Welcome to Data Science & AI Weekly!
We are back with a new episode. Tune in to Episode #29 as Manav shares a weekly learning plan for Data Science.
TIME-STAMPED SHOW NOTES:
[00:03] Topic of Discussion: Weekly Data Science Learning Plan
[00:15] Welcome to Data Science & AI Weekly!
[00:38] Assumptions of the Learning Plan
[01:28] Weekend Learning Plan
[02:13] Weekday Learning Plan
[03:16] Breakdown of study schedule
[04:36] Time frame to master Data Science
[05:11] Episode Recap
[05:25] Wrap Up!
[05:35] Learn more about Data Science at www.accredian.com
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If you are planning to master Data Science or if you are right now in the middle of your Data Science learning journey, one of the questions that you would have is how many hours per week should I dedicate to Data Science?
Hi everyone, My name is Manav and in this video, I’ll help you understand your weekly learning plan for Data Science so that you are able to become a successful Data Scientist.
So let’s start with what your weekly schedule would look like look like?
Now, there are two assumptions here that I would make for making this video that you are in a full-time job because if you are a student, or if you’re not in a full-time job, you can virtually dedicate 24×7 to doing this.
Let’s say that you are in a full-time job. And let’s say that your full-time job keeps you sufficiently busy for 8-9 hours per day because there are jobs in which you can work from home or do other things.
But you have to go to the office, I’m assuming that. So these are two assumptions. Now given these assumptions, your study plan should be like this.
On the weekends, let’s say that you are attending a Data Science program, let’s say that you are attending Accredian’s GCD program or the GCDAI program, and you are attending classes for 2.5 hours on Saturday/ 2.5 hours on Sunday, which is a good enough time and you’re doing that in the morning.
So you will not have other weekly commitments beyond those 2.5 hours or 2 hours on Saturday/Sunday. What I would recommend you to do is to dedicate another 1-1.5 hours either immediately after the class if you think that the first half is where you can dedicate but after the class usually becomes a little tiring to directly get on to studies.
So if you have time, towards the afternoon, let’s say you take out another 1-2 hours, whatever you have done during the classes, try to go through that assignment. Go through the material all over again so that you are able to cement that on the weekends.
Now on the weekdays, even if you are dedicating 1-1.5 hours per day, that’s more than enough. Do not go overboard and try to learn for 3-4 hours per day because that’s not sustainable.
Your strategy in this should be you’re not doing it for a week or two weeks, you need to do it over a sustained period of months, and possibly for an entire year. And for that, the important thing is having a sustainable plan.
So one to 1.5 hours per day is fantastic. You can do it, let’s say in the evening nights 9-10:30. The good part about this learning however, as compared to studying for your college exams is that this is not an exam. This is learning so you will have fun, you will move at your own pace so nobody’s pushing you to learn everything because you have an exam coming up tomorrow, so you will enjoy that process more.
The second part you need to look at is the distribution of how what you’re doing. During that 1.5 hours, I would want that at least on 3-4 days you are getting your hands dirty in coding, the more you code, the more confident you will become especially in the first part.
After that, you should spend 30 to 40% of the time in coding; 30 to 40% of the time in maths, statistics, linear algebra, because that is also very important, you should make that transition after the first month.
Once you’re comfortable with coding and the rest 20-30% you should focus on learning about the applications of Data Science. Now the good part about learning about the applications of Data Science is that you actually don’t need to sit and dedicate 1.5 hours every day in the evening.
You can do it in the day also in your office while commuting. You can refer to websites, our website, great blogs that I’ve talked about in another episode. You can also download the Quora app on your mobile and start going through Data Science answers, they will start preparing you for the Data Science world in a much better way.
So 1-1.5 hours of effort is highly, highly recommended over a sustained period of time to master data analysis, it will take you 3-5 months, to master Machine Learning, it will take you another 3-5 months. So lump sum, it will take you 7-10 months to become a successful Data Scientist who will have not only mastered theory but would have a great portfolio of projects and that’s what has to be your goal.
So follow the study plan, if you face any difficulty; if you have any other time commitments, try to work around that and maintain that consistency.
That’s my recommendation to you. So, thank you very much for watching this episode. I hope you loved this. And if you did, let me know in the comments section.
