http://datacamp-community-prod.s3.amazonaws.com/72771032-0653-4d87-a798-4a83879e99c8

Tackle time series in R with this xts (Extensible Time Series) cheat sheet! 

xts Cheat Sheet: Time Series in R

http://datacamp-community-prod.s3.amazonaws.com/21fdc814-3f08-4aa9-90fa-247eedefd655

This Jupyter Notebook cheat sheet will help you to find your way around the well-known Jupyter Notebook App, a subproject of Project Jupyter.

Jupyter Notebook Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Jupyter_Notebook_Cheat_Sheet.pdf

https://www.datacamp.com/community/podcast/scaling-ai-adoption-in-financial-services

<p>In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer &amp; Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.</p>

In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

Scaling AI Adoption in Financial Services

https://www.datacamp.com/community/blog/scaling-ai-adoption-in-financial-services

https://www.datacamp.com/community/podcast/building-high-performing-data-teams

<p>In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success.&nbsp;</p>

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success. 

Building High Performing Data Teams

https://www.datacamp.com/community/blog/building-high-performing-data-teams

https://www.datacamp.com/community/news/creating-smart-cities-with-data-science-ofs0psrh048

Amen Ra Mashariki deep dives into the state of data literacy in government agencies, the use cases he worked on to make the City of New York smarter, and more!

Creating Smart Cities With Data Science

https://www.datacamp.com/community/news/the-credibility-crisis-in-data-science-with-skipper-seabold-6bbmgpwy1qm

Skipper Seabold, a Director of Data Science at Civis Analytics, discusses the current and looming credibility crisis in data science.

The Credibility Crisis in Data Science (with Skipper Seabold)

https://www.datacamp.com/community/news/choosing-r-or-python-for-data-analysis-an-infographic-rhcw8x3m5wa

Wondering whether you should use R or Python for your next data analysis post? Check our infographic "Data Science Wars: R vs Python".

Choosing R or Python for Data Analysis? An Infographic

<h2 id="why-did-we-launch-data-science-competitions-">Why did we launch data science competitions?</h2>
<p>We started DataCamp to help everyone build their data skills, through learning by doing. We’re constantly innovating, making it easier for learners to go from learning to applying their skills to solve real-world challenges. For example, we’ve created over 80 different projects, we’ve launched our in-browser tool Workspace, and we’ve designed two of our certification’s assessments to be based upon how well learners can apply what they know.</p>
<p>We are now pleased to announce the launch of <a href="https://app.datacamp.com/learn/competitions">data science competitions</a>, in which you’ll perform an analysis based on a dataset and problem statement. This data analysis will be performed and presented in a Workspace notebook, and it will then be assessed through an upvote system and a panel of experienced data scientists. There are huge prizes to be won and these will be a regular occurrence, starting today!</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599264/competitionsprizes_gd4a8t.png" /></center><br>

<h2 id="what-is-the-first-datacamp-data-science-competition-">What is the first DataCamp data science competition?</h2>
<p>Our first data science competition, &quot;<a href="https://app.datacamp.com/learn/competitions/russian-alcohol-promotions">Designing a promo strategy for a drinks company</a>&quot;, is now live, so if you’re curious go check it out.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599265/Screenshot_2021-10-18_at_15.09.45_dplfq6.png" /></center><br>

<p>In this challenge, you’re asked to create promotion recommendations for a drinks company based on previous promotions and historical alcohol consumption data in various regions of Russia.</p>
<h2 id="what-entering-datacamp-competitions-can-do-for-your-career-">What entering DataCamp competitions can do for your career?</h2>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599270/wes-hicks-4-EeTnaC1S4-unsplash_fendqd.jpg" /></center><br>

<p>DataCamp Competitions are designed to help users gain real-world data science experience—similar to what a data analyst would typically encounter in the workplace. All learners stand to strengthen their data science experience and coding skills by participating, as well as the opportunity to build a strong portfolio whose contents will be given an upvote score as part of the judging process. Furthermore, non-participants will have the opportunity to learn from submitted publications by seeing how others have found creative solutions to complex data problems.</p>
<h2 id="what-competitions-entail">What competitions entail</h2>
<p>Every week a new challenge is launched based upon a real-world dataset and problem statement drawn up by our team. Once a competition is launched, learners have two weeks to complete their analysis and publish it using Workspace, our online data science notebook.</p>
<p>Once you’ve submitted your analysis you can share your publication with your friends and on your social media to get upvotes on it. This will also enable them to upvote it early, before voting to the wider public begins.</p>
<p>After the submission deadline, the judging process begins. This step is two-fold:</p>
<ul>
<li>DataCamp learners and other data science enthusiasts give publications upvotes, and the most popular will be reviewed by the judges.</li>
<li>The judges will spend the following week evaluating these publications and selecting their own top five out of these.</li>
</ul>
<p>In the case of “Designing a promo strategy for a drinks company&quot;:</p>
<ul>
<li>Submissions will close on October 31, 2021 at 11:59 PM PT and voting will start on November 1. On this date all workspace notebook publications will be opened to the wider public for upvoting, and you’ll be able to upvote your favorite publications to boost their chances of winning.</li>
<li>Voting will end on November 7.</li>
<li>Judges will determine 5 winners, then announce them by November 12.</li>
</ul>
<h2 id="what-skill-level-datacamp-competitions-are-intended-for-">What skill level DataCamp competitions are intended for?</h2>
<p>DataCamp competitions are open-ended challenges intended to test your analytical, storytelling, and visualization skills. As a recommendation, we would say complete the skill track relevant to the tool you’ll be using in your analysis. DataCamp competitions are similar in certain respects to Kaggle, although they will have more of a focus on real-world analytics so are more accessible to a wider audience.</p>
<h2 id="what-else-is-coming-up-">What else is coming up?</h2>
<p>If you haven’t already, be sure to follow DataCamp on Facebook, Twitter, LinkedIn, and Instagram, where every week we will announce the launch and topic of new competitions!</p>
<br><center><a href="https://app.datacamp.com/learn/competitions"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1634599264/Group_1127_koksuy.png" /></a></center><br>

<p>Curious? You can learn more about our <a href="https://support.datacamp.com/hc/en-us/articles/4410791195031">data science competitions here</a>, and if you’d like some inspiration you can check out the winning publications of our Workspace competition.</p>
<ul>
<li><a href="https://tinyurl.com/5cfv44k7">First place—Loan Data by Kok Kwang Ng</a></li>
<li><a href="https://tinyurl.com/4sz37tww">Second place—Loan Data by Nikolaos Christoforidis</a></li>
<li><a href="https://tinyurl.com/sazjdemc">Third place—Optimizing Gower Weights in Feature Engineering by Patrick Coulombe</a></li>
<li><a href="https://tinyurl.com/xcfx3aks">Fourth place—Internet News and Consumer Engagement by Renaat de Rudere</a></li>
<li><a href="https://tinyurl.com/cm6nxsbx">Fifth place—Internet News and Consumer Engagement</a></li>
</ul>


https://www.datacamp.com/community/blog/introducing-data-science-competitions

DataCamp’s data science competitions are now live! You’ll be able to put your data science skills into practice for the chance to win prizes, get new additions to your portfolio, and learn new approaches to a single problem from other people who solved it.

DataCamp’s data science competitions are now live! You’ll be able to put your data science skills into practice for the chance to win prizes, get new additions to your portfolio, and more!

Introducing DataCamp Competitions!

In this episode of DataFramed, we speak with Shameek Kundu,  Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

<h2>Introduction to Instrumental Variables</h2><p>This chapter will introduce you to Instrumental Variables (IV) analysis, a tricky but powerful method to find causality through indirect inference</p><h2>Instrumental Variables in Practice</h2><p>This chapter covers more details about Instrumental Variables Analysis, and allows you to practice instrumental variables for yourself</p><h2>Regression Discontinuity Design</h2><p>This chapter introduces you to Regression Discontinuity Design (RDD), which is often considered a more flexible relative of Instrumental Variables</p><h2>The Local Average Treatment Effect of IV</h2><p>This chapter explains the treatment effect we are estimating with the instrumental variables method - LATE, or the Local Average Treatment Effect</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-instrumental-variables-rdd

A look at using Instrumental Variables and Regression Discontinuity to estimate causal effects

Causal Inference with R - Instrumental Variables & RDD

<h2>Regressions 1: Introduction to Regression As Causality</h2><p>This chapter will introduce you to using regression analysis to find causal effects</p><h2>Regressions 2: Using Regression to Estimate Causal Effects</h2><p>This chapter will introduce you to using regression analysis to find causal effects</p><h2>Regressions 3: Introduction to Matching Methods</h2><p>This chapter will introduce you to using matching methods to find causal effects</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-regression

Welcome to the 3rd course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. 

Causal Inference with R - Regression

<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

<p>Plot character or <em>pch</em> is the standard argument to set the character that will be plotted in a number of R functions.</p>
<p>Explanatory text can be added to a plot in several different forms, including axis labels, titles, legends, or a text added to the plot itself. Base graphics functions in R typically create axis labels by default, although these can be overridden through the argument <code>xlab()</code> that allows us to provide our own x-axis label and <code>ylab()</code> that allows us to provide our own y-axis label.</p>
<p>Some base graphics functions also provide default titles, but again, these can be overridden has we see here:</p>
<pre><code class="lang-python">library(MASS)
par(mfrow = c(1, 2))
plot(density(geyser$waiting))
plot(density(geyser$waiting), main = &quot;Estimated density: \n Old Faithful waiting times&quot;)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603481962/pch_2_kukmll.png" /></center>


<p>The plot on the left uses the default title returned by the density function, which tells us that the plot was generated by this function using its default options and also gives the R specification for the variable whose density we are plotting. In the right-hand plot, this default title has been overridden by specifying the optional argument main. Note that by including the return character, backslash n, we are creating a two-line title in this character string.</p>
<h2 id="-text-function"><code>text()</code> Function</h2>
<p>Like the lines and points functions, text is a low-level graphics function that allows us to add explanatory text to the existing plot. To do this, we must specify the values for x and y, the coordinates on the plot where the text should appear, and labels, a character vector that specifies the text to be added.</p>
<pre><code class="lang-python">text(x, y, adj)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_575/v1603481260/pch3_ubupgd.png" /></center>

<p>By default, the text added to the plot is centered at the specified x values, but the optional argument <code>adj</code> can be used to modify the alignment.</p>
<p>It is noted that the <code>adj</code> argument to the <code>text()</code> function determines the horizontal placement of the text, and it can take any value between 0 (left-justified text) and 1 (right-justified text). In fact, this argument can take values outside this range. That is, making this value negative causes the text to start to the right of the specified <code>x</code> position. Similarly, making <code>adj</code> greater than 1 causes the text to end to the left of the <code>x</code> position.</p>
<pre><code class="lang-python">library(MASS)
plot(UScereal$fibre)
text(5, 28, &quot;&lt;-- Outliers [left-justified text at (5, 28)]&quot;, adj = 0)
text(65, 23, &quot;[Right-justified text at (65, 23)]&quot;, adj = 1, col = &quot;red&quot;)
text(5, 28, &quot;[Centered text (default) at (31, 18)]&quot;, col = &quot;blue&quot;)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best,w_575/v1603481260/pch5_decn8p.png" /></center>

<h3 id="fonts-orientations-and-other-text-features">Fonts, Orientations, and Other Text Features</h3>
<p>Below we see some of the ways the appearance of added text can be changed through optional arguments to the text function. By default, text is created horizontally across the page, but this can be changed with the <code>srt</code> argument, which specifies the angle of orientation with respect to the horizontal axis.</p>
<p>Next, the text in red angles upward as a result of specifying <code>srt</code> as the positive value 30 degrees, while the text in green angles downward by specifying <code>srt</code> equals -45.</p>
<p>We can also specify the color of the text with the <code>col</code> argument, and the red text shows that we can change the text size with the <code>cex</code> argument. By specifying <code>cex = 1.2</code>, we have specified the text to be 20% larger than normal.</p>
<p>The other text characteristic that has been modified in this example is the font, set by the <code>font</code> argument. The default value is <code>font = 1</code>, which specifies normal text, while <code>font = 2</code> specifies boldface, <code>font = 3</code> specifies italics, and <code>font = 4</code> specifies both boldface and italics.</p>
<pre><code class="lang-python">library(MASS)
plot(Boston$rad)

# &quot;Inner city&quot; with adjusted colour and rotation
text(350, 24, adj = 1, &quot;Inner city? --&gt;&quot;, srt = 30, font = 2, cex = 1.2, col = &quot;red&quot;)

# &quot;Suburbs&quot; with adjusted colour and rotation
text(100, 15, &quot;Suburbs? --&gt;&quot;, srt = -45, font = 3, col = &quot;green&quot;)

title(&quot;Text with varying orientations, fonts, sizes &amp; colors&quot;)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603481012/pch_1_qfa8ff.png" /></center>

<h2 id="adjusting-text-position-size-and-font">Adjusting Text Position, Size, and Font</h2>
<p>In the following example, you will:</p>
<ul>
<li>Create a plot of <code>MPG.city</code> vs. <code>Horsepower</code> from the <code>Cars93</code> data frame, with data represented as open circles (default <code>pch</code>).</li>
<li>Construct the variable <code>index3</code> using the <code>which()</code> function that identifies the row numbers containing all 3-cylinder cars.</li>
<li>Use the <code>points()</code> function to overlay solid circles, <code>pch = 16</code>, on top of all points in the plot that represent 3-cylinder cars.</li>
<li>Use the <code>text()</code> function with the <code>Make</code> variable as before to add labels to the right of the 3-cylinder cars, but now use <code>adj = 0.2</code> to move the labels further to the right, use the <code>cex</code> argument to increase the label size by 20 percent, and use the <code>font</code> argument to make the labels bold italic.</li>
</ul>
<pre><code class="lang-python"># Plot MPG.city vs. Horsepower as open circles
plot(Cars93$Horsepower, Cars93$MPG.city)

# Create index3, pointing to 3-cylinder cars
index3 &lt;- which(Cars93$Cylinders == 3)

# Highlight 3-cylinder cars as solid circles
points(Cars93$Horsepower[index3],
       Cars93$MPG.city[index3],
       pch = 16)

# Add car names, offset from points, with larger bold text
text(Cars93$Horsepower[index3],
     Cars93$MPG.city[index3],
     Cars93$Make[index3],
     adj = -0.2, cex = 1.2, font = 4)
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603481260/pch2_uumynj.png" /></center>

<p><a href="https://campus.datacamp.com/courses/data-visualization-in-r/adding-details-to-plots?ex=11">Try it for yourself</a>.</p>
<p>To learn more about adding text to plots, please see this video from our course <a href="https://campus.datacamp.com/courses/data-visualization-in-r/adding-details-to-plots?ex=9">Data Visualization in R</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/VY6X27P2uAQ" 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-data-visualization-with-ggplot2">Data Visualization in R</a> course by Ronald Pearson.</p>


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