BEST JOBS in Data Science

what are the best jobs in data science it turns out you aren't limited anymore to only the position of data scientists instead there are an additional four other jobs that are growing rapidly so i wanted to find out more about these jobs mainly what do they do and how does one get them what up dead nerds i'm luke a data analyst and my channel is all about tech and skills for data science and i find that for those aspiring to work in the field of data science there's a lot of confusion around what jobs are actually available in this field so i set out to interview those that work in all these different jobs in the field of data science to learn more about what they do i want to give a special shout out to coursera for putting me in touch with a lot of these interviewers but as a disclaimer carcera is not sponsoring this video nor did they have any other influence in the making of this video so with that let's meet the interviewees first up providing the data engineer perspective is roberto he works as a database architect in the education industry and is a recent chemical engineering major the primary tools he uses for his job are sql python and even some cloud services providing the data scientist perspective is ruchi she works as a data scientist in the technology industry she has an undergraduate degree in computer engineering and is currently working on her master's in information systems management primary tool of use is python and she's also learning r providing the machine learning engineer perspective is mo he works as a deep learning specialist in the it and services industry he has dual degrees in both mathematics and computer science and for machine learning he primarily uses python with tensorflow which he has a lot of different tutorials on coursera i'll be providing the data analyst perspective as i work in the chemical industry doing this and my primary tools of use are excel sql and also power bi and tableau last up is the data science manager perspective this will be provided by bernard he is a data strategist that works in the professional services industry he has both bachelor's and master's degree in both project management and marketing in addition to one of the most impressive things i've seen of 200 plus courses on coursera completed all right now let's get into what these interviewees do on a day-to-day basis for their jobs and to understand that we have to understand what a typical data science project entails the two major milestones when starting this project revolve around first collecting the data and then once you have that data actually diving into and analyzing that data so let's dive deeper into this first step of the process of collecting the data this is where a data engineer's role is so crucial for a data science project so let's hear roberto's perspective on this yes what i do in my job basically is etl processes which stands for extraction transform and load the extraction i mostly use as i said before a python to do web scrapping and then the transformation of the load i do it mostly in sql these etl processes of data engineers are used to create this data that are then later used by members of the data science team the big picture of the projects that we have is that we want to develop [Music] a big infrastructure of data you can use data to transform it into information and then use that information to make decisions uh with with visualizations so overall i would argue that based on the data engineer's job of starting that first step in the process of a data science project their jobs are the most crucial in this process one thing to note is that in smaller companies this task itself may actually be performed by data scientists or even data analysts next up is the process for analyzing the data and typically there's two questions for this what happened and why this happened hardcore data nerds would call this descriptive and diagnostic analytics data analysts perform this core function along with possibly data scientists which we'll eventually get to as a data analyst in my day job i would typically be given a problem and then access to some data in order to help solve that problem my job is to use tools such as excel and sql to understand and dive deeper into the data self itself sometimes i even would go as far as to using python from there the other half of my time would really be around communicating what insights i found via meetings and powerpoints and word docs sometimes i would need to take my analysis a step further and for this i would build dashboards in power bi or tableau in order to share this data with others that are maybe less data tech savvy and for them to go in and analyze and look at it further now for many projects analyzing the data may be the last step in the process but for those that have more advanced data science teams they may look further into actually predicting the data this involves looking at what will happen and also what action should we take those hardcore data nerds would call this predictive and prescriptive analytics machine learning engineers and sometimes even data scientists are used for this process to build and also implement machine learning models to solve this this is a newer journey that even mo had to discover when he started his journey back in 2012. you all know that machine learning engineer is a new uh like let's say job title in the market at that time i don't remember that the machine learning engineer was a trending job guidance so as he was discovering this he decided to focus more on deep learning the subset of artificial intelligence and machine learning because as you know that deep learning is the heart of ai and my opinion yeah deep learning now yes it's being in demand and it's solving very complex problems across all industries we then went further into understanding what mo does day to day regarding my role specifically i'm leading the assessment development team for kerala specimen development team is responsible of building competency models so for these models machine learning engineers are using a language like python to build and test models in order to answer that question of what will happen and what action should we take sometimes you can even find data scientists doing this type of job but when it gets to implementing models which i've tried myself as well and failed miserably it turns out that it's really difficult and you need professional like a machine learning engineer to do this so then where do data scientists fit into this process as previously hinted towards they really could be used at any stage within this process so they could be used for collecting analyzing and maybe even predicting the data so i'm sure many of you can relate to ruchi when she was first entering this field so if someone's starting out in the domain they might be just as lost as i was and they might be wondering okay how many courses should i take how many projects should i do which domain should i pick but like i said before it's just based on your interest like what do you want to do it's not what you have to do but what you want to do and just go where your interest takes you and then once you find the domain that interests you here are her recommended next steps so we really need to delve deep into the domain and understand how things work and what's required specific to a problem statement to do well there so in order to understand what a data scientist does day to day i think you have to understand what is the size of the data science team for a smaller team such as a startup they may be doing the job of that of a data engineer and also of that of a data analyst for larger teams where roles are more defined they may be doing more advanced analytics than a data analyst and maybe even guiding machine learning projects with machine learning engineers underneath them so we covered the four main roles that actually work directly with data but what happens if you're like bernard and you don't really even like to code or dig into the data sometimes you like stuff sometimes you don't like stuff just coding just all this line of code just i i look at them and i say yes not my stuff so i'm going to let this guy decide and create the stuff because it would be much better than me they're much smarter in this sense and i will just take care of where i'm good at so for larger companies you need someone like a data science manager to ensure everybody on the data science team is working towards that common goal and this is uh where i think uh data uh data manager we we will come would come these guys will just have a look at the company and say okay we need to build something we need to be uh so we know where we want to go and now we have to build everything so we will want to create like a data office these managers work as a liaison between the stakeholders and then the data engineers analysts and scientists themselves to actually go towards solving a problem so how do these jobs compare when we're talking about a salary overall i'm not a big fan of actually comparing salary or even picking a job based on being a higher salary i think that you should pick a job based on what your passion is and what you have an interest in for me as a data analyst i get more joy out of the tools i get to use and the analysis i get informed and the things that i find out and discover then my paycheck that i actually receive when looking for any of these roles it's important to understand that sometimes these names won't match exactly i'll find that companies will a lot of times call a role a data scientist when in fact when you look at the responsibilities it's in fact a data analyst or a data engineer or even a machine learning engineer so make sure you digs into the responsibilities and roles of that let's dispel this myth that you need a data science degree in order to land a job in this field as shown by these interviewees that's not the case i have found however that those with a stem degree typically have an easier transition into this field but what about those that have a non-stem degree i've also found success from these people as well after they've beefed up their knowledge on things like math and stats for those without a degree at all i'm a little pessimistic about job opportunities but with large companies such as google offering to accept things like the google data analytics certificate in place of a four-year degree i feel that change is coming towards this in the future now that we understand what these jobs do and what they entail let's look at what these interviewees did in order to land their jobs guarding certificates i think roberto showcases an excellent example of how you can capture value from these are you using your chemical engineering degree at all no no i don't using i don't use it that much well i actually learn everything i know about data and those things by courses actually and the most common theme that i found across all interviewees is this passion and desire for continuing education i'm a long life learner and i believe in that learning should be a continuous you can't just learn or take courses or learn for two or three years and then when you land a job and start working in any industry you should stop learning no and more specifically implementing what you learn for self-development you use your free time to learn new things you use your work time to improve to practice and improve because you need to practice otherwise it could be lost and then just like new knowledge and you will become like so much better but what if you don't have a job currently here's what ruchi did in order to implement the skills that she learned mainly it was me trying to work on projects that sparked my learning and interest and helped me to try and learn more over time so i would say that project based learning was something that helped me more and roberto even added this positive aspect of doing projects so i think it's really important that the people start learning stuff trying to do projects to develop uh more than their skills and i show up that they have some some experience and some to offer to the companies so what do you do with these projects are there any benefits besides just learning i start doing the google data analytics certification and something that it says that i think is really interesting or important uh is that you should make a portfolio and that's what roberto then did he gathered all those different projects he made put them into a portfolio websites and launched it and what happened next was pretty awesome it took me like two weeks to find a joke and and it was really interesting because i got more calls and offers in one week after i put that portfolio in my resume than the whole four months before that and it was i was shocked be because of that rishi however took a slightly different approach by showcasing her work through social media this actually helped her land and be selected to be an ambassador for z by hp for data science yeah i think it was because of my active involvement with the community and just contributions that are relevant to the domain it's more better to have people you form relations with on on linkedin or twitter where you have a community rather than just um applying to jobs that are available because most of the times you you might get an opportunity that you didn't know you wouldn't ruchi is highly active in the data science community on both kaggle and linkedin so what does her activity typically look like i just talk about my work that i'm currently doing so like today i published a data set about uh coursera's or global skills report and i wanted to wanted to see how that data translates and what analysis i can draw from that so what are some added benefits of this active involvement in the community well i think mo captures this perfectly i don't know if you would believe me for all my work that i'm doing i i didn't apply a resume like and nobody asked me for my resume for my educational background because they know that i'm a social media influencer i'm experienced in the field a lot of my let's say students or people that i mentored before they posted about me on linkedin about who is moriba and this is this is really amazing so overall what i hope you capture from this video is that it's not about having a specific certificate or a certain degree instead it's about becoming active in the community and sharing your passions and learnings with others whether that be a portfolio or via a social media outlet this has led to all the different interviewees including myself landing their dream jobs in data science as always if you got value out of this video smash that like button and with that i'll see you in the next one [Music]

Channel: Luke Barousse

Views: 91,608

Keywords: data viz by luke, business intelligence, data science, bi, computer science, data nerd, data analyst, data scientist, how to, data project, data analytics, day in the life, data engineer, data science manager, machine learning engineer, ml engineer, deep learning specialist, database architect, data specialist, what do they do

Id: VrdnBxx8BBI

Length: 15min 39sec (939 seconds)

Published: Wed Nov 03 2021