### Data science toolbox

We are going to be using **python 3 **for this course. You need to have some basic knowledge of how to run python scripts, how to use **pip3 **to install python packages and **basic programming concepts **such as how to use data structures, loops and control statements. If you know **JavaScript **it will help but it is not mandatory. The same goes for **reading academic papers**. If you have previous experience of **where to find them, how to read them **and **how to understand them** it will help but all algorithms will be handed out so you don’t need to find them yourself. If you know **basic arithmetic, **such as **plus, minus, division **and **multiplication ** you are good to go. The **linear algebra **needed for the course will be presented in a fashion where no prior knowledge is needed. However, it might be assumed that you **spend a little time on your own** making sure you understand them.

### 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.

### Content

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.