Fascinated by the word “Data”? Confused with numerous career field related to “Data”?
The fact that Google alone on an average have more than 20 petabytes i.e 20971520 gigabytes 🤯 of data stored in its data centres shows how trendy and vast the field of data is in the 21st century. One can now estimate how demanding the field is considering we have some major players like Amazon, Facebook, etc along with Google in market craving for data.
Gathering, Processing, Analyzing, Implementing Machine Learning Models, Testing, Optimizing and Integrating the model with this huge amount of data is not the “one-man job” right? The job roles are classified into 4 fields i.e Data Analytics, Data Engineer, Data Scientist, Machine Learning Engineer. The main motto of this article is to help people identify where they fit in the hierarchy based on their skills, so they outsmart themselves before applying for jobs. Each job role with required skills, payout, demand, etc will be discussed comparatively.
| Data Analyst :
“The guy drawing conclusion and making interpretation from the data.” A Data Analyst must possess knowledge of different statistical tools and representation techniques (so that people not knowing the analytic technique can interpret the result too).
To understand more clearly what a data analyst does, assume you are the owner of a big manufacturing unit dealing in different kinds of bags. One day you observed that not all bags produced are sold now as you are business-minded person you get curious to know which bags to manufacture more for which age group to maximize your gains, you hire a guy with analytical knowledge so that he can help you identify the right product to be manufactured here comes the role of data analyst.
| Data Engineer / Architect :
“The guy working as the backbone of Data Science and Machine Learning.” Data Engineer works at the infrastructural management(Data Warehouse) of data. The main job of a data engineer is to collect, store and transform the data in a format it can be used in analysis and machine learning models. He works with different databases and APIs for modelling of data.
Let’s continue with the above example of bag manufacturing unit to understand the role of a data engineer. The data analyst you hire found it hard to analyze the fact that your data is scattered and is not uniformed. He asked you to hire a data engineer who will help you out with the proper management of your precious resource i.e your data. The data engineer helps in giving the right shape to your data.
| Data Scientist :
“The master of all fields.” The data scientist can work right from data transformation to the machine learning model and their optimization. A Data Scientist is expected to have some knowledge of front-end too so that he can integrate the model for front-end use.
Continuing the above example, after the proper analysis of data. Now you want to know what changes can be made in product or marketing to target the right customer and increase your revenue ?? You went to hire a data scientist he applies various algorithms on your data and gives you a detailed overview of how the following changes will boost your income.
| Machine Learning Engineer :
“The face of the data scientist.” This guy fills the gap between machine learning models and front-end-interface. They integrate the machine learning model as the data scientist have nil (or little bit) knowledge working in front-end. Machine Learning Engineers are expected to possess knowledge of both Data Scientist and Front End Developer.
In the above example, now the bag manufacture has enough information about which product is most likely to be taken by a specific consumer. So he then wants to show the most likely product to the targeted customer, he goes to a machine learning engineer shows him the model prepared by data scientist and asks him to integrate it into the website. Here comes the role of ML engineer, he works with model and front-end team to integrate the recommender algorithm into the website of bag manufacturer.
| Comparative Analysis of Different Skills required for each job roles
While all the job roles require the same basic skill set. Whereas mastery in specific skill sets edges you to get your desired job role at the desired position. Below is the skill set required for job roles based on their importance:
| Demand For Data Science Jobs
According to the article written by Louis Columbus in 2017, the demand for Data Science and Machine Learning Engineer job roles are more than any other posting and is expected to outperform in the upcoming years. Another article written in the same year shows that Data Scientist Job roles will soar by 28% in the year 2020 based on the IBM Prediction. In the year 2020, we ourselves can see the tremendous demand that tech companies are putting forward for the job roles in these fields. The quote “Data is the new gold” seems to be coming true considering the rate at which data centres are expanding, and with these rate of expansion, one can estimate the amount of workforce required in management and configuration of these centres.
| Average Salaries based on different job roles
Though the actual salaries depend on various factors like demand in your location, the company in which you are working, your skillset and experience level you possess, etc. But these figures are enough to motivate you to have a kick start career in the field of data.
| Conclusion
A brief overview of job roles classified based on skillset:
The demand for data is ever-growing with each passing day, instead of being driven out the payout offer for the job roles choose a field which interests you more. In the long term for your career, the satisfaction of work will matter more than your salary.
Good Luck!
