Begin your journey in applied data science and machine learning with this blueprint — Part I

Are you interested in pursuing a career in applied data science and machine learning? Then this article is for you.

Aditya Bhattacharya

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Image Source: Pixabay

“I am interested in learning data science and machine learning, but how do I start?” — This is one of the most common questions I get at least once every week. Since data science is one of the most trending topics in the technology world, many technology enthusiasts are interested in exploring this field for solving business problems. But surprisingly, in spite of the openly available learning resources on the internet, data science and ML enthusiasts do not get sufficient guidance to start their learning journey. So, in this article, I will present you with a blueprint to get started with your learning journey in data science and ML. Along with the important topics, I will provide additional learning resources, book recommendations and hands-on problem suggestions to build your applied knowledge. Let’s dive in to the Part-I of series!

Do I need to learn both R and Python?

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Learning a new language can be tough, especially if you are familiar with any particular programming paradigm. Yet, almost every data scientist in the world practices their work in either R or Python as these two languages have very good libraries and frameworks available for implementing complex algorithms with fantastic community support. So, do you need to learn both R and Python? If not, which one is better?

I would say, both R and Python are equally popular choices, but learning both is not needed. Usually, the choice of Python or R depends on the organization or the individual. But good proficiency in either would be a great first step in your learning journey. My personal preference is Python, as the programming paradigm of Python is more simplified and productionizing Python code/scripts are much easier because of better community support for Python. It is usually observed that folks from academic backgrounds prefer R, but practitioners…

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