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Data Science Beginners’ FAQs

Data Science Beginners’ FAQs


On a regular working day, I receive at-least 4–5 messages from beginners on LinkedIn asking me different questions about Machine Learning (ML)/ Data Science (DS). Initially, I used to respond each question, later I started copying my previous responses and referring to my relevant blog-posts. But this week I realized that I could write a blog-post on DS/ML beginners’ FAQs and refer anyone to this post if someone asks me any of these questions.

I have not reinvented the wheel as such, for each question I have given my perspective and provided relevant posts, links or sources which you can go through to gain an understanding.

  1. Who all are fit to learn ML/DS? What are the pre-requisites to learn ML/DS?

  2. How to start with ML/DS? What are the good sources to learn different topics in ML/DS?

Almost anybody can learn DS/ML, you can follow an approach to learn DS/ML from scratch. There is no prerequisite as such, you just need a curious & logical mind.

I have written several blog-posts from different view-points, please have a look. Kaggle also has a learn section which is kind of fastest way to get started with ML/DS.

View at Medium.com View at Medium.com View at Medium.com

  1. Which MOOCs are worth spending time?

Yes, these days scarcity of information is not a problem, plethora of courses are available. The problem is for the beginners to decide which course to follow. In my view, every course has its own pros & cons. Some courses are theoretical, some are practical, some are for beginners and some are for experienced. One needs to do a kind of research what they are looking for.

View at Medium.com View at Medium.com

  1. I have done XYZ course on ML/DS, how to get hands-on experience?

Public data-sets are the best way to get hands-on experience. First, start with a data-set on which most the people have worked on so that you can compare how good/bad you are doing. Later, you can move to different data-sets or the problem spaces that you care to get exposure and showcase the skills you have built.

View at Medium.com View at Medium.com View at Medium.com

  1. How can I get job in ML/DS area as a fresher?

Yes, it is difficult to get job in ML/DS area as a fresher, the reason are many. Some organizations don’t have funds or time to invest on training freshers, some projects have tight delivery timelines or some teams don’t have ML/DS experts to lead etc. But there are ways, if you can build your skills and showcase your prospective employer that you can be productive from day one, you can increase your chances to get hired by leaps & bounds.

View at Medium.com View at Medium.com

  1. I am working in IT for X years, how can I start with ML/DS?

  2. I am a working professional and done XYZ course on ML/DS, how can I use these skills in my current job?

If you are already working, and you have learnt ML/DS skills as mentioned above, there is a fair chance that you can identify opportunities in your current organization as every business has data which can give them competitive advantage. Convincing your manager or business can be challenging but it depends how good you have become to convince them.

View at Medium.com View at Medium.com View at Medium.com View at Medium.com

  1. What kind of problems data scientists solve? What is their day-to-day job looks like?

Every organization’s culture is different, its business is different, its data is different, its challenges are different. Yet you can find similarities in how data scientists work in those organizations on DS/ML projects.

View at Medium.com View at Medium.com View at Medium.com

So, I have covered almost all the questions I have been asked till date, I may expand this post if someone asks me a different question which I feel can benefit larger pool of beginners.

Happy learning.

Thank you for reading my post. I regularly write about Data & Technology on LinkedIn & Medium. If you would like to read my future posts then simply ‘Connect’ or ‘Follow’. Also feel free to listen to me on SoundCloud.

#DataScience #FAQ #MachineLearning

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