

<p>The&nbsp;<a href="http://pandas.pydata.org">Pandas</a>&nbsp;library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to <a href="http://matplotlib.org">matplotlib</a> for data visualization and <a href="http://www.numpy.org">NumPy</a>, the fundamental library for scientific computing in Python on which Pandas was built.&nbsp;</p>

<p>The&nbsp;fast, flexible, and expressive Pandas data structures are designed to&nbsp;make real-world data analysis significantly&nbsp;easier, but this might not be immediately the case for those who are just getting started with it. Exactly because there is so much functionality built into this package that the options are overwhelming.</p>

<p>That&#39;s where this Pandas cheat sheet might come in handy.&nbsp;</p>

<p>It&#39;s a quick guide through the basics of Pandas that you will need to get started on wrangling your data with Python.&nbsp;</p>

<p>As such, you can use it as a handy reference if you&nbsp;are just beginning their data science journey with Pandas or, for those of you who already haven&#39;t started yet, you can just use it as a guide to make it easier to learn about and use it.&nbsp;</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/f04456d7-8e61-482f-9cc9-da6f7f25fc9b" target="_blank"><img alt="Pandas Cheat Sheet for Data Science in Python" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1621255989/Pandas_Cheat_Sheet_for_Data_Science_in_Python_hqirhp.png" /></a>

<p>The Pandas cheat sheet will guide you through the basics of the Pandas library, going from the data structures&nbsp;to I/O, selection, dropping indices or columns, sorting and ranking, retrieving basic information of the data structures you&#39;re working with to applying functions and data alignment.</p>

<p>In short, everything that you need to kickstart your data science learning with Python!</p>

<p>Do you want to learn more? Start the <a href="https://www.datacamp.com/courses/intermediate-python-for-data-science/">Intermediate Python For Data Science</a> course for free now or try out our <a href="https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python/">Pandas DataFrame tutorial</a>!&nbsp;</p>

<p>Also, don&#39;t miss out on our <a href="https://www.datacamp.com/community/blog/pandas-cheat-sheet-python">Pandas Data Wrangling cheat sheet</a> or our other <a href="https://www.datacamp.com/community/data-science-cheatsheets">data science cheat sheets</a>.&nbsp;</p>

<p><strong>(Click above to download a printable version or read the online version below.)</strong></p>
<h2>Python For Data Science Cheat Sheet: Pandas Basics</h2>
<p>Use the following import convention:</p>
<pre><code>&gt;&gt;&gt; import pandas as pd</code></pre>
<h3>Pandas Data Structures</h3>
<div>
<style scoped>
  table, th, td {
   border: 1px solid black;
    color: black;
    font-size: 12px;
max-width:25%;
white-space:nowrap;
}

h3 {font-weight:normal;
}
h4 {
	font-weight: lighter;
}
  </style>
<h3>Series</h3>
<p>A one-dimensional labeled array capable of holding any data type </p>
<pre><code>&gt;&gt;&gt; s = pd.Series([3, -5, 7, 4],  index=['a',  'b',  'c',  'd'])</code></pre>

<table class="fixed">
<tr>
  <td>A</td>
  <td>3</td>
  <tr>
</tr>
<td>B</td>
<td>5</td>
<tr>
</tr>
<td>C</td>
<td>7</td>
<tr>
</tr>
<td>D</td>
<td>4</td>
<tr>
</tr>
</table>

<h3>DataFrame</h3>
<p>A two-dimensional labeled data structure with columns of potentially different types</p>



<pre><code>&gt;&gt;&gt; data = {'Country': ['Belgium',  'India',  'Brazil'],

'Capital': ['Brussels',  'New Delhi',  'Brasilia'],

'Population': [11190846, 1303171035, 207847528]}</code>
<code>&gt;&gt;&gt; df = pd.DataFrame(data,columns=['Country',  'Capital',  'Population'])</code></pre>
<br>
<table>
<tr>
  <th></th>
  <th>Country</th>
<th>Capital</th>
<th>Population</th>
</tr>
<tr>
    <td>1</td>

  <td>Belgium</td>
<td>Brussels</td>
<td>11190846</td>
</tr>
<tr>
  <td>2</td>
<td>India</td>
<td>New Delhi</td>
<td>1303171035</td>
</tr>
<tr>
  <td>3</td>
<td>Brazil</td>
<td>Brasilia</td>
<td>207847528</td>
</tr>
</table>


<p>Please note that the first column <strong>1</strong>,<strong>2</strong>,<strong>3</strong> is the index and <strong>Country</strong>,<strong>Capital</strong>,<strong>Population</strong> are the Columns.
<h3>Asking For Help</h3>
<pre><code>&gt;&gt;&gt; help(pd.Series.loc)</code></pre>


<h2>I/O</h2>
<h3>Read and Write to CSV</h3>
<pre><code>&gt;&gt;&gt; pd.read_csv('file.csv', header=None, nrows=5)
&gt;&gt;&gt; df.to_csv('myDataFrame.csv')</code></pre>


<h3>Read multiple sheets from the same file</h3>
<pre><code>&gt;&gt;&gt; xlsx = pd.ExcelFile('file.xls')
&gt;&gt;&gt; df = pd.read_excel(xlsx,  'Sheet1')</code></pre>


<h3>Read and Write to Excel</h3>
<pre><code>&gt;&gt;&gt; pd.read_excel('file.xlsx')
&gt;&gt;&gt; df.to_excel('dir/myDataFrame.xlsx',  sheet_name='Sheet1')</code></pre>


<h3>Read and Write to SQL Query or Database Table</h3>
<p>(read_sql()is a convenience wrapper around read_sql_table() and read_sql_query())</p>
<pre><code>&gt;&gt;&gt; from sqlalchemy import create_engine
&gt;&gt;&gt; engine = create_engine('sqlite:///:memory:')
&gt;&gt;&gt; pd.read_sql(SELECT * FROM my_table;, engine)
&gt;&gt;&gt; pd.read_sql_table('my_table', engine)
&gt;&gt;&gt; pd.read_sql_query(SELECT * FROM my_table;', engine)</code></pre>



<pre><code>&gt;&gt;&gt; df.to_sql('myDf', engine)</code></pre>


<h2>Selection</h2>
<h3>Getting </h3>
<p>Get one element</p>
<pre><code>&gt;&gt;&gt; s['b']
-5</code></pre>

<p>Get subset of a DataFrame</p>
<pre><code>&gt;&gt;&gt; df[1:]
Country     Capital   Population
1  India    New Delhi 1303171035
2  Brazil   Brasilia  207847528</code></pre>

<h3>Selecting', Boolean Indexing and Setting</h3>
<h3>By Position</h3>
<p>Select single value by row and and column</p>
<pre><code>&gt;&gt;&gt; df.iloc([0], [0])
'Belgium'
&gt;&gt;&gt; df.iat([0], [0])
'Belgium'</code></pre>


<h3>   By Label</h3>
<p>Select single value by row and column labels</p>
<pre><code>&gt;&gt;&gt; df.loc([0],  ['Country'])
'Belgium'
&gt;&gt;&gt; df.at([0],  ['Country'])
'Belgium'</code></pre>

<h3>By Label/Position</h3>
<p>Select single row of subset of rows</p>
<pre><code>&gt;&gt;&gt; df.ix[2]
Country      Brazil
Capital    Brasilia
Population  207847528</code></pre>

<p>Select a single column of subset of columns</p>
<pre><code>&gt;&gt;&gt; df.ix[:, 'Capital']
0     Brussels
1    New Delhi
2     Brasilia</code></pre>

<p>Select rows and columns</p>
<pre><code>&gt;&gt;&gt; df.ix[1, 'Capital']
'New Delhi'</code></pre>


<h3>Boolean Indexing</h3>
<p>Series s where value is not &gt;1</p>
<pre><code>&gt;&gt;&gt; s[~(s &gt; 1)]</code></pre>
<p>
s where value is &lt;-1 or &gt;2 </p>
<pre><code>&gt;&gt;&gt; s[(s &lt; -1) | (s &gt; 2)]</code></pre>
<p>Use filter to adjust DataFrame</p>
<pre><code>&gt;&gt;&gt; df[df['Population']&gt;1200000000]</code></pre>
<h3>
  Setting</h3>
<p>Set index a of Series s to 6</p>
<pre><code>&gt;&gt;&gt; s['a'] = 6</code></pre>

<h2>Dropping</h2>
<p>Drop values from rows (axis=0)</p>
<pre><code>&gt;&gt;&gt; s.drop(['a',  'c'])</code></pre>
<p>
Drop values from columns(axis=1)</p>
<pre><code>&gt;&gt;&gt; df.drop('Country', axis=1) </code></pre>

<h3>Sort and Rank</h3>
<p>Sort by labels along an axis</p>
<pre><code>&gt;&gt;&gt; df.sort_index()</code></pre>
<p>Sort by the values along an axis</p>
<pre><code>&gt;&gt;&gt; df.sort_values(by='Country') </code></pre>

<p>Assign ranks to entries</p>
<pre><code>&gt;&gt;&gt; df.rank()</code></pre>



<h2>Retrieving Series/DataFrame Information</h2>
<h3>Basic Information</h3>
<p>(rows, columns)</p>
<pre><code>&gt;&gt;&gt; df.shape</code></pre>

<p>
Describe index </p>
<pre><code>&gt;&gt;&gt; df.index</code></pre>

<p>
Describe DataFrame columns</p>
<pre><code>&gt;&gt;&gt; df.columns</code></pre>
<p>
Info on DataFrame</p>
<pre><code>&gt;&gt;&gt; df.info()</code></pre>
<p>
Number of non-NA values</p>
<pre><code>&gt;&gt;&gt; df.count()</code></pre>


<h3>Summary</h3>
<p>Sum of values</p>
<pre><code>&gt;&gt;&gt; df.sum()</code></pre>
<p>
Cumulative sum of values </p>
<pre><code>&gt;&gt;&gt; df.cumsum()</code></pre>
<p>
Minimum/maximum values</p>
<pre><code>&gt;&gt;&gt; df.min()/df.max()</code></pre>


<p>Minimum/Maximum index value</p>
<pre><code>&gt;&gt;&gt; df.idxmin()/df.idxmax() </code></pre>


<p>Summary statistics</p>
<pre><code>&gt;&gt;&gt; df.describe()</code></pre>
<p>
Mean of values</p>
<pre><code>&gt;&gt;&gt; df.mean()</code></pre>
<p>
Median of values</p>
<pre><code>&gt;&gt;&gt; df.median()</code></pre>


<h2>Applying Functions</h2>
<pre><code>&gt;&gt;&gt; f = lambda x: x*2</code></pre>

<p>Apply function</p>
<pre><code>&gt;&gt;&gt; df.apply(f)</code></pre>

<p>
Apply function element-wise</p>
<pre><code>&gt;&gt;&gt; df.applymap(f)</code></pre>


<h3>Internal Data Alignment</h3>
<p>NA values are introduced in the indices that don't overlap:</p>
<pre><code>&gt;&gt;&gt; s3 = pd.Series([7, -2, 3],  index=['a',  'c',  'd'])
&gt;&gt;&gt; s + s3
a     10.0
b     NaN
c     5.0
d     7.0</code></pre>



<h3>Arithmetic Operations with Fill Methods</h3>
<p>You can also do the internal data alignment yourself with the help of the fill methods:</p>
<pre><code>&gt;&gt;&gt; s.add(s3, fill_value=0)
a    10.0
b    -5.0
c    5.0
d    7.0
&gt;&gt;&gt; s.sub(s3, fill_value=2)
&gt;&gt;&gt; s.div(s3, fill_value=4)
&gt;&gt;&gt; s.mul(s3, fill_value=3)</code></pre>
</div>


https://www.datacamp.com/community/blog/python-pandas-cheat-sheet

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

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

Pandas Cheat Sheet for Data Science in Python

https://www.datacamp.com/community/blog/from-predictions-to-decisions

Dan Becker deep dives into the intersection of decision sciences and machine learning, how data teams can go from experimentation and deployment to providing value at scale for organizations, and more!

From Predictions to Decisions

<center><a href="https://www.facebook.com/events/2850209725308354/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620856375/generic_live_event_banner_yxjjkt.png" /></a></center><br>
<p>Have you ever applied to a job that seemed like the perfect fit but never heard back after the application?  </p>
<p>During this webinar, we will discuss best practices to help your application get noticed in today’s competitive data science job market. We will focus on how you can improve your personal brand and help position yourself for success in your job search.  </p>
<h2 id="agenda-">Agenda</h2>
<ol>
<li>Applicant tracking systems</li>
<li>Resume and portfolio tips</li>
<li>Targeted cover letters</li>
<li>LinkedIn for success: profile, job searches, networking, and more!</li>
<li>Q&amp;A</li>
</ol>
<h2 id="speaker">Speaker</h2>
<p>Jen Bricker leads the Career Services team mentoring learners during their job search. Previously, she led the Instructor Success team hiring hundreds of data scientists, working with them to build DataCamp content.  Prior to DataCamp, she worked in non-profit management as well as data analytics adult education. Her career has always centered around her two main passions - people and data!</p>
<h2 id="where-">When</h2>
<p>June 10th at 11:00 AM ET</p>
<h2 id="where-">Where</h2>
<center><a href="https://www.facebook.com/events/2850209725308354/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620855480/Blog_Event_Banner_qbb21f.png" /></a></center>
<ul>
<li>Live on our <a href="https://www.facebook.com/events/2850209725308354/">Facebook page</a>. Follow us so you get notified!</li>
<li><a href="https://www.linkedin.com/events/linkedinlive-howtostandoutfromt6798359236817518592/">LinkedIn Live</a></li>
<li><a href="https://www.youtube.com/channel/UC79Gv3mYp6zKiSwYemEik9A">YouTube Live</a></li>
</ul>


https://www.datacamp.com/community/blog/how-to-stand-out-from-the-crowd

This webinar is well suited for individuals who want to improve their personal brand while looking for a job in data science.

Live Event: How To Stand Out From the Crowd

<p><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612888906/insights_qhc4kg.png" /></a></p>
<h3 id="-building-a-learning-culture-that-drives-the-business-forward-https-www-mckinsey-com-business-functions-mckinsey-accelerate-our-insights-building-a-learning-culture-that-drives-business-forward-mckinsey"><a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-accelerate/our-insights/building-a-learning-culture-that-drives-business-forward">Building a learning culture that drives the business forward</a> | McKinsey</h3>
<p>In this interview, McKinsey’s Chief Learning Officer Matthew Smith, shares his thoughts around delivering learning programs for 30,000+ employees, his best practices on creating a sustainable learning culture, and how people can become better learners.</p>
<h3 id="-ai-adoption-in-the-enterprise-2021-https-www-oreilly-com-radar-ai-adoption-in-the-enterprise-2021-o-reilly"><a target="_blank" href="https://www.oreilly.com/radar/ai-adoption-in-the-enterprise-2021/">AI Adoption in the Enterprise 2021</a> | O’Reilly</h3>
<p>This O&#39;Reilly report covers the state of AI in the enterprise. While the use and deployment of AI in the enterprise has never been more prevalent, organizations still suffer from lack of AI talent, data quality issues, a lack of data culture, and the absence of tooling to scale and monitor AI systems in production .</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612888907/Editor_s_Picks_nj17hh.png" /></a></p>
<h3 id="-this-has-just-become-a-big-week-for-ai-regulation-https-www-technologyreview-com-2021-04-21-1023254-ftc-eu-ai-regulation-bias-algorithms-civil-rights-mit-technology-review"><a target="_blank" href="https://www.technologyreview.com/2021/04/21/1023254/ftc-eu-ai-regulation-bias-algorithms-civil-rights/">This has just become a big week for AI regulation</a> | MIT Technology Review</h3>
<p>The regulatory space for AI is heating up. Over the past week, the European Union has released its plans for wide-ranging regulations on AI systems, and the Federal Trade Commission released plans to take action on AI systems that exhibit bias and discrimination. Check out this article for a detailed view on both these plans.</p>
<h3 id="-five-types-of-thinking-for-a-high-performing-data-scientist-https-towardsdatascience-com-five-types-of-thinking-for-a-high-performing-data-scientist-8ab70d70c23b-a399-anand-rao"><a target="_blank" href="https://towardsdatascience.com/five-types-of-thinking-for-a-high-performing-data-scientist-8ab70d70c23b#a399">Five types of thinking for a high performing data scientist</a> | Anand Rao</h3>
<p>In this blog post, Anand Rao, Global Artificial Intelligence Lead at PwC articulates mental models for high-performing data scientists. These mental models are highly useful for data scientists to reason about the world and the problems they’re trying to solve.</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612889346/Inspiration2_wxr25i.png" /></a></p>
<h3 id="-achieving-insights-and-savings-with-cost-data-https-medium-com-airbnb-engineering-achieving-insights-and-savings-with-cost-data-ec9a49fd74bc-airbnb"><a target="_blank" href="https://medium.com/airbnb-engineering/achieving-insights-and-savings-with-cost-data-ec9a49fd74bc">Achieving Insights and Savings with Cost Data</a> | Airbnb</h3>
<p>Any data-driven organization has to scale its cloud infrastructure and make sure it’s doing so efficiently. In this blog post, find out how the team at Airbnb leveraged AWS cost data to reduce spend and achieve AWS cost savings.</p>
<h3 id="-the-right-way-to-measure-roi-on-data-quality-https-www-montecarlodata-com-the-right-way-to-measure-roi-on-data-quality-monte-carlo-data"><a target="_blank" href="https://www.montecarlodata.com/the-right-way-to-measure-roi-on-data-quality/">The Right Way to Measure ROI on Data Quality</a> | Monte Carlo Data</h3>
<p>Data quality issues can have serious consequences on the bottom line of any aspiring data-driven organization. In this article, learn how to calculate the ROI for increasing data quality and to garner executive support for any data governance or quality effort.</p>
<h3 id="-new-ai-tool-calculates-materials-stress-and-strain-based-on-photos-https-news-mit-edu-2021-ai-materials-stress-strain-0422-mit-news"><a target="_blank" href="https://news.mit.edu/2021/ai-materials-stress-strain-0422">New AI tool calculates materials’ stress and strain based on photos</a> | MIT News</h3>
<p>Engineers often spend countless hours solving complex equations to determine the stress and strain on the materials they work with. A new deep-learning-based approach, promises to streamline this process by leveraging image data alone.</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1618166459/Tools_and_Techniques_1_ybwltq.png" /></a></p>
<h3 id="-time-series-forecasting-at-microsoft-https-medium-com-data-science-at-microsoft-time-series-forecasting-part-1-of-3-understanding-the-fundamentals-13b52eda3e5-yasmin-bokobza-siddharth-kumar"><a target="_blank" href="https://medium.com/data-science-at-microsoft/time-series-forecasting-part-1-of-3-understanding-the-fundamentals-13b52eda3e5">Time series forecasting at Microsoft</a> | Yasmin Bokobza &amp; Siddharth Kumar</h3>
<p>This series of articles on the Data Science at Microsoft blog deep dives into the best practices for time series forecasting from choosing the right forecasting method to simplifying the forecasting workflow, and more.</p>
<h3 id="-openai-powered-linux-shell-uses-ai-to-do-what-you-mean-https-riveducha-onfabrica-com-openai-powered-linux-shell-river-s-educational-channel"><a target="_blank" href="https://riveducha.onfabrica.com/openai-powered-linux-shell">OpenAI-powered Linux shell uses AI to Do What You Mean</a> | River’s Educational Channel</h3>
<p>Ever get tired of forgetting bash syntax? This project leverages OpenAI’s GPT-3 to generate bash commands based on a written description. Check out how it performs on commands like “find all files in current directory bigger than 1gb”.</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612889508/From_DataCamp_c6kasv.png" /></a></p>
<h3 id="-webinar-democratizing-data-in-government-agencies-https-www-datacamp-com-resources-webinars-democratizing-data-in-government-agencies-"><a target="_blank" href="https://www.datacamp.com/resources/webinars/democratizing-data-in-government-agencies">Webinar: Democratizing Data in Government Agencies</a></h3>
<p>In this webinar, Senior Data Scientist at the New South Wales Government Alex Scriven outlines what data democratization means in a government context, and how to best achieve it.</p>
<h3 id="-blog-storytelling-for-more-impactful-data-science-https-www-datacamp-com-community-blog-storytelling-for-more-impactful-data-science-"><a target="_blank" href="https://www.datacamp.com/community/blog/storytelling-for-more-impactful-data-science">Blog: Storytelling for More Impactful Data Science</a></h3>
<p>In this blog post, we outline storytelling techniques for data science to gain better organizational alignment around data projects and initiatives.</p>
<h3 id="-podcast-creating-smart-cities-with-data-science-https-www-datacamp-com-community-podcast-smart-cities-with-data-science-"><a target="_blank" href="https://www.datacamp.com/community/podcast/smart-cities-with-data-science">Podcast: Creating Smart Cities with Data Science</a></h3>
<p>We’ve relaunched our DataFramed podcast! In this episode, Amen Ra Mashariki, former Chief Analytics Officer of New York City outlines the state of data science in government agencies, how he helped make the city of New York smarter, the state of data literacy in government, and more.</p>


https://www.datacamp.com/community/blog/digest-may-2021

Read our favorite articles from the last month.

DataCamp Digest May 2021

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620130652/DC_for_Classrooms_logo_u5cbrr.png" /></center><br>
<p>Today is Teachers’ Day in the United States and everyone at DataCamp wants to take this opportunity to give a resounding “Thank you!” to all of the amazing teachers in this country and around the world who have sacrificed so much to educate our children and keep them safe. As of today, US high school teachers are eligible to <a target="_blank" href="https://www.datacamp.com/groups/classrooms">register their own free Professional DataCamp account</a>. US high school teachers can now invite their students into their accounts for free access, just like college/university educators have been doing for years. They can assign 350+ courses, every project, Signal assessment, and more on desktop or mobile.</p>
<h2 id="history-of-datacamp-for-classrooms">History of DataCamp for Classrooms</h2>
<p>In 2017, <a target="_blank" href="https://medium.com/martijn-theuwissen/why-we-decided-to-give-up-10-of-our-revenue-6c9fbe7d4f81">we decided to give up 10% of our revenue</a> by giving free DataCamp access to postsecondary school instructors and professors as well as their students. We wanted to show the world that DataCamp was serious about democratizing data skills around the world. DataCamp believes that data education (like all education) <a target="_blank" href="https://www.datacamp.com/community/blog/datacamp-classroom">is a basic human right</a>. Today, we’re elated to announce that 500,000 unique users have learned invaluable skills through DataCamp for Classrooms to date. Currently, over 100,000 students and teachers in over 3,750 classrooms are enrolled in DataCamp for Classrooms.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620075396/DataCamp_for_Classrooms_-_Data_1x_dnlxmr.png" /></center><br>
<p>So thank you, all 500,000 thousand of you, for making DataCamp for Classrooms the groundbreaking program it is today. Five hundred thousand humans might be hard to visualize, but we tried:</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620129006/DataCamp_for_Classrooms_-_Visual_1x_ztoesl.png" /></center><br>
<h2 id="evolution-datacamp-for-classrooms-for-us-high-schools">Evolution: DataCamp for Classrooms for US High Schools</h2>
<p>Opening up DataCamp for Classrooms to high schools launches today as we recognize that secondary schools everywhere are struggling to become data literate in both the educational and administrative realms. <a target="_blank" href="https://csteachers.org/documents/en-us/e43c7636-849d-460c-96df-fa52b987dcd8/1">Less than half of American high schools have computer science programs</a>, and far less teach data science, specifically. Frustratingly, yet fittingly, there exists very little data on data science, analytics, or engineering education in US high schools. We see no reason why DataCamp can’t change that.</p>
<p>So we have. Every US high school teacher can now make a DataCamp for Classrooms account to get six months of Professional access for themselves and their students. All of the amazing features previously only available to college and university professors and their students are now ready to be deployed in high school classrooms from coast to coast. There’s never a need to purchase or download additional software as all learning happens in-browser. Teachers have access to crucial tools like Assignments, Progress Reporting, Signal skill assessments, while students can world-class data science training online or off, at home or on the go. All teachers need is a valid official school email address and a faculty page on the school’s website.</p>
<p>Teachers can even reapply after their first free semester of access for another one. There’s no limit to the number of free semesters an educator can get with DataCamp for the Classrooms, and the same goes for their students. We hope US high school teachers can also use this to complement their remote and/or hybrid teaching models to make sure their students are still getting the highest quality education. While DataCamp for Classrooms does not accept international secondary school teachers yet, they may still qualify for the same free access by <a target="_blank" href="https://www.datacamp.com/community/blog/datacamp-donates-q1-2021">applying to DataCamp Donates</a>.</p>
<p>Teachers of the world: you all do so much more than you get credit for, especially during this pandemic. If you want to teach (or just learn) data science, DataCamp for Classrooms will save you considerable time and help you deliver a memorable experience to your students. We hope you take advantage of our offer to increase your school’s data literacy for free.</p>


https://www.datacamp.com/community/blog/us-high-schools

On this Teachers’ Day, we salute the brave heroes educating the next generation and celebrate the 500,000 teachers and students who received DataCamp for Classrooms scholarships to date.

High school teachers and their students now have free access to DataCamp for Classrooms

https://www.datacamp.com/community/blog/smart-cities-with-data-science

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 (Transcript)

Creating Smart Cities With Data Science

<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_1_cruuf7.png" /></a></center><br>
<p>In a digital era of rapid technological advances, it is imperative that governments adopt data-driven approaches in order to provide value for citizens and become more efficient. In a <a target="_blank" href="https://www.datacamp.com/resources/webinars/democratizing-data-in-government-agencies">recent webinar</a>, Alex Scriven, Senior Data Scientist from the New South Wales Government in Sydney, Australia, shared his insights around data democratization in a government environment, inspiring use cases where government data was utilized to deliver positive impact, as well as actionable best practices for developing a data-driven culture in government agencies.</p>
<h2>What Is Data Democratization?</h2>
<p>Data democratization can be described as the situation where everyone in the organization (not only data professionals) has <strong>skills</strong>, <strong>confidence</strong>, and <strong>access</strong> to produce data-informed decisions. While this definition is highly generalizable, it has two specific dimensions in the government context:</p>
<ul>
<li>Democratizing data access and skills internally amongst government staff (i.e. within and across government agencies).</li>
<li>Providing transparency for citizens by making government data available for all—which is often referred to as open data.</li>
</ul>
<p>These two dimensions are related because a positive data culture within the government where skills and access are democratized, will motivate and inspire towards the concept of open data for its citizens as a flow-on effect.</p>
<h2>Why is Data Democratization Important in Government Agencies?</h2>
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<p>There are multiple reasons to support the case for data democratization in the government. Let’s have a look at the top five reasons:</p>
<h4 id="1-shortage-of-talent">1.     Shortage of talent</h4>
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<p>It is well known that data talents are in short supply globally, with <a target="_blank" href="https://quanthub.com/data-scientist-shortage-2020/">Quanthub</a> reporting that there are three times more data science job postings than job searches. To make matters worse, this scarcity is felt especially keenly in the government sector. The ‘State of Data Science’ report by <a target="_blank" href="https://www.stitchdata.com/resources/the-state-of-data-science/">Stitch</a> revealed that only 1.2% of data scientists ply their trade in the government sector, a proportion much lower compared to numerous other industries.</p>
<p>This means that there will not be enough data practitioners to support all the units within government agencies. The solution is to work towards data democratization by building data skills amongst the broader non-technical employee base, such that they are able to initiate and undertake data-driven projects with confidence and competence.</p>
<h4 id="2-better-deployment-of-existing-talent">2.     Better deployment of existing talent</h4>
<p>Given the data talent shortage in the government sector, it is also crucial that existing talents are well managed and well deployed. In an ideal setting, data practitioners are using  their expertise to deliver the greatest value possible for agencies and citizens alike. In agencies where data is not democratized, data talent is often pigeonholed into creating dashboards and delivering value in the form of descriptive analytics (<a target="_blank" href="https://www.csc.gov.sg/articles/bring-data-in-the-heart-of-digital-government">Singapore Smart Nation &amp; Digital Government Office</a>).</p>
<p>If agencies focus on democratizing data skills and access, non-technical stakeholders will be empowered to draw their own descriptive analytics, and data practitioners’ focus will shift from descriptive analytics to predictive analytics and higher impact projects.  </p>
<p>Moreover, it is vital that these talents do not fall into the data support trap, where they face burgeoning workloads from all kinds of unfiltered requests. By providing them with the optimal working environment, it is also more likely that the government can retain these highly sought-after talents.</p>
<h4 id="3-deeper-insights">3.     Deeper Insights</h4>
<p>The non-data-savvy government staff are subject matter experts in their respective fields, meaning that there is a wealth of domain expertise waiting to be tapped into. Instead of imparting domain knowledge to data talents, it is wiser to develop the data skills of domain experts such that they can readily merge their expertise with technical abilities. By leveraging the existing domain knowledge, the agency can gather deeper insights by asking and answering business questions through data-driven approaches.</p>
<h4 id="4-overcome-speed-barriers">4.     Overcome Speed Barriers</h4>
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<p>Government agencies are traditionally associated with slow processes stemming from bureaucracy and red tape. When engaging with data practitioners, this is exacerbated by time-consuming workflows involving iterative interactions between data talents and subject matter experts.</p>
<p>With domain experts empowered, they will instead be able to immediately explore the data by themselves, and discover answers to fundamental business questions without the need to involve data practitioners all the time.</p>
<h4 id="5-trust-and-open-data">5.     Trust and open data</h4>
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<p>As discussed earlier, data democratization in a government setting can go beyond empowering staff with data skills and access, but also by opening government data up to citizens. The setup of an open data portal delivers many benefits to both the government and the citizens. For example, the citizens will value the higher level of transparency offered by the government, while the government can gain from the innovation fostered by the public and from inter-agency collaboration.</p>
<h2 id="use-cases-of-data-democratization-in-government-agencies">Use Cases of Data Democratization in Government Agencies</h2>
<p>The use cases of data democratization in government agencies are broad and many. In general, use cases can be categorized under three broad buckets: faster crisis response, improved operational efficiency, and improved citizen engagement and transparency.  </p>
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<h4 id="-1-improved-crisis-response">(1)  Improved Crisis response</h4>
<p>Equipping government agencies with the right data access and tools will allow them to mount rapid and optimal responses to major crises. <a target="_blank" href="https://www.datacamp.com/resources/webinars/democratizing-data-in-government-agencies">In the webinar</a>, Alex dives deep into a slew of use-cases, but a few stand out. For example, the World Bank and donor governments launched one of the largest disaster relief efforts in history for the <a target="_blank" href="https://blogs.worldbank.org/latinamerica/4-years-looking-back-openstreetmap-response-haiti-earthquake">Haiti 2010 earthquake</a>. This was driven by access to high-quality satellite imagery under open mapping schemes that facilitate public distribution and use. The Red Cross, for example, analyzed social media data to identify where storm victims are seeking refuge to optimize disaster response (<a target="_blank" href="https://expectexceptional.economist.com/improving-disaster-response-efforts-through-data.html">The Economist</a>).</p>
<p>The <a target="_blank" href="http://documents1.worldbank.org/curated/en/176511491287380986/pdf/114011-BRI-3-4-2017-11-49-44-WGSBigDataGovernmentFinal.pdf">West Africa 2014 Ebola crisis</a> also involved numerous public data innovations. For instance, Flowminder used cell phone data to predict potential disease transmission routes, while the Ebola Open Data Initiative allowed health officials to develop an open-source model for Ebola intervention simulations.</p>
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<h4 id="-2-improved-operational-efficiency">(2)   Improved operational efficiency</h4>
<p>Like in any organization, leveraging data at scale can provide massive gains in operational efficiency, and this is similarly seen in government settings. For example, with better deployment of data talent, the <a target="_blank" href="https://www.liberalforum.eu/wp-content/uploads/2019/11/PUBLICATION_AI-in-e-governance.pdf">city of Berlin</a> was able to apply machine learning on its data to automatically identify fraudulent health bills of its civil servants, reducing tax payer money waste.</p>
<p>The UK Government was able to save £1.24 billion by leveraging descriptive analytics to analyze technology vendor spend and avoid excessive procurement costs. Moreover, in the US, the city of Kansas was able to utilize data to prioritize housing inspections, leading to an 11-fold decrease in inspection times, and improved outcomes for citizens. (<a target="_blank" href="https://datasmart.ash.harvard.edu/news/article/case-government-investment-analytics">Harvard Ash Center</a>)</p>
<h4 id="-3-improved-citizen-engagement-and-transparency">(3)   Improved citizen engagement and transparency</h4>
<p>There are numerous data-driven solutions to help promote engagement between the government and its citizens. For instance, the <a target="_blank" href="https://www.oecd-ilibrary.org/governance/fostering-innovation-in-the-public-sector_9789264270879-en">Tunisian government</a> made use of social media data as a proxy to gauge citizens’ perceptions of the government. This is better compared to traditional household surveys as it is cheaper and can be done more regularly.</p>
<p>Another example can be found in Mexico, where the online <a target="_blank" href="http://mejoratuescuela.org">Mejora Tu Escuela</a> (improve your school) platform provides citizens with data on school performance — empowering parents to make educated decisions around where to place their children and get involved in improving the school. This is only one of the many open data use-cases covered throughout the webinar.  </p>
<h2 id="best-practices-of-democratizing-data-in-government-agencies">Best practices of democratizing data in government agencies</h2>
<p>Data democratization in government agencies requires a concerted effort in order to make it become a reality. Alex shared several valuable tips on how to get started and keep it going. In summary, it’s about empowering people with confidence, learning, tools, skills, and access to data to galvanize a data culture that drives improved outcomes.</p>
<p>Concretely, galvanizing a data culture in a government setting requires steps across a variety of levers from infrastructure, people, tools, organization, and processes. At the end of the day, the people in the government agencies are the most important piece as they are the most powerful force for groundswell democratization. Data democratization can be accelerated by consistently supporting and building up their skills and confidence, and reaching out to the passionate members to lead the transformation.</p>
<p>If you want to learn more about the steps government agencies can take to democratize data science, make sure to watch the <a target="_blank" href="https://www.datacamp.com/resources/webinars/democratizing-data-in-government-agencies">full recording of the webinar</a>.</p>
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https://www.datacamp.com/community/blog/democratizing-data-in-government-agencies

Government agencies have access to troves of data. In order to fully harness its value, there needs to be a committed and coordinated effort towards data democratization within and across government agencies.

Democratizing Data in Government Agencies

<p>DataCamp Donates is nearing our goal of donating 25,000 subscriptions to people in need. We have been delighted to receive requests from nonprofit organizations around the world that are doing the commendable work of making their communities more data literate. In fact, our number of partners almost doubled in the first quarter of 2021.</p>
<br><center><a target="_blank" href="https://support.datacamp.com/hc/en-us/articles/360051596574-DataCamp-Donates-Frequently-Asked-Questions&amp;sa=D&amp;source=editors&amp;ust=1619544968344000&amp;usg=AOvVaw2K2BwThE-Qi2tsOaW8-DFf"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1619627737/DataCamp_Donates_Update_Q1_2021_lyvjb0.jpg" /></a></center><br>
<p>It has also been extremely heartwarming to see the outpouring of support for DataCamp Donates from the #DataCampCommunity across social media. We are especially grateful for the over 12,000 people who have watched our very special panel discussion <a target="_blank" href="https://www.datacamp.com/community/blog/datacamp-donates-panel">Changing Lives Around the World with DataCamp Donates</a>, and we look forward to hosting more live broadcasts to showcase the inspiring work our partners are doing to educate their communities and help them get jobs in science, technology, engineering, and mathematics (STEM).</p>
<p>In our last update, we projected to hit our goal of 25,000 subscriptions donated by July, but now it looks like we will achieve that goal considerably sooner. If your organization hasn’t applied to become a DataCamp Donates partner yet, what are you waiting for? Apply now before they’re all gone!</p>
<h2 id="become-a-datacamp-donates-partner">Become a DataCamp Donates Partner</h2>
<p>If you work for a nonprofit organization or governmental social welfare institution, follow these three easy steps to become a DataCamp Donates partner.</p>
<ol>
<li>First, send an email to <a href="mailto:donate@datacamp.com?subject=DataCamp Donates application&amp;body=Please include details of your organization’s goals and how you intend to use the donated subscriptions. Thank you, The DataCamp Donates team.">donate@datacamp.com</a> that details your organization’s goals and how you intend to use the donated subscriptions. Our team reads every email.</li>
<li>Next, qualified organizations will be invited to complete an official application form.</li>
<li>The final step is a meeting conducted on Zoom to finalize the terms of the partnership. We then donate the licenses to our new partner organization <strong>within 2-4 weeks</strong>.</li>
</ol>
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<h2 id="datacamp-for-classrooms-is-getting-a-special-update-">DataCamp for Classrooms is getting a special update...</h2>
<p>In Q1 alone, we gave six-month DataCamp scholarships to 60,000 teachers and students in colleges and universities around the world with our DataCamp for Classrooms program, the most of any quarter since its inception. We want to thank our global teaching community by making it even easier for more teachers and students of different backgrounds and skill levels to start learning on DataCamp (you guessed it) for free. Stay tuned to this blog space for news about the next great chapter of DataCamp for Classrooms!</p>


https://www.datacamp.com/community/blog/datacamp-donates-q1-2021

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