I have around 8 + years of experience in process automation process reengineering. I am currently working as a Delivery Manager with HGS looking after automation delivery for strategic clients. My skillsets range from playing roles of solution architect, technical lead, business development & project delivery. On a personal front, I am a computer gaming enthusiast. Alongside being crazy about games, I like coding and always try to work on new tools in the market and explore details about them. I have been working in automation for my entire professional journey, be it industrial automation or process automation, and I have been exposed to both.
I am grateful to my seniors & peers who have mentored me throughout this journey. All this while before joining the program, I have been exploring forums and websites featuring data science courses either free or with minimal subscription only to bring myself up to pace with the market. I must say the PGP – AIML course by Great Learning has helped me immensely to streamline my upskilling as I have been all across multiple forums to understand what data scientists ML experts actually do & how to be one of them.
The biggest challenge will be the monetary challenge, along with a psychological fear of what if this course turns out to be similar to what other courses I had tried out earlier. I would definitely say data science is a vast field, and the only person who can do justice to the learning is you. Many platforms claim to make you proficient in the field of data science and also provide self-paced learning programs to give you the flexibility of learning. However, I am not against any one of them, but self-paced programs do tend to lose their flavor in due course of time as learners who are all also working professionals tend to lose the intensity program requires. This was my fear, and I had repeated conversations with the consultants at Great Learning on this. I also feared what if I had selected a platform inferior to its competitors.
As evident, it was a tough decision to make, and you always feel like your decision can backfire, or the worst, you learn nothing. This did provide me with a view that I wouldn’t have thought of. Challenges make us better equipped to make tough decisions, and I am proud of the fact that I was able to take the call and not on a hunch but an informed decision, thoughtful research, and due diligence.
I didn’t have any issues with the online medium of learning. By the time I was 100% sure to go with Great Learning, I was quite up to date with online learning as a service model.
There are many competitors of great learning, and they are really good as well. However, great learning definitely has the edge over others in the way they break down the batches, reduce batch size, enable students to post their learnings, prepare students for the corporate world (both freshers & experienced professionals). In my journey so far, I have had project work for every key module I learned, and honestly, the project works require a lot of primary research, and every time I landed on these platforms (Geekforgeeks, medium.com, stack overflow, Great learning Blog (of a peer learner). This also tells how much of the work is done in Great learning to enable learners in writing their own stories & projecting themselves to the external world.
This, alongside detailed research on Alumni of this program, helped me narrow down to great learning as my choice for the journey towards learning AI & ML.
Honestly, mentoring sessions are the best part of the program. They have helped me close the loop on the modules. The slides PDFs shared are easy to understand, but the mentoring sessions take us to the very bottom of each and every concept, thereby helping me to understand and retain the learnings from that module. I would also like to say one more thing about the mentored learnings, and that is the timing of the sessions. All sessions are 120 mins at max, which definitely ensures we don’t lose track or don’t get bored with the concepts discussed. One more very important aspect of the mentoring sessions is the mentor itself; they are industry experts and have their papers on some of the topics which are being covered in the class, which does help in motivating us to continue our education. Mentored sessions are of top-notch quality. There is no doubt they are the best part of the program for me. Initially, I found it a little hard to join the mentoring sessions as they are early in the morning for me, but that tells how they have been engineered to install maximum efficiency in all of us, students.
I would say the role of the mentor is only 50% in the success as the rest is on me to learn and not unlearn things quickly. Having said that, the mentor plays a huge part in arousing interest in the topics as statistics and data cleaning modules are the most time-consuming modules of this course and require a lot of deliberation from us. Great Learning provides the best of the best mentors as they make the session interesting and informative. The case studies live troubleshooting does make my life easier as a learner.
I have started working on my acquired skillset. Though the scale is small right now, I am also progressing towards business insights and business analytics in my professional journey. It will still take time, though, but the journey has already begun.
My advice to everyone would be to choose the right platform for learning, perform due diligence on courses and then move on with it. Also, understand the difference in the buzzwords currently ongoing in the industry. AI, ML is not about knowing the usage. It’s about knowing what to use and when. It also needs a clear understanding of what is the end goal. Most courses would claim to make you proficient in programming languages but are that the ultimate goal. If I am to take the call on this, then it would be a no, as programming can be taught, and the concepts are learned. I would always target the concepts. First, programming will follow as, believe me, model building and other buzz words are just words; 80% of a data scientist’s work is on data cleaning, data preparation, featurization & other activities.
I would urge every one of you to understand what you want and don’t be under peer pressure to learn something. As long as it is in your skillset and is in line with your aspirations, go with it or decide something else which will close your loop of education.
