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#datascientists

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☮ ♥ ♬ 🧑‍💻<p>“Students are introduced to advanced AI techniques such as <a href="https://ioc.exchange/tags/ChainOfThought" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ChainOfThought</span></a> and <a href="https://ioc.exchange/tags/SelfConsistencyPrompting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SelfConsistencyPrompting</span></a>, which simulate humanlike reasoning. <a href="https://ioc.exchange/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GenerativeAI</span></a> is presented not just as a tool for queries but as a partner in reasoning. </p><p>“We teach reinforcement learning from human feedback, where every correction becomes training data,” Madmoun adds. </p><p>Students are encouraged to view AI not as a static engine, but as a responsive tool for making critical decisions in high-stakes financial environments.</p><p>Recognising that students enter with varying levels of technical knowledge, the Master in International Finance (MiF) at HEC Paris provides asynchronous <a href="https://ioc.exchange/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://ioc.exchange/tags/programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>programming</span></a> courses, optional <a href="https://ioc.exchange/tags/BootCamps" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BootCamps</span></a>, and tailored elective tracks. “We’ve integrated workshops taught by Hi! PARIS into the curriculum,” says academic director Evren Örs, referring to the <a href="https://ioc.exchange/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> and <a href="https://ioc.exchange/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> centre co-founded by HEC Paris and Institut Polytechnique de Paris. </p><p>Students from both institutions collaborate on real-data projects, strengthening both technical and teamwork skills.</p><p>A tiered elective system requires all MiF students to complete at least one course focused on <a href="https://ioc.exchange/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> and <a href="https://ioc.exchange/tags/finance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>finance</span></a>. The most advanced track is the <a href="https://ioc.exchange/tags/DoubleDegree" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DoubleDegree</span></a> in data and finance, where students dive deep into <a href="https://ioc.exchange/tags/MachineLlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLlearning</span></a> applications. Graduates, Örs says, are frequently hired as <a href="https://ioc.exchange/tags/QuantitativeAnalysts" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>QuantitativeAnalysts</span></a>, <a href="https://ioc.exchange/tags/DataScientists" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScientists</span></a>, and private equity analysts in London and Paris.”</p><p><a href="https://ioc.exchange/tags/BusinessSchools" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BusinessSchools</span></a> / <a href="https://ioc.exchange/tags/education" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>education</span></a> &lt;<a href="https://archive.md/xysyM" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">archive.md/xysyM</span><span class="invisible"></span></a>&gt; / &lt;<a href="https://ft.com/content/071dc338-b267-466c-836a-f559609fffd5" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ft.com/content/071dc338-b267-4</span><span class="invisible">66c-836a-f559609fffd5</span></a>&gt; (paywall)</p>

Visualizing gene structures in R? gggenes, an extension of ggplot2, simplifies the process of creating clear and informative gene diagrams, making genomic data easier to interpret and share.

Visualization: cran.r-project.org/web/package

Click this link for detailed information: statisticsglobe.com/online-cou

Evaluating the normality of your data is crucial in statistical analysis, as many techniques assume that the data and/or residuals follow a normal distribution.

The visualization in the post contrasts two QQ plots: the left plot shows a data set following a normal distribution, where the points align closely with the reference line.

Check out this tutorial: statisticsglobe.com/r-qqplot-q

Click this link for detailed information: statisticsglobe.com/online-cou

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The employees also warned that many of those enlisted by #ElonMusk to help him slash the size of the federal government under #Trump’s admin were political ideologues who did not have the necessary skills or experience for the task ahead of them.

The mass #resignation of #engineers, #DataScientists & #ProductManagers is a temporary setback for #Musk & the Republican president’s tech-driven #purge of the federal workforce.

Principal Component Analysis (PCA) before Linear Regression can greatly enhance your data analysis process.

By incorporating PCA before performing linear regression, you can streamline your analysis pipeline and build more robust models that capture the essential relationships within your data.

I've developed an in-depth course on PCA theory and its application in R programming.

Further details: statisticsglobe.com/online-cou

«If we asked you to imagine a decillion dollars, can you actually picture it in your head? The odds are, you can't. A study in 2013 showed that people find it more difficult to comprehend larger numbers. But why?»

People are also bad in assessing very small and very last probabilities. #DataScientists should take this psychological #Biases into account when presenting results to human audiences.

bbc.com/reel/video/p0k962x2/wh via #BBC

www.bbc.comWhy our brains are bad at understanding big numbersCan you picture a decillion dollars in your head? We find out why our brains can't handle large numbers.

Diving into Principal Component Analysis (PCA) unveils two heroes of data simplification: Eigenvalues and Eigenvectors. These mathematical concepts might sound intimidating, but they're crucial for understanding how PCA transforms complex data into something much more manageable. Let's demystify them:

Looking to get hands-on with eigenvalues, eigenvectors, and PCA using the R programming language? Unlock the power of your data: statisticsglobe.com/online-cou

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