5 Tips to kickstart your Data Science journey! (TESTED)

In this short article, we’ll skip the introduction and go over 5 steps that you can choose to or not choose to apply in your journey to become a Data Scientist. Now, you may be intrigued about why the word ‘Tested’ is in the title and that’s because these steps are things that I have tried on my own and have worked for me.

I’ll let you in some personal results from these tips and you can see how they helped me.

Feel free to twist and change them as per your liking and enjoy learning.

Without any further adieu; let’s get right into it. Skip any steps you already are applying in your journey. The five tips are:

  1. What?

1. What?

Step 1 of learning is a simple 4 letter word followed by a question mark (?). The entire point of learning is to understand things you did not understand previously or were unaware of.

And it all starts with asking ‘What?’. Once you start asking What things are and once you spark that curiosity in you, you will essentially have kicked off perfectly right on the journey.

Being curious and asking questions will let you dive deeper into things. You can ask What? and then proceed to ask Why? and it’s a better question to ask because it will get you to understand concepts thoroughly.

P.S. Read the 5 Why’s method to see how to get to the root of things.

Test: I didn’t know Tableau could make a dashboard THAT EASILY. I didn’t know why Normal Distributions existed. I absolutely didn’t know why correlation did not mean causation and what entirely is hypothesis testing. It was a constant run on the question ‘What’ that led me to learn all of it.

If you don’t know where to start, just go with “Data Science… What?”

2. Think 2022 *T&C Apply if you’re reading it in any other year in the future it applies to that year.

Do we still use VBA in the majority of our tasks with data today? (Visual Basic is the language of Excel and it is used but not… much)

Ans. No, There are a lot of other languages that are on the trend like Python, R, SQL, Scala and there’s no end to the immense possibilities they offer.

Do I need to use a pen and paper to calculate the Pearson’s Correlation for a dataset? or even better; Do we have to find out the highest correlation with the naked eye in a correlation matrix? (If you don’t know what this matrix is, let me show you)

Source: https://www.displayr.com/what-is-a-correlation-matrix/

Ans. Well, this is a lot of information; but again we can calculate correlation using statistical packages in coding with just one line and the naked eye would not be able to identify a high correlation easily in so many rows and columns, but a heatmap combined with this is probably the way to go.


The point in consideration is pretty simple;


Find out what’s new.

Find out what’s the best and the most ‘current’ way of doing something.

Do that.

Be in 2022 and not 10 years late to the party.

Test: The examples above are my life in the making from 1 year ago and today. No kidding.

3. It’s not a sprint. It’s a MARATHON.

The term science itself expands the dimensions of our study and Data Science being fairly new, it is a culmination of different streams related to data. We’re talking about Data Engineering, Warehousing, Modelling, Cleaning, etc. all in one.

The point here is that it takes time.

This is not a sprint but a marathon that will eventually take you to your destination with one simple concept hand-in-hand: CONSISTENCY.

Test: I started this blog 2 years ago and I have over 27 certifications done from multiple sources and yet I feel I’ve only touched the tip of the iceberg. Be curious. Ask questions and keep moving ahead. Slow progress > No progress.

4. Buddy up!

I’ve given the emphasis on why it’s important to have a buddy in your journey or at least peers around you in my previous articles. The fact is that working with data is like an investigation, you need to have more than one perspective on the evidence. The more you see it from different angles and the more you share what you know, you will exponentiate your learning.

Find some fun discord groups and some study circles to join.

You will not regret it and you will have like minded friends who will keep you going on your journey.

Test: I have many connections on my LinkedIn thanks to this blog today and I have helped people on Discord and had them help me at certain times too. Giving and taking help will get you on the fast track to learning.

5. Design your own Pizza

The reference is metaphorical.

When you have to study on your own and something that is fairly new, it’s best to always do your research and get a plan devised just for yourself. Once you decide what type of crust and toppings you prefer, the Pizza will eventually be the best one of your life.

It is an ocean of learning when you start with Data Science and it is easy to get lost in the millions of things people suggest you learn but only when you choose what is best for your learning and when you can carve out that niche for yourself, you will learn only what you need and you will enjoy it ten folds.

Test: I never had an idea that I was going to do Data Science 2 years ago. One fine day I just learned VBA and realized coding was cool. Then I went on to learn Python because why not? Similarly, it led to one thing after another and I enjoyed finding things online and learning on my own. (Helped me keep up with the 3rd tip mentioned above)

I wish you all the very best on your journey of learning and I hope my articles help you out in some way to get to your destination.

For more such articles, stay tuned with us as we chart out paths on understanding data and coding and demystify other concepts related to Data Science. Please leave a review down in the comments.

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Do connect with me on LinkedIn if you want to discuss it further!



Understanding how Data Science and Statistics work, sharing my knowledge, and showing you how it is used everywhere and how to harness its power. Simplifying Data Science for beginners and breaking down hard-to-understand concepts in a jargon-less way. Stay tuned for more!

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