How to master Data Science in 30 days? | Ep #07

How to master Data Science in 30 days?

Here’s a brand new Episode #7 of Data Science and AI Weekly! Tune into this episode to learn how to master Data Science in just 30 days! The podcast is hosted by Manav, Chief Data Science Mentor at International School of AI and Data Science.

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[00:05] Topic of Discussion: How to master Data Science in 30 days?
[00:26] Podcast Series Introduction
[00:52] Ideal Time frame to master Data Science
[01:32] Data Science 30 Day Study Plan
[01:57] Study materials for Week 1
[03:32] Study Plan Week 2
[04:36] Study Plan Week 3 
[06:13] Study Plan Week 3 
[06:52] Data Science Fundamentals you need to learn
[07:19] Episode Recap
[07:45] Wrap Up!
[08:10] Learn more about Data Science at

Have you been thinking about mastering Data Science and you don’t know where to start from?

Hi, everyone, welcome to Episode #7 of Data Science and AI Weekly. In this episode, we have a very interesting topic that would be relevant for you. That is how to master Data Science in 30 days. Right, let’s get started with this episode. If you have not subscribed to this podcast, just go to the subscribe button. And if you want to other watch other videos in this podcast, just go to the playlist again, it is there in the description and you can watch the rest of the videos.

So let’s get started with Episode 7, which is how to master Data Science in 30 days?

So the first question is that you must be wondering Is that Is it first of all even possible to master Data Science in 30 days? And the answer clearly is No. It is not possible to master Data Science in 30 days, because Data Science in itself is such a vast field. It takes months if not years, to my It. And even the best of the experts can’t claim themselves to be masters in Data Science.

And that’s why when you are looking to get into Data Science, what is very important is you need to know or you need to have clarity about what are the important parts that you need to master to get into a Data Science job and then you need to start accordingly. And this brings me to the question that if you were to master Data Science in 30 days, and if somebody were to ask me, that man of how would you master Data Science in 30 days, here would be my study plan, and here is how I would do it.

As I said, it does not happen in one month. But the goal here is just to imagine that if you were to do that, how would you do that? Right? So I would divide my 30 days into four weeks, it would be a 4-week plan.

In first week, what I would do is, I would first of all, I mean assuming that the premise for this is that you as a beginner do not know anything about math do not know a lot about programming, you know, a little bit of programming a little bit of math, but you do not know to the extent that is required in Data Science. So in the first week, what I would do as a newbie is I would get my Python fundamentals really strong.

That’s the first thing that I would do, I would ensure that I’m getting my hands dirty in the basic Python code. So this I would possibly do for the first three to four days. The second thing I would do in the next 2-3 days is to get my statistics concepts very, very clear. Right. So the basic statistical concepts is something that will help you in the later part of Data Science and in the last 3 weeks journey will become much easier if you if you would have mastered the first two steps which is learning Python and learning statistics, basics and doing this should be possible in one week, assuming that you are doing full time and since this is the premise that here we are taking a hypothetical situation.

So I’m assuming that you are devoting, or full-day to mastering this, because mastering Data Science in 30 days while having a job is next to impossible, so I’m assuming that let’s say that you have a block of 30 days to devote to this. The second thing you would do the second step, step one is focused on Maths Institute and Python in week 1, week 2 would be all about getting your hands dirty in the Data Analysis part. So in Data Analysis you have you need to be very comfortable you need to master Pandas, well Pandas is used for Data Transformation, it should take you 1-2 days, you should spend with NumPy and then one to two days you should spend in a data visualization packages like Matplotlib, Seaborne book, etc.

And then the rest 2-3 days, what you should do is bring all of these packages together into real-world Data Science projects, and implement all of these packages in a data set that you could possibly take from any source like, UC Irvine data sets are pretty good. You can also pick up Kaggle data sets and do just a basic level of data analysis so that you are comfortable with the with everything that you have learned. So that is week 2. So week 1 was about mastering the fundamentals of Python and statistics. Week 2 is all about data analysis that I’ve just discussed.

Week 3, I would move ahead and I would move to do Machine Learning. And in Machine Learning, what I would do is I would not bother about learning 20-30 different Machine Learning algorithms. What I would do is I would master 5-6 Machine Learning algorithms that are the building blocks of Machine Learning that you need to know for data science.

For example, I would spend one day understanding the very basics of Machine Learning how is it applied, etc, I would spend Day 2 in Linear Regression, I would understand the theory behind Linear Regression, the math behind Linear Regression, the assumptions of Linear Regression, and I would also implement Linear Regression in a project. And I would also see how Linear Regression is used from Scikit Learn library of Python.

So that’s how I will spend Day 2 of the third week, next day, Day 3 of Week 3, I could potentially learn logistic regression day or I can learn Decision Trees, day five, I can learn random forest and day six I can spend on basically learning how to evaluate Machine Learning models, since there are multiple models that can be used to solve a particular business problem. So I want to as a Data Scientists know which is the best model for me and that’s what I would do in week 3, right? So as I said, you would not want to do too many algorithms. But you could take a handful of supervised Machine Learning algorithms and unsupervised Machine Learning algorithm so that you know, the basics week 4, I would continue my journey into Machine Learning by learning some more unsupervised algorithms, right on which I can spend two to three days. And the rest 3 days what I would do if I have to master Data Science, and at least call myself to be reasonably skilled in Data Sciences, I would implement all of this in more data sets from different industries.

For example, I could pick up a credit card fraud detection data set, I could pick up a telecom data set, I could pick up a simple data set that could help you implement some of these Machine Learning algorithms in real-world scenario, right.

So this is the four-week plan that you should follow if you are on a crash course, so to speak, to master Data Science, 30 days, should be good enough to master the fundamentals. But this is with the assumption that you are doing this for on a full-time basis and you don’t have the luxury of spending more time which I would highly recommend. So this is the four-week plan to master Data Science from scratch week 1.

Just to recap everything week 1, Master Python, statistics to focus on data analysis and exploratory data analysis. Week 3 master Machine Learning algorithms, especially the supervised week for half of the week, I would devote to unsupervised Machine Learning algorithms and the rest half I would spend on working on more projects, right, so this is the 30-day plan of mastering Data Science from scratch.

Let me know what do you think about this plan and leave your comments. I hope you would have loved this episode. Our goal through this a Data Science & AI Weekly series is to help you master Data Science in a step-by-step way If you want us to cover a particular topic in an upcoming episode, leave your comment or the topic that you want me to talk about and I will short for sure pick up the topic very soon.

Thank you very much for tuning in to Episode 7 of Data Science & AI weekly. My name is Manav. I’m signing off and look forward to seeing you in Episode 8.

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