 Hello everyone and welcome to another episode of Code Emporium where we are gonna go through some of the most Googled questions on data science I currently have a couple of years of experience in data science And I thought that this is probably one of the best ways to help impart knowledge to either people who are looking for a job People who want to get to know more about the field people who are curious people who are already data scientists working Anybody but before we get started, please do make sure to hit that like button We also do have a discord server now, so the link is down in the description below Please do join us We have an amazing community going on talking about some fantastic things in the field of data science machine learning and artificial intelligence Also, please do ring that bell and subscribe for more videos like this and with that let's get back to the video Why is data science important? Data science is a combination of multiple inter-disciplinary fields including programming statistics probability and product management One of the best ways to make decisions about a product is typically with data Historical trends best dictate what we can do moving forward We can learn better from past mistakes using data And so running statistical tests also using machine learning models Which are based on historical data and sometimes data that we collect along an experiment Can really help frame what kind of decisions we make and what direction we want to take certain products in moving forward And for these reasons data positions have increasingly become important and many companies are also becoming more and more data driven as they understand This so when I type in data science alone The first thing that pops up is data science salary not really surprising at all because a lot of people do get into data science because The pay is enticing and they're not completely off I do know some people who have graduated college and immediately got jobs that are six figures But then there are also others who have not but gradually worked their way there, too a lot of this mostly hinges on location as well as What level of data scientists that you are if you go and search probably for median data science? Salaries you'll see probably it might be a little over six figures But that is also including positions that are From the entry level to a senior or even a staff level data scientist And their pay ranges can vary very heavily So you best want to be careful not to just expect too much from the get-go However, it is still a lucrative opportunity Do data scientists code? Yes. Yes, we do A lot of what we do is primarily in python and sequel Some people also use our data visualization is always spectacular There are also times that you do interact with software engineers So it would be helpful if you do understand some of their tech stack Mostly from the guys of they might be calling a service that you have built And you want to know how that interaction takes place so that eventually you can also participate and At least looking at their code kind of understanding it and also debugging it if required to Where is data science used data science is used in many fields in many domains From finance to education to just business to e-commerce. You name it It's prevailed in all of these industries a lot of this is because we can kind of abstract individual companies and individual sectors as to just solving different types of problems if we abstract it as Different types of problems that are being solved Then you can see how data science could just you know insert itself in these multiple sectors It's also kind of similar to how software engineering and data analysis also works You don't really need to be a super expert in a specific domain Just to be able to bit leverage the amount of data that they have Data is data and we solve problems with it in the end regardless of the sector How is data science related to computer science? A lot of people who get into data science do have a background in computer science That is they have like an undergraduate degree or a master's degree in computer science This is mostly because the nature of how you think and solve problems as a computer scientist can easily be transferred into data science And so you also see a lot of people who work as software engineers eventually transition into data scientists later on in their career Without too much of a hassle that said you can also still transition into data science from different career paths too There are many companies where you can have like a physics background and still get into data science Or a statisticians background and also get into the field Do data scientists work from home? Yes. Yes, we do Whether we like working from home is obviously subjective to individual opinion I remember that when I first started out working from home It was a little difficult mostly because you know as a data scientist that is of entry level You want to ask a bunch of questions and when you're in a workplace It's really easy to prod your mentor or your manager to just keep asking questions However, whenever you go back to like a work from home setting where you're completely isolated from the world Asking those questions becomes more and more difficult And so your learning curve also kind of starts to plateau and it becomes difficult to learn faster However, as and when you start gaining experience It does get a lot easier because you are at a stage where you have some foundational knowledge And you don't need to rely on somebody to learn little things You can pick them up yourself and you eventually start learning to learn and that way you can grow independently For the most part while still maybe just still using your manager or your mentor for advice Why is data science so popular? A lot of it again is because of the salary But then there's also a good chunk of it that is because of how interdisciplinary the field is It's really welcoming to people with diverse backgrounds. You don't need just a computer science background You don't need just a finance background. Most people are welcome in the field Because any prior knowledge that you have in solving problems in general can only help you as a data scientist And that's probably one of the primary reasons why the field has been exploding over the last few years too There is also so many people who are willing to instruct and so resources on data science are becoming more available to people So it's probably why data science is becoming popular. How is data science different from statistics? Statistics is a branch of mathematics that is used in data science, but data science itself Is a big umbrella that encompasses statistics probability software engineering slash computer science data analysis machine learning and so many other fields Statistics on its own is a great foundation specifically. We use it in data science for conducting like ab tests more popularly But statistics on its own is also the foundation for so many other fields too aside from data science And so if you were to describe both of these fields, you can say that they intersect with each other But they also have their own use cases Do data scientists need a master's degree? No data scientists do not need a master's degree. In fact, I know a few people who have just graduated from an undergraduate degree And they have directly gone to become data scientists and companies There's also some cases like I mentioned before where you have an undergraduate in computer science, for example You go become a software engineer and then from there you can transition into data science And that can be extended to not just software engineering, but also any other major like you can go to college get a degree Work in the industry for a while work on solving problems And then you can transition into data science because again data science is about solving problems And most domains especially in stem do have a background of solving problems. So that can only help you as experience How is data science used in healthcare? So this is kind of one of the fields where it's a little shaky to just thrust data and data science at a particular problem When we are making decisions, we would always typically want to Create let's say do an analysis create some model and then Choose the approach or model that gives us the best outcome and performance based on some sort of performance metric But when you're dealing with healthcare Numbers are not just numbers. Numbers could be a matter of lives too So even if you get a couple of cases wrong with your machine learning model, it could be the matter of life And because of this reason too It is very hard for autonomous machines to just take over the healthcare system And the best way that we could deal with this at least in modern times Is if we do have a medical professional on the field We could have probably an ai system assist them in some way Maybe just to make their work easier rather than completely supplant them like how many movies slash Frenzy of media portrayed to be doctors are too important to be losing their jobs anytime soon Do data scientists use excel? I personally have not used excel to a very large extent I only use it when i'm interacting with certain stakeholders who use excel and who like getting their data into an excel spreadsheet And so I would basically take my pandas data frame and probably export it into a sheet and maybe format it a little bit So that it's easier to look at but excel is still a pretty useful tool to to know at least a little bit However, there is not really much that I can do with excel that I cannot do with like pandas python and map plot live How is data science related to machine learning? Well machine learning is kind of the the brainchild of what everybody thinks data science is all about Although we would like to be modeling all the time We do not spend all of our day with machine learning and just modeling Data science is an umbrella like I mentioned before where one facet of it is machine learning But then there's so many other parts to it as well Which regard like data analysis processing data in general and understanding what the data is about But overall machine learning is definitely an integral part of the data science process Do data scientists use sequel? Yes, data scientists do use sequel And in fact, it's probably the bread and butter of how we wrangle data Apart from of course the python library pandas sequel becomes very important during your interview process But it becomes almost irreplaceable when you're actually working in the industry because you'll most likely be Doing some form of sequel processing on the daily Where is data science going? That's a good question and that's going places because right now the way I see it holistically The industry as a whole is that many companies are actually not very data centric And what I mean by that is they don't necessarily make decisions just based on what data that they are presented If they were to make decisions based on just data presented That would make our life as a data scientist pretty easy Because all we would need to do is basically our job And we would almost guarantee that anything that we say would be pushed forward because we can always make a case for it And let's say a stakeholder would look at our case and be like, oh right It does make it does make sense because we are losing money here So why not use your approach would suggest that we'll use less money However, this is not really the fact for most industrial cases. In fact, many companies are not data driven first So they are mostly driven by certain either product decisions or other more personal decisions within the company And there are chances where you as a data scientist would be doing a lot of work You can present a case, but yet it can still be shut down One of the primary reasons for this could be that a lot of Stakeholders and a lot of executives a lot of people in general just are not 100% sure What data science could do for their company and they're also not 100% sure whether it's worth the time and the effort to spend in order to figure out whether you know We would be making money in a way However, as more and more people do become open to data science techniques and data science methodology It also allows us data scientists to become able to make decisions and also help push certain decisions forward And also act as consultants Which could only help the company in the long term But there is always some initial investment that is required And a lot of the a lot of the big problems that happen these days is like A lot of people who get into the field May not know 100% of what they're doing and I feel like that is unfortunately a perception that Many others also have many other non technical and non data scientists have But that's something that We should try to rectify moving forward too So I'm hoping that we will move in that direction of more data driven decisions And also more confidence in data scientists working in your companies And that's all we have for today. Thank you all so much for watching And I hope these questions actually gave you some more insight into How data scientists work what they do What their life is like and some extents and some of your questions have been answered here But so many more may have been formed too. Please do write those questions down in the description below I am going to be making this a series Probably on data science also a little bit on sequel a little bit on programming and many others So you get a holistic view of how Um data science kind of helps in all of these fields Again, thank you all so much for watching Please do give this video a like and also down to the description below We have the discord server So please do join that server and I will see you very soon with another amazing video. So stay tuned. Bye. Bye