comprise what is usually referred to as the <a href="https://pandas.pydata.org/community/ecosystem.html">PyData ecosystem</a>. But you don’t need to worry about learning all of these tools at once—start by focusing on pandas.</p>
<h2 id="six-steps-for-your-pandas-learning-journey">Six steps for your pandas learning journey</h2>
<h3 id="1-learn-basic-python-syntax">1) Learn basic Python syntax</h3>
<p>pandas is a package built for Python, so you need to have a firm grasp of basic Python syntax before you get started with pandas. It’s very easy to get bogged down when learning syntax, as introductory courses often make learning a chore by focusing purely on Python syntax.</p>
<p>As a rule of thumb, you should spend as little time as possible on syntax and learn just enough syntax to get you started with simple tasks with pandas. Here’s a list of pure Python resources to get you started with the basics:</p>
<ul>
<li><a href="https://realpython.com/products/python-basics-book/">A Practical Introduction to Python 3</a></li>
<li><a href="https://docs.python.org/3/tutorial/">Python’s Main tutorial page</a></li>
<li><a href="https://www.coursera.org/learn/python-crash-course">Coursera&#39;s Crash Course in Python taught by Google</a></li>
<li><a href="http://www.datacamp.com/courses/intro-to-python-for-data-science">DataCamp’s Introduction to Python</a></li>
</ul>

<h3 id="2-set-up-your-python-environment">2) Set up your Python environment</h3>
<p>Before working with pandas, you need to have a Python environment set up on your machine. This will allow you to apply your newfound skills and experiment with pandas on your own data.</p>
<p>To set up your Python environment for data science, I recommend installing an <a href="https://www.anaconda.com/products/individual">Anaconda distribution</a> to conveniently install pandas along with relevant data science packages and tools like Jupyter Notebooks.</p>
<p>For a primer on setting up your Python environment with an Anaconda, watch my <a href="https://www.datacamp.com/resources/webinars/setting-up-your-python-environment">hands-on training</a> for a step-by-step tutorial. You can also follow beginner guides for installing Anaconda on <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a> and <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">Mac OS X</a>.</p>
<h3 id="3-learn-by-doing">3) Learn by doing</h3>
<p>At DataCamp, we believe that learning by doing is the best way to learn and apply programming skills. Applying your pandas skills with guided projects will help you get out of your comfort zone, learn how to debug and read documentation, and gain confidence working with real data.</p>
<p>Becoming proficient in pandas requires knowing how to import and export data of different types, manipulating and reshaping data, pivoting and aggregating data, deriving simple insights from DataFrames, and more. Here’s a list of resources that cover these topics and more, containing easy-to-implement pandas code in the form of guided projects:</p>
<ul>
<li>The <a href="https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented">pandas</a> website offers a ton of tutorials on getting started with the package. More importantly, it has many <a href="https://pandas.pydata.org/pandas-docs/stable/getting_started/tutorials.html">community tutorials</a> to get new users started.</li>
<li><a href="https://www.amazon.com/Pandas-Cookbook-Scientific-Computing-Visualization/dp/1784393878">Pandas Cookbook</a> is an excellent book offering a code-rich, project-based approach to working with pandas. It also introduces other packages often used with pandas in the PyData Ecosystem, like seaborn for data visualization.</li>
<li>Coursera’s <a href="https://www.coursera.org/projects/pandas-python-library-beginners-data-science">Pandas Python Library for Beginners in Data Science</a> and <a href="https://www.coursera.org/projects/intermediate-pandas-python-library-data-science">Intermediate Pandas Python Library for Data Science</a> are great ways to get up to speed in pandas in a Jupyter Notebook environment.</li>
<li><a href="https://datacamp.com">DataCamp</a> offers a range of pandas courses ranging from beginner to advanced. The <a href="http://www.datacamp.com/courses/pandas-foundations">pandas foundations</a> course shows you how to import DataFrames, conduct basic exploratory analysis, and analyze time series—all from your browser.</li>
</ul>

<h3 id="4-work-on-projects-with-real-world-data">4) Work on projects with real-world data</h3>
<p>Practicing the pandas skills you’ve learned on real-world data is an excellent way to retain your skills and apply them confidently in your daily work.</p>
<p>There’s a variety of ways to get started on your own project. For starters, if your daily work requires you to work with tabular data, start using pandas instead of spreadsheet software like Google Sheets and Microsoft Excel. While this could take more time initially, it will help you sharpen and retain your pandas skills, making you faster and more proficient in the long run.</p>
<p><a href="http://www.kaggle.com">Kaggle</a> contains a range of real-world datasets and machine learning competitions. While I encourage you to learn machine learning, it’s best suited for more advanced learners, so make sure to initially focus on projects focused on data analysis and exploration with pandas. You can do this by writing data analysis notebooks on Kaggle or blogging platforms like <a href="http://www.medium.com">Medium</a>.</p>
<h3 id="5-master-the-art-of-debugging">5) Master the art of debugging</h3>
<p>One of the most overlooked skills any aspiring programmer needs to hone is debugging. Every Python and pandas practitioner, regardless of skill level, faces errors and problems with their code. When you encounter errors in your pandas code, these resources are at your disposal:</p>
<ul>
<li><a href="http://google.com/">Google</a> is the first place I go when faced with errors with my code. Almost always, the answer to my pandas query will be in the first ten search results.</li>
<li><a href="https://stackoverflow.com/">Stack Overflow</a> is the largest community of developers where practitioners can seek help with debugging code by asking and answering programming questions. It currently contains around <a href="https://stackoverflow.com/questions/tagged/pandas?sort=MostVotes&amp;edited=true">180,000 questions</a> on pandas. If you want to post a question yourself, make sure to follow their <a href="https://stackoverflow.com/help/how-to-ask">best practices when phrasing questions</a>.</li>
<li>Documentation will always be your friend, and the <a href="https://pandas.pydata.org/docs/">pandas documentation</a> is extensive. It covers all the possible functionalities of pandas as well as best practices.</li>
</ul>

<h3 id="6-keep-expanding-your-pandas-skills">6) Keep expanding your pandas skills</h3>
<p>Keep on building and expanding your pandas skills. Many resources cover advanced pandas topics. For example, Ashish Kumar’s <a href="https://www.amazon.com/Mastering-pandas-complete-installation-techniques-ebook/dp/B07WFM4JXZ">Mastering pandas</a> covers techniques to speed up and simplify your pandas workflow. This <a href="https://realpython.com/python-pandas-tricks/">Real Python blog post</a> covers tips and tricks for advanced users. You can also start learning how other packages in the PyData Ecosystem extend your pandas ability, like <a href="https://seaborn.pydata.org/">seaborn</a> and <a href="https://plotly.com/">plotly</a>, for creating interactive plots.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1607005373/How_to_Learn_Pandas_lqualj.gif" /></a>
<br></p>
<h5 id="an-interactive-plot-made-with-plotly">An interactive plot made with Plotly</h5>
<p>A great way to expand your pandas skills is by revisiting some of the pandas code you wrote at the beginning of your learning journey. Try to spot opportunities to simplify your code, adopt best practices, and leverage other packages in the PyData ecosystem.</p>
<p>I hope you feel inspired by this blog post and that these resources are useful to you. Mastering pandas is a lifelong learning journey—one that I am still on myself. At DataCamp, pandas is covered across our core curriculum, and we’re continually improving and adding to our content. Lastly, I invite you to join thousands of learners in the <a href="https://support.datacamp.com/hc/en-us/articles/360001915993-The-DataCamp-Slack-Community-An-Overview">DataCamp Slack Community</a> to get support on your pandas journey!</p>
<p><em>Adel Nehme is a Data Science Evangelist at DataCamp. He was previously a Content Developer where he collaborated with instructors on a range of Python courses and created our course <a href="http://datacamp.com/courses/cleaning-data-in-python">Cleaning Data in Python</a>.</em></p>


https://www.datacamp.com/community/blog/how-to-learn-pandas

Here’s all you need to know to get started with pandas.

How to Learn pandas

<h2>Retrieving Data From The Database</h2><p>This chapter provides a brief introduction to working with relational databases in a marketing context. You'll learn about their structure,  how to talk about them using database lingo, and how to begin an  analysis by using simple SQL commands to select and summarize columns  from database tables.</p><h2>Filter Data And Limit Your Output</h2><p>In this chapter, you'll learn how to filter tables for rows that satisfy your criteria of interest with the help of basic comparison operators and combinations of multiple criteria.</p><h2>What's Next?</h2><p>Besides selecting columns and filtering rows, there is much more that you can do with SQL! This chapter will present you with some examples that will motivate you to continue to develop this newly gained skill!</p>

https://www.datacamp.com/community/open-courses/sql-tutorial-for-marketers

The best marketers and growth hackers are data-driven. Learn how to use Structured Query Language (SQL) to query a database containing bank clients and marketing campaigns data.

SQL Tutorial for Marketers

<h2>Tidy Data in Python</h2><p>It is often said that data scientists spend only 20% of their time analyzing their data, and 80% of time cleaning it. Indeed, maintaining a tidy, easy-to-use dataset is crucial in our age of big data. In the paper Tidy Data, veteran statistician Hadley Wickham gives definitions of tidy and messy data so that all data scientists can keep their work organized. In this mini-course, you'll learn to transform messy datasets into tidy datasets using the pandas package in python. Let's get started!</p>

https://www.datacamp.com/community/open-courses/tidy-data-in-python-mini-course

Most of the world's data aren't sorted cleanly nor organized. In this course, you'll learn about the standards for tidy data and you'll create tidy data sets from messy ones.

Tidy Data in Python Mini-Course

<p>SQL Server is a relational database management system. One of the key principles of the relational database is that data is stored across multiple tables.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_564/v1603485855/join1_nitybm.png" /></center>

<p>We will need to be able to join tables together in order to extract the data we need. We use <code>primary</code> and <code>foreign</code> keys to join tables.</p>
<h2 id="primary-key">Primary Key</h2>
<p>A primary key is a column that is used to uniquely identify each row in a table. This uniqueness can be achieved by using a sequential integer as an identity column. Or sometimes, existing columns naturally contain unique values, and they can be used.</p>
<p>In the below example, we can see the first few rows from the <code>artist</code> table. It has two columns, <code>artist_id</code>, and <code>name</code>. The <code>artist_id</code> column acts as a primary key for this table, it is an integer column, and each value is different.</p>
<pre><code>+-----------+-------------------+
| artist_id | name              |
|-----------+-------------------|
| 1         | AC/DC             |
| 2         | Accept            |
| 3         | Aerosmith         |
| 4         | Alanis Morissette |
| 5         | Alice In Chains   |
+-----------+-------------------+
</code></pre><p>Now let&#39;s look at the <code>album</code> table. Can you spot the primary key? Yes, it&#39;s the <code>album_id</code> column. It&#39;s common for the primary key to be named &quot;table-name underscore id&quot;. But, you&#39;ll have noticed there is also an <code>artist_id</code> column. That also consists of integers and has the same name as the <code>artist_id</code> column in the artist table. What does this mean?</p>
<pre><code>+----------+-------------------------+-----------+
| album_id | title                   | artist_id |  
|----------+-------------------------+-----------|
| 1        | For Those About To Rock | 1         |
| 2        | Balls to the Wall       | 2         |
| 3        | Restless and Wild       | 2         |
| 4        | Let There Be Rock       | 1         |
| 5        | Big Ones                | 3         |
+----------+-------------------------+-----------+
</code></pre><h2 id="foreign-keys">Foreign Keys</h2>
<p>Well, from the previous output, it is clear that the <code>artist_id</code> in the artist table acts as a <code>Foreign_key</code> to the album table. The <code>FOREIGN KEY</code> constraint is used to prevent actions that would destroy links between tables.</p>
<ul>
<li><code>artist</code> table</li>
</ul>
<pre><code>+-----------+-------------------+
| artist_id | name              |
|-----------+-------------------|
| 1         | AC/DC             |
| 2         | Accept            |
| 3         | Aerosmith         |
| 4         | Alanis Morissette |
| 5         | Alice In Chains   |
+-----------+-------------------+
</code></pre><ul>
<li><code>album</code> table</li>
</ul>
<pre><code>+----------+-------------------------+-----------+
| album_id | title                   | artist_id |  
|----------+-------------------------+-----------|
| 1        | For Those About To Rock | 1         |
| 2        | Balls to the Wall       | 2         |
| 3        | Restless and Wild       | 2         |
| 4        | Let There Be Rock       | 1         |
| 5        | Big Ones                | 3         |
+----------+-------------------------+-----------+
</code></pre><p>This means that when the <code>artist_id</code> in the album table matches the <code>artist_id</code> in the artist table, those rows can be linked as a <code>JOIN</code>.</p>
<h2 id="joining-album-and-artist">Joining Album and Artist</h2>
<p>Rows 1 and 4 of the artist table have an <code>artist_id</code> of 1. The <code>artist_id</code> 1 in the artist table is AC/DC. So, when we join the two tables together, we return the album details from the album table, and the corresponding artist details from the artist table - joined using the <code>artist_id</code> field, which is common to both tables.</p>
<p>This is known as an <code>inner</code> join.</p>
<pre><code class="lang-SQL">SELECT
  album_id,
  title,
  album.artist_id,
  name AS artist_name
FROM album
INNER JOIN artist ON artist.artist_id = album.artist_id
WHERE album.artist_id = 1;
</code></pre>
<pre><code>+----------+-------------------------+-----------+-------------+
| album_id | title                   | artist_id | artist_name |
|----------+---------------------------------------+-----------|
| 1        | For Those About To Rock | 1         | AC/DC       |
| 4        | Let There Be Rock       | 1         | AC/DC       |
+----------+-------------------------+-----------+-------------|
</code></pre><p>We joined the album and artist tables by matching the <code>artist_id</code> from the artist table to the <code>artist_id</code> in the album table.</p>
<p>When selecting columns with the same name from different tables, you must fully qualify the column name. Otherwise, SQL Server will not know which table to <code>SELECT</code> the column from.</p>
<p>To fully qualify a column, you have to prefix it with the relevant table name, followed by a period.</p>
<h3 id="inner-join-syntax">Inner Join Syntax</h3>
<p>Here&#39;s a generic example to remind you of the syntax. When we select the relevant columns from the main table, <code>table_A</code> in this case, then select any additional column we want to join to, which in this case is <code>table_B</code>. Then we specify the join using the keywords <code>INNER JOIN</code> and <code>ON</code>, providing the necessary key columns from both the tables.</p>
<pre><code class="lang-sql">SELECT
    table_A.columnX,
    table_A.columnY,
    table_B.columnZ,
FROM table_A
INNER JOIN table_B ON table_B.foreign_key = table_A.primary_key;
</code></pre>
<h2 id="not-using-a-where-clause">Not Using a <code>WHERE</code> Clause</h2>
<p>In this example, we don&#39;t specify a <code>WHERE</code> clause, so we return ALL combinations of ALL matches between the artist and album tables, again, based on the <code>artist_id</code> column, which is common to both.</p>
<pre><code class="lang-SQL">SELECT
  album_id,
  title,
  album.artist_id,
  name AS artist_name
FROM album
INNER JOIN artist on artist.artist_id = album.artist_id;
</code></pre>
<pre><code>+----------+---------------------------------------+-----------+
| album_id | title                   | artist_id | artist_name |
|----------+---------------------------------------+-----------|
| 1        | For Those About To Rock | 1         | AC/DC       |
| 4        | Let There Be Rock       | 1         | AC/DC       |
| 2        | Balls To The Wall       | 2         | Accept      |
| 3        | Restless and Wild       | 2         | Accept      |
+----------+---------------------------------------+-----------+
</code></pre><h2 id="multiple-inner-join">Multiple Inner Join</h2>
<p>You can also join more than two tables using <code>INNER JOINS</code>. You simply provide an additional <code>INNER JOIN</code> for each combination of tables. In our code below, we join tables A and B, and also B and C.</p>
<pre><code class="lang-SQL">SELECT
  table_A.columnX,
  table_A.columnY,
  table_B.columnZ table_C columnW
FROM table_A
INNER JOIN table_B ON table_B.foreign_key = table_A.primary_key
INNER JOIN table_C ON table_C.foreign_key = table_B.primary_key;
</code></pre>
<p>To learn more about joining tables, please see this video from our course <a href="https://campus.datacamp.com/courses/introduction-to-sql-server/joining-tables?ex=1">Introduction to SQL Server</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/DRDFtejKZs8" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>This content is taken from DataCamp’s <a href="https://www.datacamp.com/courses/introduction-to-sql-server">Introduction to SQL Server</a> course by John MacKintosh.</p>


https://www.datacamp.com/community/tutorials/sql-joins

(Tutorial) Joins in SQL

Joins in SQL

<p>Array&#39;s are the foundation for all data science in Python. Arrays can be multidimensional, and all elements in an array need to be of the same type, all integers or all floats, for example.</p>
<h2 id="advantages-of-using-an-array">Advantages of using an Array</h2>
<ul>
<li>Arrays can handle very large datasets efficiently</li>
<li>Computationally-memory efficient</li>
<li>Faster calculations and analysis than lists</li>
<li>Diverse functionality (many functions in Python packages). With several Python packages that make trend modeling, statistics, and visualization easier.</li>
</ul>
<h2 id="basics-of-an-array">Basics of an Array</h2>
<p>In Python, you can create new datatypes, called arrays using the NumPy package. NumPy arrays are optimized for numerical analyses and contain only a single data type.</p>
<p>You first import NumPy and then use the <code>array()</code> function to create an array. The <code>array()</code> function takes a list as an input.</p>
<pre><code class="lang-python">import numpy
my_array = numpy.array([0, 1, 2, 3, 4])
print(my_array)
</code></pre>
<pre><code>[0, 1, 2, 3, 4]
</code></pre><p>The type of <code>my_array</code> is a <code>numpy.ndarray</code>.</p>
<pre><code class="lang-python">print(type(my_array))
</code></pre>
<pre><code>&lt;class &#39;numpy.ndarray&#39;&gt;
</code></pre><h2 id="array-examples">Array Examples</h2>
<h3 id="example-of-creating-an-array">Example of creating an Array</h3>
<p>In the below example, you will convert a list to an array using the <code>array()</code> function from NumPy. You will create a list <code>a_list</code> comprising of integers. Then, using the <code>array()</code> function, convert it an array.</p>
<pre><code class="lang-python">import numpy as np

a_list = [1, 2, 3, 4]
a_list
</code></pre>
<pre><code>[1, 2, 3, 4]
</code></pre><pre><code class="lang-python">an_array = np.array(a_list)
an_array
</code></pre>
<pre><code>array([1, 2, 3, 4])
</code></pre><h3 id="example-of-an-array-operation">Example of an Array operation</h3>
<p>In the below example, you add two numpy arrays. The result is an element-wise sum of both the arrays.</p>
<pre><code class="lang-python">import numpy as np

array_A = np.array([1, 2, 3])
array_B = np.array([4, 5, 6])

print(array_A + array_B)
</code></pre>
<pre><code>[5 7 9]
</code></pre><h3 id="example-of-array-indexing">Example of Array indexing</h3>
<p>You can select a specific index element of an array using indexing notation.</p>
<pre><code class="lang-python">import numpy as np

months_array = np.array([&#39;Jan&#39;, &#39;Feb&#39;, &#39;March&#39;, &#39;Apr&#39;, &#39;May&#39;])
print(months_array[3])
</code></pre>
<pre><code>Apr
</code></pre><p>You can also slice a range of elements using the slicing notation specifying a range of indices.</p>
<pre><code class="lang-python">print(months_array[2:5])
</code></pre>
<pre><code>[&#39;March&#39;, &#39;Apr&#39;, &#39;May&#39;]
</code></pre><h2 id="interactive-example-of-a-list-to-an-array">Interactive Example of a List to an Array</h2>
<p>In the below example, you will import <code>numpy</code> using the alias <code>np</code>. Create <code>prices_array</code> and <code>earnings_array</code> arrays from the lists <code>prices</code> and <code>earnings</code>, respectively. Finally, print both the arrays.</p>
<pre><code class="lang-python"># IMPORT numpy as np
import numpy as np

# Lists
prices = [170.12, 93.29, 55.28, 145.30, 171.81, 59.50, 100.50]
earnings = [9.2, 5.31, 2.41, 5.91, 15.42, 2.51, 6.79]

# NumPy arrays
prices_array = np.array(prices)
earnings_array = np.array(earnings)

# Print the arrays
print(prices_array)
print(earnings_array)
</code></pre>
<p>When you run the above code, it produces the following result:</p>
<pre><code>[170.12  93.29  55.28 145.3  171.81  59.5  100.5 ]
[ 9.2   5.31  2.41  5.91 15.42  2.51  6.79]
</code></pre><p><a href="https://campus.datacamp.com/courses/introduction-to-python-for-finance/arrays-in-python?ex=2">Try it for yourself</a>.</p>
<p>To learn more about NumPy arrays in Python, please see this video from our course <a href="https://campus.datacamp.com/courses/introduction-to-python-for-finance/arrays-in-python?ex=1">Introduction to Python for Finance</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/E2bKxhix3aU?start=85" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>This content is taken from DataCamp’s <a href="https://www.datacamp.com/courses/introduction-to-python-for-finance">Introduction to Python for Finance</a> course by Adina Howe.</p>


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