The Gap: Where Machine Learning Education Falls Short
This complaint comes up time and time again. &quot;Universities should prepare students better for jobs!&quot; &quot;Teach more real life skills!&quot; &quot;Turn CS uni into trade school!&quot;<p>But a computer science university program is not about scikit-learn or TensorFlow! It&#x27;s about long-lasting principles, underlying mathematics, mental models and ways of thinking.<p>None of my computer science lectures were about how to apply that particular part of CS knowledge in some hot new Python library. It&#x27;s expected that there will be some amount of time required to adjust to a company&#x27;s software setup. That&#x27;s not a big hurdle usually.<p>I&#x27;m not saying it should be only theory, though. University courses often have accompanying assignments or projects. Depending on the country in question, they often offer more hands-on, practical courses (&quot;lab courses&quot;) as well, where you do actually go through the steps of making the theory work in real life. I had such courses where we played with microcontrollers and FPGAs to understand CPU instructions, assembly and low-level C concepts (but even there the goal wasn&#x27;t to learn exactly the thing that you will use on the job. Most CS graduates will never need to program FPGAs in their day job.).<p>But sure, there is a place for even more data engineering training, but I don&#x27;t think it&#x27;s computer science university programs. Where do people like network engineers learn how to configure Cisco routers and use whatever config software they use? Where do sysadmins learn Bash, Unix, backup management etc? Not at university courses. Wherever they learn those skills, that&#x27;s where data cleaning, parallelization engineering etc aspects of machine learning should be taught as well.