How to be a good data sciencist?

Hi Lee,

I think there are many valid approaches because data science is such a large field. Many good data scientists come from very different backgrounds. A lot of this comes down to where you want to focus. Based on your post, I’m going to guess that you are interested in focusing on practical applications of AI. If that’s correct, then I would recommend that you let practical projects and examples drive your studies. Having said that, it can be hard to get started unless you know at least some of the theory and principles. So learn some theory and principles; in particular, make sure you know how to correctly validate your models. Then get started solving problems. As you go, you may find that it would help to learn a little more theory, or improve your technical skills in a particular software package or programming language.

To this end, I would recommend beginning with three of our free courses to cover a little of the theory and principles, and to get started with some hands-on examples. Then as soon as you can, get started with your own projects. As you work on your own projects, I’m confident you’ll find many learning opportunities.

1.      https://academy.rapidminer.com/learn/course/applications-use-cases-professional

2.      https://academy.rapidminer.com/learn/course/data-engineering-professional/

3.      https://academy.rapidminer.com/learn/course/machine-learning-professional

Now there’s plenty more about becoming a good data scientist that I have left unsaid, but I think this could be a good way to start!

Regards,

Jeff

Thank you, Mr Jmergler
Your suggestion is very inspiring!

Hi SpyderMan: In  addition to Jeff's great suggestions above, I would suggest considering other factors that may determine if your data science projects are considered successful or not - such as whether or not your company is prepared to implement and use the predictive models that you build.  Believe it or not, many data science projects are not considered successful even if the predictive models are quite good.  This is because many organizations have not done the planning work required to integrate the outputs of data science projects into the workflows that other people perform.  If people within an organization have no way to use the predictive model outputs to make a positive business difference, business leaders will likely consider that the project has not really paid off - even if the technical "data science work" is very good.  Data Scientists have the opportunity to act in more than just a technical role - they can also help management understand that developing predictive models is just the first step in getting business value from them.  Also important is to validate and discuss the outputs of your models with business people within your company as you are developing them so that they grow to trust the predictions the model makes.  Subject matter experts may also tell you very helpful information that could lead to improvements in your models.  Wishing you every success!  Best wishes, Michael Martin