As documented by Aditya Shivam
Right from the start, I was always looking toward the future. I started out as a software engineer, but was fascinated with the seemingly endless possibilities in data science and artificial intelligence (AI). I knew these interrelated fields would be the way of the future and I wanted to become a part of it. Undergraduate technology degrees provide a broad basis of knowledge, but it was up to me to hone my skills and specialize in a given niche.
For me, that niche turned out to be big data and AI. All I needed was the right combination of practical, hands-on skills to get started. This is the story of how I prepared for the challenges I created for myself, including the crucial role of finding the right educational platform.
The Challenge: Making a Lateral Move from Software to Data
My career never felt right to me, which made me realize early on that I wasn’t on my preferred trajectory, even though I enjoyed early success. I started my career as a software engineer trainee with Nucleus Software in Noida, India, working my way up to senior software engineer role after two quick promotions.
I realized that essentially all industries would be looking for ways to leverage the tremendous amount of raw data generated every second in order to gain an edge and create efficiencies. My specific interest was in how this data could be used for predictions, classifications, and language processing within the context of AI and machine learning.
Machine learning has changed the way we see data. Now, with the help of easier programming languages like Python, we can create magic with code.
My other passion, also enabled by big data, is robotics process automation (RPA). While the term “robot” may conjure images of industrial machines welding steel automobile chassis together, or perhaps your automated vacuum cleaner robot, RPA has more to do with the automation of administrative work. RPA may automate such processes as IT support, shipping logistics, data migration, or other jobs that reduce the time humans spend on repetitive tasks while reducing errors.
Every magician must learn the tools of their trade, however. I was confident I had the requisite foundation in technology but needed specific skills training to get started.
The Simplilearn Solution: Patience and Planning for the Future
I knew I wanted to learn the core skills that would enable a future career in big data and AI, but I wasn’t sure where to turn at first. I watched numerous free tutorials online but realized I wasn’t getting anything “concrete” that would help me make this transition. Then, one of my relatives suggested that I look into Simplilearn’s offerings, something they have benefited from.
In order to make the big shift I had planned, I decided to take my relative’s advice and enrolled in Simplilearn’s extensive Big Data Engineer Master’s Program. This program combines several key courses on data science that help prepare learners for a wide variety of roles involving data and AI, including instruction in Hadoop, Spark, MongoDB, Data Science with R, and Data Science with SAS.
While I found the Hadoop ecosystem to be the most interesting part, I truly appreciated the comprehensive nature of the program. I learned the fundamentals of data science and how it could be applied in addition to hands-on training on how to use Python, R, and other tools and processes.
All these courses actually helped me to see my way through to become a data scientist and was pleased not only with the subject matter but in how it was packaged and presented in a practical, career-ready manner.
I didn’t go and look for a job the moment I completed the program, instead I took about four months to fully immerse myself in these technologies, while continuing working in my software engineer job. That meant less time for my other personal interests, including cricket and spending time outdoors, but I had come prepared with a singular goal in mind.