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

<nav><h2 id="table-of-contents">Table of Contents</h2>
<ul>
<li><a href="#kaggle-notebooks">Kaggle Notebooks</a></li>
<li><a href="#kaggle-datasets">Kaggle Datasets</a>
<ul>
<li><a href="#what-are-kaggle-datasets-">What Are Kaggle Datasets?</a></li>
<li><a href="#dataset-search-filters">Dataset Search Filters</a></li>
<li><a href="#data-explorer">Data Explorer</a></li>
<li><a href="#datasets-for-beginners">Datasets For Beginners</a></li>
<li><a href="#custom-datasets">Custom Datasets</a></li>
</ul>
</li>
</ul>
</nav>
<h2 id="kaggle-notebooks">KAGGLE NOTEBOOKS</h2>
<p>A "Kaggle Notebook" is a free jupyter notebook server that can be GPU integrated. Just like DataCamp Workspace notebooks, it allows you to perform machine learning operations on cloud computers instead of doing it on your own computer. Each time you create a Kaggle Notebook, you can edit and run its content in the browser. There is no need to set up your own jupyter notebook environment, just enter Kaggle, create a notebook and start using it on the browser. You can see the notebooks you have created before on the Kaggle Notebook page and you can also review other people&#39;s notebooks.</p>
<p>To create a Notebook, click on &quot;New Notebook&quot; after you navigate to the Kaggle Code page (Figure 3.1). After this process, cloud resources will be allocated for you and a notebook will be created instantly. You can give your notebook a name by clicking on the notebook text in the upper left corner. As you can see at a first glance, many options and features found in jupyter notebooks are also available here.</p>
<p>The most frequent questions about Kaggle Notebooks are how to share a notebook publicly, how to add another person as a collaborator, how to import a dataset to a notebook, and how to use the GPU. You can see the buttons required to perform each of these operations in Figure 3.2.</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226116/create_notebook_ycwbc8.png" alt="Figure 3.1: Kaggle Code"></centre>
<center>Figure 3.1: Kaggle Code</center>
<br>
<p>First, you have to commit a version of your Notebook to make it public. You can create a new version of your Notebook by clicking the &quot;Save Version&quot; button at the top right. Then, you can make your notebook accessible to everyone by clicking the &quot;Share&quot; button on the left of this button, or you can add others as collaborators using the same menu. To import a dataset, simply click on the &quot;Add data&quot; button under the &quot;Save Version&quot; button on the right menu, and select the dataset you want to add. To activate the GPU, you need to select the GPU option from the accelerator section in the menu on the right side. The maximum GPU time you can use on Kaggle is set at 30 hours per week.</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226116/notebook_r620gk.png" alt="Figure 3.2: Kaggle Notebook"></centre>
<center>Figure 3.2: Kaggle Notebook</center>

<p>All other features of notebooks are explained in detail in the <a target="_blank" href="https://www.kaggle.com/docs/notebooks">Kaggle documentation.</a></p>
<h2 id="kaggle-datasets">KAGGLE DATASETS</h2>
<h3 id="what-are-kaggle-datasets-">WHAT ARE  KAGGLE DATASETS?</h3>
<p>Kaggle is a data science platform but it also supports dataset handling. "Kaggle Datasets" allows you to create your own custom datasets, share them with others and easily import them into your notebooks. Additionally, you can add private datasets which would only be visible to you.</p>
<p>What makes this feature one of the most important ones in Kaggle is that it gives you  access to a wide variety of top-quality datasets shared by other users. You can easily find the datasets you want with just a few search and filtering methods.</p>
<h3 id="dataset-search-filters">DATASET SEARCH FILTERS</h3>
<p>To search for a dataset, write your keywords in the search field, as shown in Figure 4.1. Here you can see that we can access several datasets about the pandemic just by typing &quot;Covid&quot; in the search bar.</p>
<p>If you click on &quot;Filters&quot; on the right side of the search bar, more filtering options will appear (Figure 4.2). With these, you can narrow your search by entering dataset tags, file type, and other values like the minimum or maximum size of the dataset (Figure 4.3).</p>
<br>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226092/datasets_z4c6lk.png" alt="Figure 4.1: Dataset Search Filters"></centre>
<center>Figure 4.1: Dataset Search Filters</center>
<br>
<p>Kaggle allows you to download any dataset for free, but depending on what you are going to use it for, you may need to pay attention to the license type of the datasets. In some cases, it is possible that you may need to obtain additional permissions from their owners in case you want to use a dataset for an academic paper or in case you intend to use it for commercial purposes, for example.</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226092/datasets_computer_vision_kcadmg.png" alt="Figure 4.2: Dataset Search Filters by Tags"></centre>
<center>Figure 4.2: Dataset Search Filters by Tags</center>
<br>
<p>There are  three main license types on Kaggle:</p>
<ol>
<li><strong>Creative Commons</strong>: There are several kinds of Creative Commons licenses:</li>
<ol type = "a">
<li>CCO, which stands for public domain and means that the dataset is available to everyone under any circumstances.</li>
<li>CC-BY, which requires the dataset user to credit its owner.</li>
<li>CC-BY-SA, which also requires the owner to be credited and adds the condition that the dataset keeps the same kind of license even after it&#39;s modified.</li>
</ol>
<li>GPL: This license basically provides four main usage options:</li>
<ol type = "a">
<li>Firstly, you get unlimited use of a dataset.</li>
<li>You also have the possibility to examine how the dataset works, and modify it.</li>
<li>Additionally, you are entitled to the unlimited distribution of copies of the dataset.</li>
<li>And lastly, you can distribute the modified version of the dataset as well.</li>
</ol>
<li>Open Database: This allows users to share, modify and use the dataset but it makes it mandatory to establish the same kind of license for the modified dataset.</li>
</ol>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226091/datasets_filter_opjfyj.png" alt="Figure 4.3: Dataset Search Filters"></centre>
<center>Figure 4.3: Dataset Search Filters</center>
<br>
<h3 id="data-explorer">DATA EXPLORER</h3>
<p>The Data Explorer section allows you to quickly browse through the content and structure of the datasets. It gives you an overview of the files and the columns in the data, as well as their histogram graphs (Figure 4.4).</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226091/dataset_explorer_ebgf9u.png" alt="Figure 4.4: Data Explorer"></centre>
<center>Figure 4.4: Data Explorer</center>

<h3 id="datasets-for-beginners">DATASETS FOR BEGINNERS</h3>
<p>The following datasets are fun and easy to play with as a beginner. You can fetch these to your notebooks and start getting your hands dirty by visualizing the data. Once you come up with an idea, you can even build machine learning models with some of these datasets.</p>
<ul>
<li><a target="_blank" href="https://www.kaggle.com/rtatman/digidb">Digimon Database</a>: A database of Digimon and their moves from Digimon Story CyberSleuth.</li>
<li><a target="_blank" href="https://www.kaggle.com/rtatman/animal-bites">Animal Bites</a>: Data on over 9,000 bites, including rabies tests.</li>
<li><a target="_blank" href="https://www.kaggle.com/crawford/80-cereals">80 Cereals</a>: Nutrition data on 80 cereal products.</li>
<li><a target="_blank" href="https://www.kaggle.com/datafiniti/womens-shoes-prices">Women&#39;s Shoe Prices</a>: A list of 10,000 women&#39;s shoes and the prices at which they are sold.</li>
<li><a target="_blank" href="https://www.kaggle.com/groundhogclub/groundhog-day">Groundhog Day Forecasts and Temperatures</a>: How accurate is Punxsutawney Phil&#39;s winter weather forecast?</li>
</ul>
<h3 id="custom-datasets">CUSTOM DATASETS</h3>
<p>You can also upload and use your own datasets in Kaggle. This feature comes in handy when you have your own dataset or when you've modified a dataset and want to use it in your notebook. In order to upload a dataset, first, you need to zip your main dataset file. Then click on &quot;New Dataset&quot; in the Datasets section. Give your dataset a name and upload your zip file (Figure 4.5).</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646226092/dataset_import_qljtq7.png" alt="Figure 4.5: Importing Custom Datasets"></centre>
<center>Figure 4.5: Importing Custom Datasets</center>
<br>
<p>And that&#39;s it. You can now fetch the uploaded dataset to your notebook and start using it, as shown in section 2. If you want to keep the dataset private, make sure that the label in the right bottom corner of the uploading screen reads &quot;Private&quot;. If you don&#39;t want it to be private, you can click on the label and change it to "Public".</p>


https://www.datacamp.com/community/tutorials/tutorial-kaggle-datasets-tutorials-kaggle-notebooks

Learn about Kaggle datasets and notebooks and get a head start on creating your Kaggle profile.

Explore kaggle with this tutorial and get a head start on creating your Kaggle profile. Learn your way around Kaggle Notebooks.

Kaggle Datasets Tutorial: Kaggle Notebooks

https://www.datacamp.com/community/tutorials/tutorial-time-series-forecasting

A detailed guide to time series forecasting. Learn to use python and supporting frameworks. Learn about the statistical modelling involved. 

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