Data Scientist Job Description explained by Brillio Data Scientist

hi everybody my name is Indra Krishnamurthy I'm a senior data scientist with Giulia from the past one and a half years I'm also a mentor at springboard I have about 10 years of experience in the IT industry of which the last five years have been in data science currently I'm working on a couple of interesting models one is a recommendation engine using a flavor of war two vet called Proctor wek there's also a multi-class a text classification problem that I'm working on coming to the agenda of this video I want to talk about why data science is such a big deal today what are the roles around data and the descriptions of each of this role plus what it takes for you to become a data scientist they say that it is an awesome time to enter the job market as a data scientist today as per job portals like dyes and indeed there has been overwhelming 400% increase in the demand for data scientists from 2013 and as much as 29 percent year-over-year why is that why is data science commanding such attention at this point of time there are two main reasons for this one organizations pileup humongous amount of data on a day to day basis this data might come from website from social media from legacy systems from IOT but these this data is actually useless as long as it has not been seen through the eyes of a data scientist data scientists make sense out of this data which adds value to the organization which brings about sale and growth for the organization point number two though there is such a huge demand for data scientist there is a there's a complete shortage of skilled data science professionals today there's a huge huge demand supply gap and that is one of the biggest reason why there is a demand if you look at any job portal today you will find a deluge of job roles surrounding the word data data analyst data scientist data engineer data architect data manager statistician machine learning angel engineer the list goes on they all sound same right but they're not the same they are they are interrelated they are interdependent but they do have a soft margin and the skill sets for each differs let me try explaining to you the top four of these roles and the skill sets that are required for you to become one let's begin with the data engineer now we need data in the first place for all these roles to exist the data in jr. is a professional who prepares this data for the rest of the team to use now he goes and fetches or extract the data from different sources integrates them and prepares the data to to present it in a consistent format which is then used by the data scientist and the data analyst in the team now data engineers and data architects work in tandem with each other the data architect is the guy who will conceptualize will visualize and build the enterprise data framework now he is the guy who actually gives the blueprint the plan how a data should flow right from the beginning till the end and the data engineer goes by this plan now the skills that are required by data engineers and data architects are say pig hive hadoop mapreduce sequel no is no sequel I mean a lot of thing a lot of these things require squaring of the data and pig violence QL all these are actually querying languages now over years and years of experience in data management and data storage here so this actually leads a data engineer to a data architect role so I would like to talk about data analyst next data analyst is like the Sherlock Holmes of the team you can imagine him using a magnifying glass looking into the data literally finding out patterns in the data summarizing them and then present some kind of dashboards are reports to the audience now what is not expected out of a data analyst is building new algorithms or working on real huge data he is expected to know some form of querying R and Python programming as well as tableau or power bi for the dashboarding purposes now moving on to the next role is that of a data scientist data scientist is like an alchemist in the team if you call a data analyst a detective or a spy I would call a data scientist as an alchemist who turns raw data into gold gold in the sense valuable insights who solves critical business problems for thee for the client or the business so a data scientist basically uses statistics machine learning and analytical skills in order to convert whatever raw data that has been prepared by your data engineers and data architects into something that can be consumed a lot of mathematical modeling algorithms strong programming skills in our Python all that comes into play when you have to become a successful data scientist I hope that clears the air about the roles you know that are there in the data sciences field one question that regularly pops up in front of me when I meet people is that how is the transition from a non data science role to that of a data scientist that I am today now trust me it's it's not Herculean task at the same time it's not a cakewalk it's not an overnight thing where today you are a software engineer and tomorrow you're a data scientist it's not even a three-month thingy you become a data scientist by thinking like a data scientist on a day to day basis and believe me it involves learning each and every day I would like to list down a few hard skills and soft skills in order to in order for you to take up a data scientist role the first in the foremost one that I can think is maths for you to become a strong data scientist your foundation in maths and statistics have to be good you you you possibly have to go back to your high school and 11th grade 12th grade maths I'm talking about matrices and calculus and things like that get strong in them in order to appreciate the beauty behind the algorithms that you would be applying in the future to you know build your models the second one is a very strong programming very strong programming skill the most used languages today are Python and are I would personally prefer Python I moved from R to Python if you're not a great programmer in the day one make sure that you get better in terms of how your code scales over the time the third thing that I would emphasize is again related to scaling some some knowledge on Big Data on how you could apply your algorithms or how you could make it perform well when you apply the same thing on data at scale now that is very important when talking about soft skills data scientists is expected to be good at narrating eloquently he's he's supposed to be a good he or she is supposed to be good storyteller in order to convince what's happening you know you know to the clients and to the business so these are the hard skills and the soft skills that I would I would say is a must for you to become a successful data scientist one concluding piece of advice that I would like to give to all you aspiring data scientists is that you are at the right place at the very right time don't have doubts about this this field is challenging it is interesting when you get up in the morning and you go for your work you will find it challenging you'll find it inspiring to go try out new new stuff now when you're a data scientist you are a student every single day you have to learn new stuffs you cannot keep doing this thing again and again and that's what is the most interesting thing about being a data scientist all the best to all of you thank you [Music]

Channel: Springboard India

Views: 58,307

Keywords: Data Scientist, Data Scientist Job Description, data scientist salary, Data Scientist Jobs, Data Scientist resume, Data Scientist Qualifications, Data, Online Learning, E Learning, Brillio, Artificial Intelligence, Machine Learning, Data Science Tools, Python, Programming, R Language, data science tutorial, What is Data Science, India, Data Science Bootcamp, Insights Data Science

Id: 4mVcZWm3pd0

Length: 9min 9sec (549 seconds)

Published: Thu Oct 10 2019