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/podcast/data-science-gambling-bookmaking

<p>Marco Blume, Trading Director at Pinnacle Sports talks about the role of data science in large-scale bets and bookmaking, how Marco is training an army of data scientists and much more.</p>

Marco Blume, Trading Director at Pinnacle Sports talks about the role of data science in large-scale bets and bookmaking.

Data Science, Gambling and Bookmaking

https://www.datacamp.com/community/blog/data-science-gambling-bookmaking

https://www.datacamp.com/community/news/weapons-of-math-destruction-with-cathy-oneil-du7aqb297we

Cathy and Hugo discuss the current lack of fairness in artificial intelligence, how societal biases are perpetuated by algorithms and how both transparency and auditability of algorithms will be necessary for a fairer future.

Weapons of Math Destruction (with Cathy O'Neil)

https://www.datacamp.com/community/news/testing-multiple-interventions-in-a-single-experiment-2jedgabir95

A reader recently inquired about functions in simstudy that could generate data for a balanced multi-factorial design. I had to report that nothing really exists. A few weeks later, a colleague of mine asked if I could help estimate the appropriate sample size for a study that plans to use a...

Testing multiple interventions in a single experiment

https://www.datacamp.com/community/news/16-useful-advices-for-aspiring-data-scientists-5ggav9rqq3u

Data is being generated exponentially and those who can understand and extract value from it are needed more than ever. The hard-earned lessons and joy about data and models from these thoughtful practitioners would be tremendously useful if you aspire to join the next generation of data scientists.

16 Useful Advices for Aspiring Data Scientists

https://www.datacamp.com/community/news/logistic-regression-in-r-tutorial-pfa5sd77cy

Discover all about logistic regression: how it differs from linear regression, how to fit and evaluate these models it in R with the glm() function and more!

Logistic Regression in R Tutorial

https://www.datacamp.com/community/news/network-analysis-in-r-centrality-measures-gqj0ns7618r

Explore the definition of centrality, learn what different types of centrality measures exist in network analysis and pick the best one for a given network!

Network Analysis in R: Centrality Measures

<p><center><a href="https://www.datacamp.com/courses/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603467080/Python_vs._R_for_Data_Science__What_s_the_Difference__klxnwh.png" /></a></center><br></p>
<p>If you’re new to data science, or your organization is, you’ll need to pick a language to analyze your data and a thoughtful way to make that decision. Full disclosure: While I can write Python, my background is mostly in the R community—but I&#39;ll try my best to be non-partisan.</p>
<p>The good news is that you don&#39;t need to sweat the decision too hard: both Python and R have vast software ecosystems and communities, so either language is suitable for almost any data science task.</p>
<p>The two most commonly used programming language indexes, <a href="https://www.tiobe.com/tiobe-index">TIOBE</a> and <a href="https://spectrum.ieee.org/computing/software/the-top-programming-languages-2019">IEEE Spectrum</a>, rank the most popular programming languages. They use different criteria for popularity, which explains the differences in the results (TIOBE is entirely based on search engine results; IEEE Spectrum also includes community and social media data sources like Stack Overflow, Reddit, and Twitter). Of the languages on each list that are commonly used for data science, both indexes list Python as the most popular language for data science, followed by R. MATLAB and SAS come in third and fourth place, respectively.</p>
<p>Now that we&#39;ve established that Python and R are both good, popular choices, there are a few factors that may sway your decision one way or the other.</p>
<h2 id="what-language-do-your-colleagues-use-">What language do your colleagues use?</h2>
<p>The most important factor in deciding which programming language to use is knowing which language your colleagues use, since the benefits of being able to share code with your colleagues and maintaining a simpler software stack outweigh any benefits of one language over another.</p>
<h2 id="who-is-working-with-data-">Who is working with data?</h2>
<p>Python was originally developed as a programming language for software development (the data science tools were added later), so people with a computer science or software development background often find Python comes more naturally to them. That is, the transition from other popular programming languages like Java or C++ to Python is easier than the transition from those languages to R.</p>
<p>R has a set of packages known as the Tidyverse, which provide powerful yet easy-to-learn tools for importing, manipulating, visualizing, and reporting on data. Using these tools, people without any programming or data science experience (at least anecdotally) can become productive more quickly than in Python. If you want to test this for yourself, try taking <a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a>, which introduces R&#39;s <a href="https://dplyr.tidyverse.org/">dplyr</a> and <a href="https://ggplot2.tidyverse.org/">ggplot2</a> packages, and <a href="https://www.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a>, which introduces Python&#39;s <a href="https://pandas.pydata.org/">pandas</a> and <a href="https://matplotlib.org/">Matplotlib</a> packages, and see which you prefer.</p>
<p>Verdict: If data science in your organization will primarily be conducted by a dedicated team with programming experience, Python has a slight advantage. If you have many employees who don&#39;t have a data science or programming background, but who still need to work with data, R has a slight advantage.</p>
<h2 id="what-tasks-are-you-performing-">What tasks are you performing?</h2>
<p>While Python and R can basically both do any data science task you can think of, there are some areas where one language is stronger than the other.</p>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Where Python Excels</th>
<th>Where R Excels</th>
</tr>
</thead>
<tbody>
<tr>
<td>The majority of <strong>deep learning</strong> research is done in Python, so tools such as <a href="https://keras.io/">Keras</a> and <a href="https://pytorch.org/">PyTorch</a> have &quot;Python-first&quot; development. You can learn about these topics in <a href="https://www.datacamp.com/courses/deep-learning-with-keras-in-python">Introduction to Deep Learning in Keras</a> and <a href="https://www.datacamp.com/courses/deep-learning-with-pytorch">Introduction to Deep Learning in PyTorch</a>.</td>
<td>A lot of <strong>statistical modeling</strong> research is conducted in R, so there&#39;s a wider variety of model types to choose from. If you regularly have questions about the best way to model data, R is the better option. DataCamp has a large selection of courses on <a href="https://www.datacamp.com/search?facets%5Btechnology%5D%5B%5D=r&amp;facets%5Btopic%5D%5B%5D=Probability+%26+Statistics">statistics with R</a>.</td>
</tr>
<tr>
<td>Another area where Python has an edge over R is with <strong>deploying models into other pieces of software</strong>. Since Python is a general purpose programming language, you can write the whole application in Python and then including your Python-based model is seamless. We cover deploying models in <a href="https://www.datacamp.com/courses/designing-machine-learning-workflows-in-python">Designing Machine Learning Workflows in Python</a> and <a href="https://www.datacamp.com/courses/building-data-engineering-pipelines-in-python">Building Data Engineering Pipelines in Python</a>.</td>
<td>The other big trick up R&#39;s sleeve is easy <strong>dashboard creation</strong> using <a href="https://shiny.rstudio.com/">Shiny</a>. This enables people without much technical experience to create and publish dashboards to share with their colleagues. Python&#39;s <a href="https://plot.ly/dash/">Dash</a> is an alternative, but not as mature. You can learn about Shiny in <a href="https://www.datacamp.com/courses/building-web-applications-with-shiny-in-r">Building Web Applications with Shiny in R</a> and <a href="https://www.datacamp.com/courses/building-web-applications-in-r-with-shiny-case-studies">Building Web Applications with Shiny in R: Case Studies</a>.</td>
</tr>
</tbody>
</table></div>
<p>This list is far from exhaustive and experts endlessly debate which tasks can be done better in one language or another. Again, there is more good news: Python programmers and R programmers borrow good ideas from each other a lot. For example, Python&#39;s <a href="https://plotnine.readthedocs.io/en/stable/">plotnine</a> data visualization package was inspired by R&#39;s <a href="https://ggplot2.tidyverse.org/">ggplot2</a> package, and R&#39;s <a href="https://rvest.tidyverse.org/">rvest</a> web scraping package was inspired by Python&#39;s <a href="https://www.crummy.com/software/BeautifulSoup/">BeautifulSoup</a> package. So eventually the best ideas from either language make their way into the other.</p>
<p>If you’re too impatient to wait for a particular feature in your language of choice, it&#39;s also worth noting that there is excellent language interoperability between Python and R. That is, you can run R code from Python using the <a href="https://rpy2.bitbucket.io/">rpy2</a> package, and you can run Python code from R using <a href="https://rstudio.github.io/reticulate/">reticulate</a>. That means that all the features present in one language can be accessed from the other language. For example, the <a href="https://keras.rstudio.com/">R version</a> of deep learning package <a href="https://keras.io/">Keras</a> actually calls Python. Likewise, <a href="https://f0nzie.github.io/rTorch/">rTorch</a> calls <a href="https://pytorch.org/">PyTorch</a>.</p>
<h2 id="what-do-your-competitors-use-">What do your competitors use?</h2>
<p>If you work at a business that is growing fast and want to recruit top employees, it&#39;s worth doing some opposition research to see what technologies your competitors are using. After all, your new hires will be productive more quickly if they don&#39;t have to learn a new language.</p>
<h2 id="summary">Summary</h2>
<p>Programming language wars are mostly excuses for people to promote their favorite language and have fun trolling people who use something else. So I want to be clear that I&#39;m not interested in starting another argument on the internet about Python versus R for data science.</p>
<p>I hope I&#39;ve convinced you that, while both Python and R are good choices for data science, factors like employee background, the problems you work on, and the culture of your industry can guide your decision.</p>


https://www.datacamp.com/community/blog/when-to-use-python-or-r

Here’s how to determine when to use the two most popular languages for data science: Python and R. 

Python vs. R for Data Science: What’s the Difference?

Debbie Berebichez speaks with Hugo about what type of critical thinking skills are necessary in data science.

<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

<h2>Lab 1 - Intro to probability and data</h2><p>Questions for Lab 1</p><h2>Lab 2 - Introduction to data</h2><p>Questions for Lab 2</p><h2>Lab 3 - Probability</h2><p>Questions for Lab 3</p>

https://www.datacamp.com/community/open-courses/introduction-to-probability-and-data-labs

This interactive DataCamp course complements the Coursera course Introduction to Probability and Data by Mine Çetinkaya-Rundel. 

Introduction to Probability and Data - Labs

https://www.datacamp.com/community/tutorials/python-statistics-data-science

A list of Python resources for the eight statistics topics that you need to know to excel in data science

40+ resources for Python statistical analysis. Understand statistics with Python and topics that you need to know to excel in data science today!

Statistics with Python & 40+ Resources For Statistical Analysis

40+ Python Statistics For Data Science Resources

https://www.datacamp.com/community/tutorials/markov-chains-python-tutorial

Learn about Markov Chains, their properties, transition matrices, and implement one yourself in Python!

Learn about Markov Chains and how they can be applied in this tutorial. Build your very own model using Python today!

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