7 Job Options After Learning Python | Python Career Opportunities

Key Takeaways

python has become one of the most popular programming languages in the world today and learning python can open so many career opportunities for you in today's video I'm going to talk about seven different career paths that you can pursue once you know python towards the end I will mention one career role which can pay you the most amount of money number one is python back and engineer I myself worked at Bloomberg for more than 10 years as a python backend and data engineer and we were using python in one of the three ways number one is is writing a python backend or a web server that can serve request coming from some kind of front end so at Bloomberg we had this financial application which will be a website or a mobile app and that will call a backend to get the requireed data or to perform some computation and this back end will be interfacing with some database let's say mongod DB or my SQL Etc and for running a business logic you might be using along with the basic python concept you might be using numpy Panda scipi there are tons of python modules available so you might be using one of those modules and then that python code will be wrapped in a web server now for web server there are three popular python Frameworks such as fast API flask and Jango so you'll be using these Frameworks writing a back end and that back end could be serving request to UI or another back end the Second Use case is to write a back end that performs some kind of computation at Bloomberg for example we used to get this financial data from stock exchanges so let's say you get a flat file which is just a text file where you have company's price and earnings information now your goal is to extract information from this file and store it in a database so you are not only storing price and earnings but you might be Computing derived field such as price to earning ratio which is just a division of price and earning so you would be doing data extraction data computation deriving New Field all of this thing will be done in a python backend now this python backend can be triggered on Unix using a Chrome tab Chrome tab is more like Windows task scheduler where you can say okay every day at 5:00 or every 1 hour kick off this python job and it will go to some FTP server secured FTP server grab all the files you know extract the information do computation and store it in some database the third use case was automating small task using python script so we had variety of small task that we needed to automate it and we would use Python for that python backend engineers get paid really high the pay scale keeps on changing that's why I will uh suggest you few websites which you can check to get an idea on the latest pay scale for python backend engineers and these websites are payscale.com glasso la. fii and team blind if you go to LinkedIn uh jobs and search for python backend developer you'll find tons of jobs and these jobs will list all the required skills along with python so let's say if you're python backend engineer or a fullstack engineer along with python you need to know other skills as well so if you're thinking if this is the right role for you then the question you need to ask yourself is do you love coding because as a python backck and engineer you will be doing lot of coding okay so you need to be loving the coding as well as software engineering in general so if you love both of these things then python backend engineer could be a right career for you the second career role is data engineer now let me tell you why this role is very important for organizations nowadays in last few years you all know there was a hype of machine learning every companies and every manager wants to do machine learning so that they can prove to their boss that their group is working on something very cool for that reason they started hiring data scientist data scientist started building machine learning models but when they deployed these model to production these models failed horribly and the reason was machine learning is all about garbage in and garbage out so if you're training your model on a wrong type of data it will not perform well companies did not have right amount of data that they needed for ML and also this data had lot of Errors so then they started hiring data Engineers now data Engineers role is to make sure they are capturing right am amount of data they're setting data pipelines and they are building data warehouses where there's a good quality data available that can be used by both data analyst and data scientist for analytical as well as machine learning purposes data Engineers main skills are in terms of programming language it is either Python and Scala in terms of tools they could be using Apache airflow or Apache spark variety of databases SQL and so on and in terms of cloud offering they could be using Google big query Amazon red shift snowflake stream sets Etc data Engineers when they write individual components in in their data pipelines they will be using Python and Scala in Python also they might be using pandas numai Etc modules uh they could be using Apache spark as well they also need to be very good in SQL and distributed computing scalable art chitecture and so on so if you're wondering if you should P pursue a role of data engineer or not you need to ask yourself do you love coding first of all coding is a must second thing Cloud tools knowledge such as snowflake red shift Etc is also required and the third thing is General software engineering and data infrastructure principle where you figure out how you can scale your databases you know how you can use a performant architecture so you need to be very good in software engineering in general for this role once again you can go to LinkedIn jobs and scroll through all data engineering jobs and figure out what kind of skills the companies are asking for this particular role by the way on code basic. I have a nice python course available at a very affordable price and we have implemented endtoend project in healthcare domain this course is perfect for beginner as well as advanced users the link of that course is in video description below the next rule is RPA developer RPA stands for robotic process automation there are companies where they have business processes where you need to download let's say data from certain website or a software then in the Excel file you are creating few more Excel columns for doing data crunching and traditionally this was done manually by humans RPA automates this manual task so you are writing RPA board which will automate this manual boarding process and for this purpose you might be using one of the RPS software such as automation anywhere or a UI path now if you are someone who do not like coding very much then this role could be a nice fit for you because most of the time you're doing drag and drop in tools such as automation anywhere in UI path and sometimes to automate some small data extraction task you have to use Python so you might need some python scripting knowledge some programming knowledge but it's not not like you're using python all the time so once again if you don't like coding too much then this could be a right role for you you can explore LinkedIn jobs for RPA developer roles to see what other skills uh they look for RPA developers do not get paid as high as other roles so there is a trade-off you're doing less coding but then you're getting less pay as well the next rule is devops Engineers many companies have this devop infrastructure which is maintained either by full stake engineers or they have a separate devops engineer role now in this role once again for automating manual task related to your devops engineering you might be writing Python scripts once again check LinkedIn jobs for various skills which are required for this role now let's talk about sexiest job in the recent Century which is data scientist yes for data science you need either python or R python is extremely popular in data science domain and as a data scientist you could be doing one of the two things either doing explor data analysis using jupyter notebook where you using python along with modules such as pandas numpy CPI Etc or you could be writing machine learning model and for that you will be using one of the popular python Frameworks such as psyit learn tensor flow or py toch data science jobs are booming and it offers lot of future career growth opportunities so once again python skills are going to be super Cru crucial for this particular role the next one is one of the popular roles in data science Industry which is data analyst as a data analyst you are responsible for doing data cleaning exploratory data analysis etc for which you could be using one of the four main skills which could be bi tool either powerbi or Tableau SQL Excel or python for doing data analysis in Python you'll be using basic python along with numai panda mat plot lib Etc now this career role is also for those who do not like coding that much in this role I have seen many people from non- Tech background mechanical engineers HR people they move to this role where because here the domain knowledge is important there are a lot of soft skills such as communication analytical thinking Etc are important and there are certain career opportunities where you don't need to do even coding that much you know by using bi to drag and drop you can complete your work so it is good career role for you if you don't want to do too much coding and the last one which gets most highest salary is machine learning engineer yes as a machine learning engineer you're responsible for solving business problem using machine learning and machine learning engineers and data scientists are kind of similar but machine learning Engineers are more software Engineers you know they know about scalability infrastructure they are responsible often for maintaining machine learning infrastructure in the company they know about distributed computing they can even twick uh the machine learning models in C++ all right so machine learning Engineers are more heavy on a software engineering and ml side whereas data scientist could be more on a business side and they know ml but from the applied standpoint I hope that give you a good understanding on various career roles available for you if You Learn Python once again check my affordable course on code basics. to get started with python today