I received Excellent Support from the Program Manager: Sujay, BACP.

I pursued the BACP (Business Analytics Certificate Program) to get a headstart in my organization’s analytics team.
I am from a manufacturing background. When I got a strategic project management role in my organization Nalco, I knew that I needed to upskill to analytics quickly. They were setting up an analytics unit in Pune and hiring across a wide experience range, so I needed to learn analytics to become a part of the team. A program like BACP has immense brand value and I knew that it would help me a lot to substantiate my analytics knowledge to my company.
My current role demands that I spend more time in the office so an online format was perfect for me.
My current role is at an upper middle management level which comes with its set of challenges and travel time. I wasn’t sure if I would be able to allocate enough time to studies in a full-time program, so I opted for an online format. As per inputs and reviews from other BACP participants, I was convinced that BACP was going to be quite a different experience with its micro-classes (study sessions in small groups of 5). I also realized that at the end of the day, whichever program format you opt for- blended, online, full-time, you will be driven by your interest and intent to learn. Self-study is an extremely critical component in any program.

Is BACP the right program for me?
I must have asked this question to myself at least a 100 times before enrolling as I was hard-pressed on time. Honestly, I did my market research, but most of the courses or programs required me to have prior statistical knowledge or programming experience, etc. Due to my background in the manufacturing industry, I had some statistical know-how in my arsenal, but I was averse to coding and programming. Feedback and reviews from alumni actually convinced me the most to go ahead with BACP. Post completion, I have started working on analytics projects but our projects are very basic unlike the sophisticated models built in other companies. It’s a manufacturing company predominantly driven by sales. I mostly use linear and logistic regression in my current project.
My biggest challenge during the program was managing my study time.
The success of any course does not depend on the course curriculum, the faculty, or any other components. It mostly depends on your dedication and hard work. After a 12-hour day at the office, spending an additional 3 hours even on the weekends seemed like an uphill task. Stretching oneself has its limitations but I knew that in order to do justice to the time and money I had invested in the program, I will have to get over these time constraints and establish a studying rhythm that would last for the entire duration of the program. I received excellent support from the program manager who kept my spirit high even during the low days when I just wanted to give up. Dipayan Sarkar, my mentor was very helpful and encouraging. And even today, I am in touch with him.

Lifelong Learning as a mantra is embedded deep in my system.
After my graduation, I was sure of one thing that I would always keep myself updated and upskill from time to time. Over the past 10 years, I have pursued several programs, at least one program every two years. While others may disagree, but the days of learning once and applying the learning forever are behind us. Learning new things gives me the confidence to catapult both my personal and professional growth.
My advice to aspirants wanting to pursue this course is to be serious about it.
I somehow feel that doing a course just for the sake of a career transition will not be successful all the time. The candidate should be really serious about learning something new. From a learning perspective, it should be more about the process rather than the result. One more thing to be clear about is the projects you will be working on once the program is completed. Analytics is not just about building fancy models from day one. It will take a long time (even 4 to 5 years) to become a proficient data scientist for anyone entering the field without experience.