How to become a data scientist
There’s a lot of focus on data science as of late, and along with it comes the pressing question – how does one go about becoming a data scientist?
We believe that it is really dependent on where you are now and what you really want to perform as a data scientist.
Nonetheless, DataCamp posted an infographic that detailed 8 simple steps to becoming a data scientist. In this blog post by AICorespot, we want to highlight and review DataCamp’s infographic.
What is a Data Scientist?
Prior to defining the stages to becoming a data scientist, the graphic defines what a data scientist is leveraging three critical resources:
- Drew Conway’s data science venn diagram that brings together hacking skills, mathematics, and statistical know-how and substantive expertise.
- A graph displaying the survey outcomes on the question of education level, not unlike the graph in O’Reilly’s Analyzing the Analyzers.
- Josh Wills’ quote on what is a data scientist.
Become a Data Scientist
From the infographic, the eight stages to becoming a data scientist are:
1] Get good at statistics, mathematics, and machine learning. Enroll in online courses.
2] Learn to code. Computer Science, development, and a language.
3] Understand databases. Data variants, technologies that record them, and strategies to retrieve data.
4] Master data munging, visualization and reporting. Tools.
5] Level up with big data. Bigger tools such as Hadoop, mapreduce and spark.
6] Obtain experience, practice and meet fellow data scientists. Contests, pet project and developing an intuition.
7] Internship, bootcamp, or get a job
8] Follow and engage with the community
At first look, the graphic indicates that conventional mantra of become a math and programming genius prior to even looking at data or algorithms, a strategy we believe is wrong.
At closer inspection, the graphic is indicating a path of familiarization from stages 1-5. It provides suggestions to take courses and get up to speed with the language of data science and data.
Then stages 6-7 are about actually working problems and cultivating skills and abilities prior to topping out and following the community in step 8.
From this more nuanced viewpoint, it’s an amazing graphic.
We would go one step further.
We would provide the suggestion that stages 1-5 be reduced further to a single step that furnished a crash course of terms and themes across these spheres. We would provide the suggestion of getting to a point of working a dataset leveraging a tool as quickly as possible. Working through this procedure and working problem after problem will illustrate the requirement and furnish the context for those foundational topics that can be learned and weaved in just-in-time.
A segmented linear decomposition is ideal for course design and infographics, but not best for learning and obtaining outcomes. We believe the modules or steps should be integrated.
Researching and studying computer science can make you a solid computer scientist (for whomever requires whatever that is) and a more well-rounded engineer, but to be an amazing programmer, you require to practice programming.
We believe the same is applicable to working data problems. To get really good at working problems end-to-end, you are required to concentrate on and practice this procedure and learn relevant theory in the context of this procedure. It will function as a great knife, cutting scope that what is needed and relevant, rather than all that happens to be in the courses and the textbooks.
So how do you become a data scientist? Work on a ton of data problems. Keep working on them until its perfect.