Don’t just fall for the glamour and hype.
There are plenty of resources available online, such as the various articles and blogs in Analytics Vidhya, to get a feel of what this field is all about Statisticians and programming professionals will undoubtedly have a bit of an advantage.
But here’s the good news – the transition is possible even for non-technical people.
The primary thing that matters is your thought process and skill of questioning and analyzing the information at hand If you are an experienced professional in any other sector, get ready to be treated as a relative fresher when you make the switch to data science.
It might be quite difficult for many people to accept a transition where you have to forego some years of seniority.
I understand that.
But if you give your best, then this industry will bestow even more knowledge, greater successes and awesome salary hikes on you The Different Activities and Resources that Helped my Transition Despite the industry focused and well-structured curriculum that Praxis provides in its full-time program, it’s very essential to know where you stand in the world of Data Science.
To get into the ecosystem, I participated in as many hackathons as possible, especially those organized by Analytics Vidhya and Kaggle.
These hackathons give a sense of real-life data science problems and also provide a leaderboard to compare yourself against the top data scientists.
Even now, when I am into this field professionally, I try my best to attend the great meetups organized by Analytics Vidhya, Pycon India, etc.
to name a few.
These meetups are the best source of knowing what’s happening in Data Science around the world and meeting some great minds.
Also, I always read the blog section of AV – it comprises of the latest developments in Data Science, and also explains ML algorithms in simple and easy to understand language.
Moreover, I have some great LinkedIn connections who share wonderful articles related to AI and ML regularly.
I also used to refer to the UCI Machine Learning Repository which is another great source for datasets.
The resources are unlimited but you have to know how to search and find them as self-learning is the best thing that one can gift oneself.
Challenges Faced During this Transition The biggest challenge is to stitch not one but many skills together to be an effective data scientist – skills like statistics, machine learning, databases, visualization techniques, programming and of course the art of storytelling.
Also, there is always a new tool or package or an entire algorithm coming up every now and then.
It’s a tough job to cope with the speed with which this field is progressing.
Therefore, in addition to the classroom studies, I used to devote an extra hour daily to read articles and blogs published in AV, Quora and LinkedIn so that I stay updated with the latest technologies.
Also, I have reached out to the faculty team at Praxis in case of doubts with any concerned subjects and every time they proved to be very helpful.
PRAXIS: The beginning of new challenges and opportunities Coming back to student life after spending 4 years in the corporate sector was quite a challenge.
From the first day at Praxis, it was a known fact that the next one year was not going to be easy.
A variety of nearly 25 different subjects distributed over 3 trimesters were covered during a span of nine months.
Most subjects were totally new to me.
But I was hooked due to my previous programming experience and love for statistics.
There were subjects in different domains, like: Basic and Advanced Statistics Machine Learning Algorithms Marketing and Retail Analytics Data Warehousing Web Analytics Finance Services Analytics HR Analytics Text Analytics Data Visualization We were exposed to different tools and technologies like R, Python, SAS, Tableau, Hadoop, Spark, etc.
The curriculum was very well designed with concepts and real-life application-based case studies going hand-in-hand.
Campus Life: A Bag Full of Memories My days at Praxis were full of assignments – tons of quizzes, semester exams, team presentations and, of course, the main group capstone project.
The environment at Praxis and the quality and attitude of the faculty was superb.
This new world of new subjects and exams turned out to be very interesting and the challenges worth taking.
Additionally, the class was a mix of highly talented professionals from diverse industries (there were a few freshers as well).
This encouraged healthy discussions, brainstorming, and learning from each other.
In addition to academics, there were various cultural and sports events that lent a good balance between studying and campus life.
The First Analytics Job Offer: Outcome of the Intense Routine The placement season in Praxis starts as early as November every year.
But the preparation for it starts from Day 1 of joining Praxis.
I was fortunate enough to get a job offer from the National Payments Corporation Of India (NCPI) in the role of a Data Scientist.
This happened through the campus placement program.
Three other colleagues were placed in NCPI, while others were placed in organizations across different verticals and function areas.
In NPCI, my team was building a Fraud Risk Management Model to predict and prevent different kinds of fraud transactions – ATM, UPI, POS and E-commerce.
We worked on different technologies, such as Python, R, PySpark, Julia, Tableau and Hive.
It was my first experience working with petabytes of data.
The NPCI journey, though short, was interesting, challenging and a great learning experience of nearly one and a half years.
The classroom training received at Praxis and the rigor that we were made to go through in that one year turned out to be very helpful in not just getting the job but in performing with distinction.
The next BIG move: Learn and grow It is exciting setting targets and moving forward to achieve them.
After a successful professional career in NPCI, I got the opportunity to move to one of the BIG 5: AMAZON.
I joined Amazon as a Data Subject Matter Expert (Data SME) in the Alexa Data Services branch of Amazon Analytics.
This was indeed a dream come true and I personally believe that my academic and industry background, my decision to leave my job and enroll into a full-time program in analytics at Praxis, and my stint with NCPI were all contributors to this achievement.
My current role involves working with Alexa Machine Learning Scientists to enhance the Alexa experience.
I am quite new to this venture and am committed to making this a successful one.
End Notes It is my utmost pleasure to share my story on the portal which has an equal contribution in building my Data Science career as far as self-study through blogs and competitive hackathons are concerned.
As artificial intelligence and machine learning are grabbing the world with their presence, we also need to learn new things, take risks, work a little harder and start looking for opportunities.
If you are serious about a career in Data Science, take time off and register for a full-time program, – and use that time to dive deep into the domain.
It is quite complex and will take a lot of time and work – but the results are worth the effort.
Data Science, AI and ML – these are great opportunities – if you like technology and numbers, go for it – and as they say at Praxis – ‘CELEBRATE YOUR WORTH’.
This is a sponsored post and the opinions expressed in this article are exclusively of the author.
A few minor edits have been made by Analytics Vidhya.
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