<story><title>The Importance of Probability in Data Science</title><url>https://www.kdnuggets.com/2023/02/importance-probability-data-science.html</url></story><parent_chain><item><author>nerdponx</author><text>It&amp;#x27;s disheartening to see that the content of this isn&amp;#x27;t just &amp;quot;well, duh!&amp;quot; and that some people actually need to be convinced.&lt;p&gt;That said, this article is &lt;i&gt;weird&lt;/i&gt; (&amp;quot;theoretical probability&amp;quot; vs. &amp;quot;experimental probability&amp;quot;... huh?) and is not exactly something I would want to share around. It almost seems like the author wrote it for their own benefit, after themselves recently realizing that probability is important and useful&amp;#x2F;essential in data science, and wanting to share what they learned. I&amp;#x27;m not sure it&amp;#x27;s worth anyone&amp;#x27;s time to read it.</text></item></parent_chain><comment><author>qsort</author><text>&amp;gt; this article is weird&lt;p&gt;Yes, it&amp;#x27;s very weird. I can&amp;#x27;t tell if the author can&amp;#x27;t express themselves well enough in English, doesn&amp;#x27;t understand the topic, or something else happened (editorialization, SEO garbage...)&lt;p&gt;&amp;gt; Likelihood is applied when you want to increase the chances of a specific event or outcome occurring.&lt;p&gt;???&lt;p&gt;&amp;gt; if 12 cars go down a particular road at 11 am every day, we can use Poisson distribution to figure out how many cars go down that road at 11 am in a month.&lt;p&gt;???&lt;p&gt;&amp;gt; if we’re looking at how many chocolate bars were sold in a day, we would use the normal distribution. However, if we want to look into how many were sold in a specific hour, we will use t-distribution&lt;p&gt;???</text></comment>
<story><title>The Importance of Probability in Data Science</title><url>https://www.kdnuggets.com/2023/02/importance-probability-data-science.html</url></story><parent_chain><item><author>nerdponx</author><text>It&amp;#x27;s disheartening to see that the content of this isn&amp;#x27;t just &amp;quot;well, duh!&amp;quot; and that some people actually need to be convinced.&lt;p&gt;That said, this article is &lt;i&gt;weird&lt;/i&gt; (&amp;quot;theoretical probability&amp;quot; vs. &amp;quot;experimental probability&amp;quot;... huh?) and is not exactly something I would want to share around. It almost seems like the author wrote it for their own benefit, after themselves recently realizing that probability is important and useful&amp;#x2F;essential in data science, and wanting to share what they learned. I&amp;#x27;m not sure it&amp;#x27;s worth anyone&amp;#x27;s time to read it.</text></item></parent_chain><comment><author>hcks</author><text>Well I indeed expected more than a few basic definitions for sure.&lt;p&gt;For instance I was expecting a few case studies where a blind application of sklearn defaults led to issues fixed with proper probabilistic thinking.&lt;p&gt;I remain unconvinced about the importance of probabilities for data science, even more so when I doubt 1% of practicing data scientists could explain what a measure is.</text></comment>