http://datacamp-community-prod.s3.amazonaws.com/02213cb4-b391-4516-adcd-57243ced8eed

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

http://datacamp-community-prod.s3.amazonaws.com/f04456d7-8e61-482f-9cc9-da6f7f25fc9b

A quick guide with code samples to the basics of the Python data analysis library Pandas.

Pandas Cheat Sheet for Data Science in Python

https://www.datacamp.com/community/podcast/beyond-the-language-wars-r-python-for-the-modern-data-scientist

<p>In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community. &nbsp;</p>

In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.  

Beyond the Language Wars: R & Python for the Modern Data Scientist

https://www.datacamp.com/community/blog/beyond-the-language-wars-r-python-for-the-modern-data-scientist

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/a-cheat-sheet-for-python-pandas-43gyt7bw3a9

A quick guide to the basics of the Python data analysis library Pandas, including code samples.

A Cheat Sheet For Python Pandas

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

<h2 id="what-will-i-learn-">What will I learn?</h2>
<p><a href="https://www.datacamp.com/courses/building-dashboards-with-dash-and-plotly">Building Dashboards with Dash and Plotly</a> will introduce you to the go-to tools for creating visualizations within Python. Dash, an open-source framework, is one of the most popular tools available within Plotly, and enables you to create interactive visualizations. It’s a simple tool to use, with many of its lines of code pre-written.</p>
<p>The first chapter (which, as always on DataCamp, is free for registered users) provides you with a concise overview of this course’s content, introducing you to HTML and CSS, and building multi-graph dashboards based on data from an e-commerce organization.</p>
<p>Subsequent chapters will dive deeper: you’ll learn more about using HTML and CSS to create individual charts and add interactivity to your dashboards in the form of filters and tooltips. You’ll learn how to create filters that are applied at the individual visualization and the dashboard level.</p>
<h2 id="why-should-i-take-this-course-">Why should I take this course?</h2>
<p>Visualizing data is the best way to communicate the insights within your dataset, and as you use Python to extrapolate more complex data you will want to display your findings in a way that people can understand and act upon. There are various different chart types, and dashboards allow you to combine them to get an overall picture of what the data is telling you.</p>
<p>Additionally, this course’s instructor Alex Scriven is a Senior Data Scientist for the New South Wales government. With a history of lecturing at universities, creating Masters-level courses, and applying data science to commercial settings, he makes for the ideal teacher—plus, if you love Australian accents, you’re in luck!</p>
<h2 id="are-there-any-prerequisites-">Are there any prerequisites?</h2>
<p>Before taking this course, you should have an <a href="https://www.datacamp.com/courses/intermediate-python">intermediate-level</a> understanding of Python, and you should also take our course on <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-plotly-in-python">data visualization in Plotly</a>. If you did the latter a while back, don’t worry: before dashing straight to Dash, the first chapter will provide a recap of Plotly.</p>
<h2 id="what-s-in-this-course-">What’s in this course?</h2>
<h4 id="chapter-1">Chapter 1</h4>
<iframe width="560" height="315" src="https://www.youtube.com/embed/Hw7XXzvZtuE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>Take Plotly to the next level with Dash. After briefly revising Plotly, you&#39;ll dive straight into building your first Dash app. You&#39;ll then get your first taste of HTML and how Dash harnesses this vital web development language to structure larger Dash apps. Finally, you&#39;ll put all this together in your first multi-graph dashboard for a global e-commerce organization.</p>
<h4 id="chapter-2">Chapter 2</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634045079/sales_dashboard2_hmptlc.png" /></center>

<p>In this exercise, you’ll use your data skills to respond to a series of requests from management, including placing charts side-by-side and making the organization’s logos more prominent.
Take control of the elements in your Dash apps with HTML and CSS. Learn what each of these is and how they are at the core of modern web development, then incorporate their powerful abilities in your Dash apps to change your objects’ size, color, and placement.</p>
<h4 id="chapter-3">Chapter 3</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634045079/reusablecomponent_kdvwxk.png" /></center>

<p>Creating reusable components is one way to ensure that your code can be neatly packaged as much as possible, and in this exercise you’ll learn how to apply this to the organization’s logo.</p>
<p>Turn your Dash apps into user-driven experiences by mastering interactive components. Learn how to include dropdowns, date pickers, and event-free text fields to modify elements on your Dash apps and build truly self-service dashboards your users can explore.</p>
<h4 id="chapter-4">Chapter 4</h4>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634045080/endchapterdash2_nuja6a.png" /></center>

<p>In this exercise, you’ll add more interactivity to your dashboards by allowing the viewer greater control over its tabular elements.</p>
<p>Unleash the full power of Dash app interactivity by triggering changes to one graph when a user clicks on or hovers over, another graph. Additionally, you&#39;ll learn the basics of Dash&#39;s Data Table module for creating tables that can be easily filtered, sorted, and paginated; as well as trigger changes in other graphs as well.</p>
<p>Are you ready to bring your Python data to life through visualizations in Dash? Click <a href="https://www.datacamp.com/courses/building-dashboards-with-dash-and-plotly">here</a> to take this course!</p>


https://www.datacamp.com/community/blog/building-dashboards-with-dash-and-plotly

You’ve learned how to use Python to collect data—now it’s time to learn how to present it! With our new course in Dash, you’ll learn the ins and outs of building dashboards with Python’s most popular visualization tool!

New Course: Building Dashboards with Dash and Plotly

<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>The&nbsp;data.table R package provides an enhanced version of <code>data.frame</code> that allows you to do <a href="https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping" target="_blank">blazing fast</a> data manipulations. The <code>data.table</code> R package is being used in different fields such as finance and genomics&nbsp;and is especially useful for those of you that are working with large data sets (for example, 1GB to 100GB in RAM).</p>

<p>Although its typical syntax structure is not hard to master, it is unlike other things you might have seen in R. Hence the reason to create this cheat sheet. <a href="https://www.datacamp.com/courses/data-analysis-the-data-table-way/?utm_source=blog&amp;utm_medium=blog&amp;utm_content=cheat%20sheet%20blog&amp;utm_campaign=data.table%20cheat%20sheet" target="_blank" title="data.table tutorial">DataCamp</a>&rsquo;s <code>data.table</code> cheat sheet is a quick reference for doing data manipulations in R with the <code>data.table</code> R package and syntax</p>

<p>The cheat sheet will guide you from doing simple data manipulations using <code>data.table</code>&rsquo;s basic <code>i, j, by </code> syntax, to chaining expressions, to using the famous <code>set()</code>-family.</p>

<p>(To practice using the <code>data.table</code> package, try the free first chapter of this <a href="https://www.datacamp.com/courses/data-analysis-the-data-table-way/?utm_source=blog&amp;utm_medium=blog&amp;utm_content=cheat%20sheet%20blog&amp;utm_campaign=data.table%20cheat%20sheet">course</a>, which includes exercises in subsetting a <code>data.table</code> and manipulating data with <code>data.table</code>&#39;s&nbsp;<code>by</code>, <code>.N</code>, and <code>j</code> syntax. )</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/008e41e8-7526-4bbc-806b-8c828dfb81d4" target="_blank" title="data table cheat sheet"><img alt="data.table R package tutorial cheat sheet" class="aligncenter wp-image-437 size-medium" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839980/The_data.table_R_Package_Cheat_Sheet_fdbgnt.png" style="width: 800px; height: 551px;" /></a></p>


https://www.datacamp.com/community/tutorials/data-table-cheat-sheet

The data.table cheat sheet helps you master the syntax of this R package, and helps you to do data manipulations.

 data.table R Package Cheat Sheet

The data.table R Package Cheat Sheet

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

Are you ready to begin your SQL journey? Here’s a step-by-step guide on how to get started.

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Word Frequency: The Difference Between Simple and Weighted Counting

A Cheat Sheet For Python Pandas

Getting Started with the Tidyverse: Tutorial

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PySpark Cheat Sheet: Spark DataFrames in Python

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SQL Tutorial for Marketers

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Beyond the Language Wars: R & Python for the Modern Data Scientist (with Rick Scavetta and Boyan Angelov)

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New Course: Building Dashboards with Dash and Plotly

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