Data science toolbox
Why should you care about data science?
Data science and machine learning (ML) is becoming bigger and more hyped. Python as a language is growing and have a lot of nice libraries for data science and ML.
Because of the hype it might be expected of you at your current workplace to have some kind of “in the ballpark” knowledge about the topics. You might be interested in getting these knowledges to boost your career to land some prestigous new work. It is no secret that data science jobs are very well paid if salary is an interest of yours. You might be interested in gaining some knowledge about a more theoretical area than you normally spend your time in, e.g. linear algebra and its applications. I will also say that the biggest benefit of this tutorial series is that we will actually see practical implementations and use cases, so the videos will be more pragmatic than normally seen at universities or online courses.
The content is dynamic and I will take request on topics that you are interested in. Just leave it in the comment section on youtube and I will see what I can do.
Some of the topics that will be covered are linear algebra, because many algorithms are based on manipulation of matrices, like matrix multiplication and vector multiplications. Other topics include data normalization, so that the data behaves in nice way making certain algorithms practically feasible. We will also touch on the subject of similarity measurements, so that we can make sense of our data when we are comparing highly dimensional data. This is not as trivial as it might seem at first glance.