https://www.datacamp.com/community/news/the-spread-of-true-and-false-news-online-from-a-science-article-njetcot1i5g

Interesting data science research article from Science to investigate that Lies spread faster than the truth. Authors used a data set of rumor cascades on Twitter from 2006 to 2017  found 126,000 rumors were spread by ∼3 million people. 

The spread of true and false news online from a Science article

https://www.datacamp.com/community/news/the-data-scientists-mindset-hx54233xu2t

Do you have the right mindset to become a great Data Scientist? 

The Data Scientist’s mindset

<p>Last August, DataCamp’s VP of Product Research, Ramnath Vaidyanathan, hosted a <a href="https://www.datacamp.com/resources/webinars/scaling-data-science-at-your-organization-part-1">three-part webinar series</a> on scaling data science with our IPTOP framework. The IPTOP framework breaks down every organizational lever to democratize data science and become data fluent organization-wide.</p>
<p>IPTOP stands for Infrastructure, People, Tools, Organization, and Processes. The three-part webinar series goes into great detail on how organizations can scale each of these levers to democratize data science and become data fluent in the process. Here’s a breakdown of each of those levers:</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1608306088/Demand_Gen_Themes_2021_eebx0u.png" /></a></p>
<h2 id="1-infrastructure">1 - Infrastructure</h2>
<p>The goal of any data strategy is to transform raw data into insights and decisions. This requires organizations to collect, record, and store their data safely and efficiently to be accessible by all. As such, this involves understanding best practices and frameworks for enabling data access, ensuring data governance, clarifying data lineage, and more.</p>
<h2 id="2-people">2 - People</h2>
<p>People are arguably the most critical lever in the framework, as organizations won’t make the most of their data if their members don’t have the skills to work with data. This is why cultivating the necessary data skills <a href="https://www.datacamp.com/resources/webinars/l-and-d-leaders-data-fluency">across every persona</a> in an organization is imperative for democratizing data science and becoming data fluent.</p>
<h2 id="3-tools">3 - Tools</h2>
<p>While infrastructure enables organizations to deliver insights from data, tools facilitate and incentivize a common data language across the organization. This is why it’s necessary to understand the range of possible tools that can be used for a specific task and invest and build tools that lower the barrier to entry for data science work.</p>
<h2 id="4-organization">4 - Organization</h2>
<p>An essential dimension of scaling data science is how data professionals are organized. Given that reporting structures and agendas drive work in most firms, the organizational structure must set up your company for sustainable success. As such, setting up and organizing data science talent within an organization requires careful consideration.</p>
<h2 id="5-processes">5 - Processes</h2>
<p>Finally, scaling data science requires alignment on conventions, best practices, and processes. Fostering alignment is essential to facilitating collaboration and avoiding a siloed organization. This allows all teams to work together and seamlessly communicate under a common data language.</p>
<p>Last week, we released our <a href="https://www.datacamp.com/resources/whitepapers/data-trends-and-predictions-2021">data trends and predictions for 2021</a>. Our predictions touch upon every lever in the IPTOP framework, from enabling more robust data access through increased cloud adoption and metadata tools (Infrastructure), a higher commitment to data upskilling (People), more collaborative and robust tooling (Tools), emergent roles for managing machine learning models in operation (Organization), and finally, best practices in data storytelling and visualization (Processes).</p>
<p>This is why we’re excited to renew our commitment to bringing the latest and most important insights from data science practitioners and leaders on how to best scale data science and become data fluent.</p>
<p>In 2021, we’ll be hosting a series of monthly webinars tackling how different industries can leverage data science, how organizations can govern their data and enable better data access, best practices to upskill people for data science, how to lower the barrier to entry to working with data with modern tooling, how to organize and hire the best data science talent, and how to set up processes to become data-driven, and more.</p>
<p>Keep an eye on our <a href="https://www.datacamp.com/webinars">Upcoming Events</a> page to stay in the loop—see you in 2021!</p>


https://www.datacamp.com/community/blog/data-fluency-2021

DataCamp is hosting a monthly webinar in series in 2021 focused on scaling data science and building organization-wide data fluency with our IPTOP Framework.

Data Fluency in 2021

<p><center><a href="https://www.datacamp.com/courses/bayesian-regression-modeling-with-rstanarm"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537986753/Bayesian_Regression_Banner_cqsqt9.png" /></a></p>
<p>Here is the course <a href="https://www.datacamp.com/courses/bayesian-regression-modeling-with-rstanarm">link</a>.</p>
<h3 id="course-description">Course Description</h3>
<p>Bayesian estimation offers a flexible alternative to modeling techniques where the inferences depend on p-values. In this course, you’ll learn how to estimate linear regression models using Bayesian methods and the rstanarm package. You’ll be introduced to prior distributions, posterior predictive model checking, and model comparisons within the Bayesian framework. You’ll also learn how to use your estimated model to make predictions for new data.</p>
<h3 id="chapter-1-introduction-to-bayesian-linear-models-free-">Chapter 1: Introduction to Bayesian Linear Models (Free)</h3>
<p>A review of frequentist regression using lm(), an introduction to Bayesian regression using stan_glm(), and a comparison of the respective outputs.</p>
<h3 id="chapter-2-modifying-a-bayesian-model">Chapter 2: Modifying a Bayesian Model</h3>
<p>Learn how to modify your Bayesian model including changing the number and length of chains, changing prior distributions, and adding predictors.</p>
<p><center><a href="https://www.datacamp.com/courses/bayesian-regression-modeling-with-rstanarm"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537986967/Screen_Shot_2018-09-25_at_11.57.55_AM_uuyxpc.png" /></a></p>
<h3 id="chapter-3-assessing-model-fit">Chapter 3: Assessing Model Fit</h3>
<p>In this chapter, we&#39;ll learn how to determine if our estimated model fits our data and how to compare competing models.</p>
<h3 id="chapter-4-presenting-and-using-a-bayesian-regression">Chapter 4: Presenting and Using a Bayesian Regression</h3>
<p>In this chapter, we&#39;ll learn how to use the estimated model to create visualizations of your model and make predictions for new data.</p>
<h3 id="prerquisites">Prerquisites</h3>
<ul>
<li><a href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-1">Data Visualization with ggplot2 (Part 1)</a></li>
<li><a href="https://www.datacamp.com/courses/multiple-and-logistic-regression">Multiple and Logistic Regression</a></li>
<li><a href="https://www.datacamp.com/courses/bayesian-modeling-with-rjags">Bayesian Modeling with RJAGS</a></li>
</ul>


https://www.datacamp.com/community/blog/course-bayesian-regression-modeling-rstanarm

New Course: Bayesian Regression Modeling with rstanarm

<h2>Introduction to Bayesian thinking</h2><p>This chapter introduces the idea of discrete probability models and Bayesian learning. You'll express your opinion about plausible models by defining a prior probability distribution, you'll observe new information, and then, you'll update your opinion about the models by applying Bayes' theorem.</p><h2>Learning about a binomial probability</h2><p>This chapter describes learning about a population proportion using discrete and continuous models. You'll use a beta curve to represent prior opinion about the proportion, take a sample and observe the number of successes and failures, and construct a beta posterior curve that combines both the information in the prior and in the sample. You'll then use the beta posterior curve to draw inferences about the population proportion.</p><h2>Learning about a normal mean</h2><p>This chapter introduces Bayesian learning about a population mean. You'll sample from a normal population with an unknown mean and a known standard deviation,  construct a normal prior to reflect your opinion about the location of the mean before sampling, and see that the posterior distribution also has a normal form with updated values of the mean and standard deviation. You'll also get more practice  drawing inferences from the posterior distribution, only this time, about a population mean.</p><h2>Bayesian comparisons</h2><p>Suppose you're interested in comparing proportions from two populations. You take a random sample from each population and you want to learn about the difference in proportions. This chapter will illustrate the use of discrete and continuous priors to do this kind of inference. You'll use a Bayesian regression approach to learn about a mean or the difference in means when the sampling standard deviation is unknown.</p>

https://www.datacamp.com/community/open-courses/beginning-bayes-in-r

Discover Bayesian statistics with R: represent your beliefs with the language of probability, collect some data and update your beliefs based on the evidence contained in the data.

Beginning Bayes in R

https://www.datacamp.com/community/tutorials/naive-bayes-scikit-learn

Learn how to build and evaluate a Naive Bayes Classifier using Python's Scikit-learn package.

Naive Bayes Classification using Scikit-learn

https://www.datacamp.com/community/tutorials/scikit-learn-fake-news

This scikit-learn tutorial will walk you through building a fake news classifier with the help of Bayesian models. 

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The spread of true and false news online from a Science article

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