Entry Level Data Science Jobs: Which Data Science Role to Choose – When I started applying for data science jobs, these are some of the points that I kept in mind while building my resume. The first and foremost thing is to list down all the projects where you have actually played a major role. Hi everyone, I am Abinaya Mahendiran. I work as a data scientist in Mphasis NextLabs. In today’s article, we will be looking at entry level data science roles and the salaries associated with them and also will have some tips on how to build a profile to get into such roles.
Since we are discussing entry level data science jobs, these are the prerequisites that a person must possess. The first and foremost thing is a person should know at least one of the programming languages like Python or R. So if you are someone coming from a software engineering background, it is easy for you to pick up a programming language like Python or a tool like R. If you are from a non-programming background, still, you will be able to pick up those skills easily. Apart from being proficient in a programming language, it is also essential to have some mathematical skills. So a person should be good at probability, statistics, linear algebra and vector calculus.
So these are some basic skills that are essential for any kind of data science roles that are available in the market. There are several roles that are available in the data science domain that one can pick up, let’s discuss them one by one. The first one that we wanted to discuss is the data engineer role. A data engineer is someone who is responsible for setting up the pipeline which helps in the ingestion of data, preparation of data, cleaning of data so that that is usable by a data scientist or a data analyst at the end.
He is the one setting up the infrastructure and the pipeline wherein the data will be ingested and it will be converted to the right format. If you are someone from a software engineering background this role might be better one for you because you will have to learn available frameworks like Hadoop, Spark or you should be good at some querying languages like NoSQL, SQL etc. So this might be a very good place to transition into data science for a software engineer.
The next role that I want to discuss about is that of a data analyst. Data analyst is someone who can actually tell stories with data. So he is responsible for dash-boarding and kind of giving the insights that he gets from that data to the business people. So what kind of skills are needed for this role is that you should be good at statistics, you should have a. bit of programming skills either in python or R and you should also no some dash-boarding tools like Tableau, clickview etc.
So for someone who is from non-programming background but has good presentation skills or statistical knowledge this might be a better role to transition into. The most sought after role in the industry right now is that of a data scientist. A data scientist is someone who actually understand the business problem. He or she also interprets the data that is available, finds patterns and provides solution to the problem at hand. So a data scientist will take the data from a data engineer, does a lot of analysis on the data, derives insights and he/she builds the model to kind of solve the business problem at hand. A data scientist must be proficient in any of the programming languages like Python or R. Apart from that they should have a strong mathematical background like the ones that we have discussed in the beginning of the article.
So these skills are very essential, apart from these skills they should also have domain-specific knowledge. For example, if you are a computer engineering graduate and you want to get into data science say in a specific domain like the energy sector, you should understand that domain well and you can apply all the skills there. If you are a non-CS student and you want to get into data science all you have to do is learn programming and the mathematical skills that we have discussed earlier and you can apply that in your own domain. So this way everyone gets to learn something to become a data scientist.
The next role that we are going to see is about machine learning engineer. Machine learning engineer is someone who takes all the models that are developed by the data scientist to production. So they take the model, the containerise it, they deploy the model in production. They maintain the code and they also make sure that the performance is optimised and the solution is scalable. These people are responsible for taking the solution to the end-user. So machine learning engineer role is suitable for someone who has strong software engineering skills because they just have to pick up certain frameworks or certain tools and they will be able to transition into this role very easily.
When it comes to salary, data analysts make around 5L/Annum on an average. Data engineers and machine learning engineers are almost paid the same. So they can get around 8-9L/Annum whereas data scientist at an entry level can fetch you up to 10L/Annum on average. Now that we have seen the various data science entry level jobs that are available, let’s have a look at how one can transition into those jobs. So I would like to share my experience of becoming a data scientist from being a senior software engineer.
My journey started in 2011 when I took up Andrew Young’s machine learning course when they rolled it out for the public for the first time. After completing the course I decided that I wanted to do my masters, so I got into IIIT Bangalore and I made sure my course work is aligned to machine learning, deep learning and NLP. So I have even done a thesis during my course work. So after completing my masters, I did not
land up in a data science job immediately, instead, I took up a senior software engineer role at CISCO.
At CISCO I was able to work on various projects that developed my software engineering skills. After gaining enough exposure I really wanted to change my domain to data science. But then the issue I had was I did not have enough hands-on experience in the data science domain. That was one of the prerequisites the industry expects you to have. Because I did not have an internship during my course work, I couldn’t show anything in the data science domain in my resume. So the thing that I did was to participate in various hackathons as well as Kaggle competitions that way I was able to learn the necessary data science skills, as well as I, could add those things in my resume. Once I was able to do that, I started applying to data science jobs and I got a job at Mphasis NextLabs. So this was my journey into data science.
When I started applying for data science jobs, these are some of the points that I kept in mind while building my resume. The first and foremost things are to list down all the projects where you have actually played a major role. For example, if you have academic projects or even your work-related projects where you have taken the major part of the work those projects should be listed. Also, make sure you describe it in just one or two lines because you should have the space to discuss about the project in detail during the interview.
So it is always good to have a very brief description of your project. If suppose you do not have any academic projects or industry-related projects, it is always good to participate in hackathons or in Kaggle competitions and list those projects as your machine learning or data science projects on your resume. This will show the interviewer that you actually took your time out to learn something related to data science and you have built your resume on top of it. This will give a better impression about your resume.
Apart from listing down your projects on your resume, you can also have sections like an area of interest. For example, if you worked on a variety of computer vision problems or an NLP problem you can list those domains as your area of interest in the resume, also you can add the libraries, the tools, the frameworks that you have worked on in a different section. Most of the time your resume length should not exceed 2 pages. It’s better to have it either as a one-page resume or a two-page resume based on what jobs you are applying to.
The interview process for any data science or machine learning job might contain the following stages. Initially, you might be given a coding challenge to understand your programming aptitude, your data structure and algorithms knowledge. There might be few face to face rounds wherein you will be asked to describe about the projects that you have worked on, what techniques you have used in those projects, what is the problem that you are trying to solve and also basic machine learning concepts. The next round might be a take-home assignment or a live coding challenge wherein you will be given a data set and a problem statement and you will be expected to code everything from end to end, as in the data analysis part, data visualisation part, the modelling as well as coming up with the right metrics to improve the business.
These are some of the rounds that are included in any data science or machine learning interview process. In order to do well in these interview processes you should always make sure that your fundamentals are strong. For example, you have to brush up your data structure and algorithms knowledge as well as the mathematical concepts behind all the machine learning algorithms. So if you are able to do that then it will be easy for you to crack these interviews.
Also make sure whatever you write in your resume is something you are aware of. So you will be interviewed mostly on what you have already done. So if you are clear on your concepts as well as the things that you have done on your projects you should be able to crack these interviews easily. Now that we have some clarity on different entry level data science jobs, let’s talk about some of the myths that people generally have about data science. Most of the people think that data science is all about tools, that’s not actually true.
We use tools whenever it is necessary but then data science is not just tools. You will have to do a lot of things apart from that by coding certain things, by analysing certain things, you will be using your programming skills as well. This takes me to the question where people keep asking, coding is a necessary skill to become a data scientist. Of course, you need to do at least a basic level of programming in your day to day job. So it is advisable to learn programming languages like Python or R in order to become a data scientist.
You should also know the tools as well as a programming language in order to successfully become a data scientist. People often mistake a data scientist to be a data analyst and vice versa. That’s because both the roles have some common functionalities but a data scientist is someone who is a data analyst but he does more than what a data analyst would do. For example, he does not just visualisation, he actually understands the problem, analyses the data, builds the model and then he derives insights but a data analyst will be just analysing the data and he will be giving the insights only. He will not actually be doing the modelling and other things related to a data scientist role.
People also think that data scientists are geeks and they can predict the future. Well, I would say it all depends because data scientists need not necessarily be geeks but then they are very passionate about understanding what data insights they can derive and also they are interested in solving business use cases so people often consider them as geeks.
People often say that data scientists can predict the future, that all depends on the problem statement that they are working on. For example, if you are working on predicting a stock market price prediction so in that scenario given the data is available with you and you know how to model those things, of course, you will be able to come up with a prediction how the prices will be on the next day. This is what people mean when they say data scientists can predict the future. so it is on very subjective to what problem they are working on.
My advice to aspiring learners would be to get their basics right. We have discussed about a lot of skills that are necessary to get into data science jobs earlier in this article so you have to kind of improve your programming skills, improve your mathematical skills. Apart from all that you should also keep yourself updated on what is happening in the industry. The best way to do that would be to follow a lot of twitter handles or posts or blogs wherein people discuss a lot about the research happening in the industry. So these are some of the ways which you can keep learning. This will enhance your data science skills, make it easier for you to transition into any data science roles.
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