http://datacamp-community-prod.s3.amazonaws.com/72e88aa1-b4f2-4658-9d86-15becf8263df

A cheat sheet that covers several ways of getting data into Python: from flat files such as .txts and .csv to files native to other software, such as Excel, SAS, or Matlab, and relational databases such as SQLite & PostgreSQL.

Importing Data: Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Cheat+Sheets/Importing_Data_Python_Cheat_Sheet.pdf

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

<p>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!</p>

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/blog/smart-cities-with-data-science

https://www.datacamp.com/community/podcast/critical-thinking-in-data-science

<p>Debbie Berebichez, Chief Data Scientist at Metis, speaks with Hugo about what type of critical thinking skills are necessary in data science.</p>

Debbie Berebichez speaks with Hugo about the importance of critical thinking in data science.

Critical Thinking in Data Science

https://www.datacamp.com/community/blog/critical-thinking-in-data-science

https://www.datacamp.com/community/news/data-science-at-atandt-labs-research-with-noemi-derzsy-w1vwosxhaq

Noemi Derzsy, a Senior Inventive Scientist at AT&T Labs Research within the Data Science and AI Research organization, discusses her work there and much more!

Data Science at AT&T Labs Research (with Noemi Derzsy)

https://www.datacamp.com/community/news/video-intro-to-python-for-spreadsheet-users-6fmsw8na1xp

In this video tutorial, you'll watch the end-to-end automation of a routine spreadsheet task being completed using Python.

Video: Intro to Python for Spreadsheet Users

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

<p>Welcome to the golden age of data. Today, companies have the privilege of collecting and storing ever-increasing amounts of data in all shapes and sizes. That’s great news, but how good are companies at extracting value from data? Not excellent, according to a <a target="_blank" href="https://www.accenture.com/_acnmedia/pdf-108/accenture-closing-data-value-gap-fixed.pdf">Forrester report</a> that cites that 60%to 73% of all data within an enterprise remains unused for analytics in 2019.</p>
<p>Why is that so? One of the reasons companies are stopping short of becoming fully data-driven, according to <a target="_blank" href="https://www.accenture.com/_acnmedia/PDF-115/Accenture-Human-Impact-Data-Literacy-Latest.pdf">Accenture</a>, is the lack of data skills that stifles workplace productivity. Surprisingly, in a survey of 9,000 companies, Accenture found that three-quarters of the employees feel overwhelmed or unhappy when working with data. Some employees report feeling as such because they are not ‘keeping up’ with the rapid changes in technological transformations. In fact, 59% of employees report feeling burnt out when using business intelligence and data analytics tools. Such unhappiness can be averted if employees are equipped with the skills required to deal with data, or, more precisely, data literacy.</p>
<p><a target="_blank" href="https://www.datacamp.com/tracks/data-literacy-fundamentals">Data literacy</a> can be defined as the ability to read, write, analyze, and communicate with data. Much like how one does not need to be Shakespeare to be considered literate, one does not need to become a data scientist to be data literate.  Data literacy is not about learning every single programming language or the most advanced data science skills. Instead, it is about understanding and making decisions with data.</p>
<p>Achieving data literacy is an essential step in a company’s roadmap to become data fluent. More concretely, it helps employees make <a target="_blank" href="https://www.datacamp.com/courses/data-driven-decision-making">data-driven decisions</a>, interact with data critically, form effective <a target="_blank" href="https://www.datacamp.com/resources/webinars/how-data-governance-enables-scalable-data-science">data governance</a>, and make ethical data decisions.</p>
<h2 id="data-literacy-is-the-keystone-to-making-data-driven-decisions">Data literacy is the keystone to making data-driven decisions</h2>
<p><a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/advanced-analytics-nine-insights-from-the-c-suite">McKinsey</a> found that employees are more likely to buy into a change when they understand it. Employees who understand both data science and its business application can spearhead the end-to-end development of use cases, and in turn influence others to make data-driven decisions.</p>
<p>For instance, the <a target="_blank" href="https://www.wsj.com/articles/the-houston-astros-a-case-study-of-the-transformative-power-of-analytics-1532102127">Houston Astros</a> used analytics to build its competitive advantage in a zero-sum industry, paving its way to victory in the Major League Baseline in 2017. Their secret? Executives who champion data literacy and make data-driven decisions. They employed data literate coaches (who can program in SQL) who could translate data insights to a language understood by players. Beyond that, the Astros built a strong data culture that permeates every facet of the team’s game strategy running the gamut from the process of player selection before the game to that of player position during the game. Such data-driven decisions would not have been possible without a data literate team.</p>
<h2 id="data-literacy-is-a-prerequisite-for-employees-to-interact-with-data-insights-meaningfully-and-critically-">Data literacy is a prerequisite for employees to interact with data insights meaningfully and critically.</h2>
<p>Data-literate employees are capable of creating and analyzing data visualizations that are integrated into the company&#39;s decision-making process. An example of such a company is PepsiCo, which uses <a target="_blank" href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau</a> and Hadoop to <a target="_blank" href="https://www.tableau.com/learn/webinars/how-pepsicos-big-data-strategy-disrupting-cpg-retail-analytics">visualize large volumes of data</a> that drive million-dollar sales decisions.</p>
<p>Moreover, being data literate is also a prerequisite to one’s ability to critically evaluate the validity of the data used to create visualizations.</p>
<blockquote>
<p>These employees who interrogate the reliability, correctness, and consistency of the data sources can potentially catch costly mistakes, thereby improving the company’s confidence and effectiveness in making data-driven decisions.</p>
</blockquote>
<h2 id="data-literacy-forms-the-foundation-of-effective-data-governance">Data literacy forms the foundation of effective data governance</h2>
<p><a target="_blank" href="https://www.datacamp.com/webinars/how-data-governance-enables-scalable-data-science">Data governance</a> is the set of policies, processes and organizational structure that define how the organization’s data is managed. It ensures that data is readily available, relevant to create value for the company, of high quality, and compliant with existing regulations. To build strong data governance, leadership must have basic data literacy to first understand the data context and needs of the organization before setting data policies.</p>
<p><a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/designing-data-governance-that-delivers-value">McKinsey</a> suggested that the foundation for effective data governance involves a data organizational structure with three components:</p>
<ol><li>the data management office (DMO) in charge of defining policies and standards</li>
<li>domain leaders who set and execute domain-specific strategies</li>
<li>the data council that connects the domain leaders and DMO</li></ol>
<p>Conceivably, the leaders in each of these components should understand data processes sufficiently to set up unambiguous and fair data governance roadmaps. For instance, data-literate domain leaders who understand the value of the data can effectively work with the DMO to design and deploy an appropriate enterprise data lake for a domain.</p>
<h2 id="data-literacy-helps-companies-make-ethical-ai-decisions">Data literacy helps companies make ethical AI decisions</h2>
<p>Companies that deploy AI systems for real-world use cases face the risk of AI systems, such as <a target="_blank" href="https://arxiv.org/pdf/1606.06565.pdf">AI accidents</a>, breaches of data privacy, and <a target="_blank" href="https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai">AI bias</a>. One way to mitigate such a risk is to engage technical and non-technical stakeholders to critically examine the processes and outputs of the data science system. Business stakeholders who are data literate play a crucial role in evaluating the risks of AI systems and ensuring that existing AI systems are fair and ethical.</p>
<p>For instance, as Facebook has come under intense scrutiny in recent years over its recommendation systems, the <a target="_blank" href="https://ai.facebook.com/blog/what-ai-fairness-in-practice-looks-like-at-facebook/">Facebook Responsible AI Initiative</a> was founded. It consists of a multidisciplinary team of not just data scientists, but also philosophers and social scientists. This team evaluates existing systems and determines ethical standards openly. These qualitative standards are then translated and implemented by data scientists as quantitative metrics. Such debates on AI risks are productive only when all the stakeholders are well-informed and have a minimum level of data literacy to weigh the cost and benefits of an AI system. Similarly, organizations looking to operationalize and govern AI need a multidisciplinary team of business and data experts that have a common data language.</p>
<h2 id="building-organizational-data-literacy">Building Organizational Data Literacy</h2>
<p>Clearly, inculcating a strong data literacy in all employees brings about a myriad of benefits. Data literacy programs take many forms, including existing skills initiatives, e-learning courses, or specialized classroom training. Unsurprisingly, designing in-house data literacy programmes is a non-trivial and long-drawn process that requires meticulous planning. This is whyDataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data literacy, and data science to data engineering and machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.</p>
<br><p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></p>

https://www.datacamp.com/community/blog/why-data-literacy-is-important-for-your-team

In this blog post, learn why data literacy is important and why some companies aren't extracting value from their data like they should be.

Why Data Literacy Is Important for Your Team

<p>What is a datathon? Similar to hackathons, a datathon is an event where participants gather to solve practical problems through the application of data science tools and techniques, by working together in teams to generate insights and potential solutions.</p>
<h2 id="the-benefits-of-a-datathon">The benefits of a datathon</h2>
<p>With the world transforming into a digital one, the idea of a datathon is fast becoming mainstream. The benefits of a datathon, whether organized by a company or by a volunteer group, are extensive.</p>
<p><span style="text-decoration:underline">Encourages teamwork:</span> These events create a special sense of purpose to achieve something greater collectively, drawing out creativity and new perspectives that often emerge when working intensely in a time-bound setting.</p>
<p><span style="text-decoration:underline">Fosters collaboration:</span> In a traditional hierarchical structure, decision-making is based on positional authority. In a horizontal setting, decision-making is based on competence and value contribution, attributes commonly found in high-performing teams. This structure also promotes transparency and candor, requiring team members to exhibit a higher degree of emotional intelligence. More time is devoted to issue identification and less on worrying about being “kept in the loop”. Teams tend to be more autonomous, depending on one another to carry through on commitments. A fluid environment is created where ideas are shared openly and the steps taken to test an idea happen quickly. Some ideas “fail quickly” to produce valuable learnings, while other ideas proceed to the next phase of viability.</p>
<p><span style="text-decoration:underline">Promotes innovation:</span> A traditional corporate culture, particularly that found in the oil and gas sector, is a sensibility that promotes safety and reliability, critical to be successful in this industry. However, such a mindset inadvertently stifles innovation. It is a mindset that fears “what could go wrong” rather than fearing if something good does not occur. In a datathon, the mindset is an entrepreneurial one.</p>
<h2 id="how-is-a-datathon-organized-">How is a datathon organized?</h2>
<p>Firstly, you should make sure these key components are in place—a well-structured dataset that can drive insights on a business problem that needs solving, a positive environment for participants to learn new data skills, a chance for participants to make professional connections, and for everyone to have fun!  Secondly, datathons can be run in-person (in adherence to local health authority guidelines during a pandemic, of course) or completely virtual. A participant has the best experience when the event is inclusive and welcoming to both beginners and seasoned data scientists alike. Anchor the event around informative and engaging meetings, which could include a kick-off event to let participants know what to expect, an opening ceremony to start the competition, and a closing ceremony to celebrate its conclusion.  Events in between can drive up engagement from a learning and networking perspective, such as through learning workshops and social networking gatherings. Communications throughout the event are kept high through postings on the event’s social media channels, while near-real-time communications is achieved through using IRC-style platforms like Slack, Discord, or Teams.</p>
<p>Sponsorship not only provides much-needed financial support, but it also adds credibility to your event and sponsors can also offer prizes to motivate your participants.</p>
<p>It is human nature to seek out fun activities. Gamification is the application of game playing (e.g. point scoring, competition with others, rules of play) to encourage engagement and motivation. It can also lead to a different, even unexpected, way of solving a business problem.  To be inclusive, it should not be the primary focus but is a delightful feature of a datathon.</p>
<h2 id="how-long-is-a-datathon-">How long is a datathon?</h2>
<p>Datathons can be held over the course of a single day, over multiple days or a weekend, or even span across a month. It’s important to choose the right time period to sustain engagement throughout your event. In-person events are wonderful for building lasting network connections, though virtual events can attract a broader, even global, audience. Longer events also require more effort from the planning team, though the effort to plan a successful single-day datathon is similar to the effort needed to plan a multi-day one.</p>
<h2 id="want-to-see-a-datathon-in-action-">Want to see a datathon in action?</h2>
<p>Come join the <a target="_blank" href="https://www.speuntapped.com/">GTX 2021 – Geothermal datathon</a> happening between April 20 and June 25, 2021. Hosted in Calgary, Canada, this virtual event will draw people from around the world.  Organized by the Society of Petroleum Engineers (Calgary and US Gulf Coast chapters) and Untapped Energy, this datathon is driven by the desire to use real data to solve a real problem, elevate data literacy, and make new professional connections—all while having fun! The problem: aging oil and gas wells represent an option for the transition to a low-carbon energy world and a future for drilling engineers. Geothermal is a suitable approach and will be the focus of this datathon. All participants enjoy:</p>
<ul>
<li>Use of a great energy industry dataset.</li>
<li>Access to a series of data science bootcamps and workshops.</li>
<li>Access to an incredible library of self-paced online data science courses through DataCamp.</li>
<li>Entry into the datathon competition to test and prove data skills.</li>
<li>Opportunities to meet and work with fellow data enthusiasts to solve real-world problems.</li>
<li>The chance to win a series of great prizes.</li>
</ul>
<p>More details about this event: <a target="_blank" href="https://www.speuntapped.com/">speuntapped.com</a></p>
<h4 id="datacamp-learning-platform-and-learning-competition">DataCamp – Learning Platform and Learning Competition</h4>
<p>All registered participants receive access to the entire DataCamp platform for the duration of the datathon, which includes all of the platform’s 350+ courses, 90 projects, and 10 skill assessments.  Additionally, DataCamp is sponsoring a learning competition: the participant who completes the greatest number of courses throughout the datathon will receive a one-year Premium subscription to DataCamp.</p>
<p>I hope this article has helped create excitement about running your first datathon. Please feel free to connect with me on LinkedIn and keep me updated on your datathon. Good luck!</p>


https://www.datacamp.com/community/blog/how-to-plan-a-successful-datathon

Learn everything you need to know about datathons, from their benefits, how to organize one, and more.

How To Plan a Successful Datathon

<h2>Importing data and star review modification</h2><p>Weighting star reviews based on users who review more restaurants</p><h2>Generating authentic star reviews</h2><p>Modifying restaurant reviews to only the native Indian users</p>

https://www.datacamp.com/community/open-courses/r-yelp-and-the-search-for-good-indian-food

Are all Yelp restaurant reviews created equal? You will learn the techniques to “cut through the noise” and deliver useful metrics for finding a great place to eat on Friday night!

R, Yelp and the Search for Good Indian Food

<h2>Analyzing the 2013 American Community Survey</h2><p>In this first chapter you will use data from the <a href="https://www.kaggle.com/census/2013-american-community-survey" target="_blank" >2013 American Community Survey</a> to figure out whether it makes sense to pursue a PhD or not. The end result of this chapter will be <a href="https://www.kaggle.com/martijn/d/census/2013-american-community-survey/data-exploration-with-kaggle" target="_blank">your own Kaggle script</a> that you can add to your Kaggle account.</p><h2>Pigeons, Chopsticks, Spanish Silver and Marijuana</h2><p>Analyze pigeon racing data, discover the optimal size for your chopsticks, plot Spanish silver production in the 18th century and map the average price of marijuana in the different states of the US.</p>

https://www.datacamp.com/community/open-courses/data-exploration-with-kaggle-scripts

In this course, you'll begin learning the art and science of data exploration. You'll also become familiar with some of the most widely used visualization packages in data science.

Data Exploration With Kaggle Scripts

https://www.datacamp.com/community/tutorials/streaming-platform-analysis

In this Python tutorial, explore movie data of popular streaming platforms and build a recommendation system.

(Tutorial) Recommendation System for Streaming Platforms

Recommendation System for Streaming Platforms

https://www.datacamp.com/community/tutorials/python-list-comprehension

Learn how to effectively use list comprehension in Python to create lists, to replace (nested) for loops and the map(), filter() and reduce() functions, ...!

community

datacamp

data analysis

News (56)

Data Science at AT&T Labs Research (with Noemi Derzsy)

Video: Intro to Python for Spreadsheet Users

Data Science at AT&T Labs Research (with Noemi Derzsy)

Tutorials (45)

Recommendation System for Streaming Platforms

Python List Comprehension

Cheat Sheets (1)

Importing Data: Python Cheat Sheet

Open Courses (2)

R, Yelp and the Search for Good Indian Food

Data Exploration With Kaggle Scripts

Podcast (9)

Creating Smart Cities with Data Science (with Amen Ra Mashariki)

Blog Posts (55)

Why Data Literacy Is Important for Your Team

Tech Thoughts (0)