A Learning Path To Becoming a Data Scientist

A Learning Path To Becoming a Data Scientist

A Learning Path To Becoming a Data Scientist. In this article, I will layout a 10 steps roadmap from start to finish of concepts you need to cover along your data science learning journey.

Data science is one of the rapidly growing fields that demand a data scientist growing up daily. As of October 2020, I can’t see this demand slowing down anytime soon. It is an interdisciplinary field that can help us analyze the data around us to make our life better and our future brighter.

Luckily, becoming a data scientist does not require a degree. As long as you are open to learning new things and willing to put in the effort and time, you can become a data scientist.

The question now is, where to start?

“The beginning is perhaps more difficult than anything else, but keep heart, it will turn out all right.”

― Vincent van Gogh

The internet is full of tutorials about all the details of every data science aspect, such as machine learning basics, natural language processing, speech recognition, and all kind of amazing data science magic.

But,

For a beginner, the amount of information can be overwhelming and lead someone to give up before they even start.

What could help is having a structured roadmap that clearly lays out what you need to learn and the order that you should learn to become a data scientist.

In this article, I will layout a 10 steps roadmap from start to finish of concepts you need to cover along your data science learning journey.

Step №1: Programming

If you’re new to the technical field, then programming would be the best place to start. Currently, the two programming languages used most in data science are Python and R.

  • R: A programming language for statistical computing. R is widely for developing statistical software and data analysis.
  • Python: A high-level, general-purpose programming language. Python is widely used in many applications and fields, from simple programming to quantum computing.

Because Python is a beginner-friendly programming language, I find it a great place to start with data science and maybe more fields in the future. Due to Python's popularity, there are many resources available to learn it independently of your goal application field.

Some of my favorite Python learning resources are CodeAcademyGoogle ClassesLearn Python the Hard Way.

However, if you decide to go with R, both Coursera and edX have great courses that you can audit for free.

Some of you might already know how to program and might be transferred to data science from another technical field. In that case, you can skip this step and move forward to the next step of the journey.

programming work machine-learning future data-science

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