How looks like a good data scientist?

data scientist
Data scientist

Data scientist, what an appealing definition, but what kind of skills and which tools do you need to start? What is a good definition of data scientist?

Let’s discover on the web some good resources that will help us

Bernand Marr, in his9 steps to become a data scientist from scratch” makes a good syntesis. I just reported what I like most:

  • Math & Statistical skills: mmh, not very appealing in some cases. I remind my study in Statistics and how much theoretical looks like. Probably I will discover soon that could be used in a more pratical way. 
  • Learn tools: most of the activity will be cleaning your data to make good analysis. Remember garbage in, garbage out
  • Community: life of data scientist in same cases is not easy, many different tools and languages to be used. Having some peers and community places where asking help and support is fundamental.
  • Practice: someone has said that 90% of what you will learn is training by doing. I don’t know if the percentage is realistic, but certainly what I have learn most is from my trials, success and fortunately from my mistakes

Karlijn Willems,  Remind us that Harvard Review has defined Data scientist  as sexiest job of the 21st Century.

You can find here a wonderful infographic that explain quite well 8 steps to become data scientist

  • Good at stat: with a list of good courses to learn or refresh your math or stats skills. Don’t loose too much time on theory! (I fully agree)
  • Understand database: maybe using SQL or similar languages with useful resources to learn about most popular db softwares/languages
  • Level up with big data: learn how to play with data not only on your computer but using multiple servers (that means doing more sophisticated and powerful analysis)
  • Master data, Visualization and Reporting: resources to learn to clean and use data,  visualize it in a good way and generate reports. Just for this chapter, you will find 8 software (and it is not an exhaustive list)

Another useful tip shared by Vijay Laxmi on Dzone is  “10 steps to become a Data scientist in 2018”

I personally like this suggestion:

Compete: maybe you don’t know but you can subscribe to Kaggle to find teammates and partecipate in projects, and maybe show your skills to data industry…

To summarize, I personally agree with this definition of data scientist:

“a person employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making”

To become data scientist we need to do the following things:

  • We need to refresh math & stat skills through Kahn Academy or Coursera. Stay tune to know more on this.
  • Learn tools (follow Tools page in our blog for more info)
    • to manage DB, mainly based on SQL languages
    • To clean and manage data: Coursera and R
    • Analytics tools:R, SAS, Python
    • Reporting & Visualization software like Tableau, Qlikview
  • Improve our communication skills to get powerful insight from data and give clear messages. Stay tuned on our section Communication skills
  • Community & Practice: start from simple project, but don’t forget to get involved with other buddies to share problems or projects, maybe with Kaggle or other community forums. In Community & Practice you will find useful resources about this topic.
  • Level up with big data: learn how to distribute your project on multiple peoples and servers

It’s a marathon and not a run, but  don’t worry. You will not be alone, and if you follow this blog you will find support and step-by-step guide.

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Do you agree with the description of data scientist and what need to be learned to be a good one? Let me know your comments