Meet Japjeet Kaur, the dynamic Data Analyst from Amity Global Business School. Dive into her exhilarating journey of transforming from an Assistant Professor to conquering new horizons in the realm of Data Science.
Uncover her fascinating tale as she takes us on an extraordinary adventure through this incredible transformation.
Q1: Tell us about your current role and background.
Hi, I am Japjeet. Currently I’m working as a Senior Data Scientist at Oracle India.
I began my journey in education, serving as an Assistant Professor at Galgotias College of Engineering and Technology, affiliated with AKT University. After seven and a half years, I transitioned from education to the industrial sector.
During my teaching years, I mentored students in data science, machine learning, and AI projects. My interest grew, encouraged by my department head.
To pursue this passion, I took a sabbatical, extensively researching various institutes. I chose Accredian due to its compelling promises, curriculum, and support.
It proved to be an excellent decision as my journey was met with consistent support. After completing three terms, I earned my Data Analyst certification and began applying for relevant job roles.
Q2: What challenges did you face while switching to Data Science?
Coming from an educational background, I initially wondered if I could apply theoretical knowledge to practical data science tasks. The first challenge was bridging this gap.
Secondly, mastering basics demanded time, yet Mr. Deepesh Wadhwani’s teaching across three terms greatly assisted. His approach aligned with our learning style, addressing hurdles effectively.
I’m currently navigating machine learning, facing challenges, but I’m determined to overcome them using the provided materials.
Q3: How has Data Science helped you in your new role?
In my current role, I handle accreditation tasks, aligning with UGC and Ministry of Education requirements. Institutions need accreditation and rankings like NIRF to map their metrics.
For NAAC accreditation, there are 85 metrics, 32 qualitative, and the rest quantitative. Analyzing data on research, student admissions, pass rates, and more is crucial.
My role involves predicting an institution’s grading and score based on incoming data. Data cleaning is essential, as people often input data with slight variations. I also conduct audits, analyzing student feedback and academic/administrative aspects.
Recently, for NIRF rankings, my team predicted scores across 16 parameters to secure a top university ranking.
Q4: What was the interview like at your current company?
When applying at Amity, I underwent a thorough interview process with six to seven stages. Initial rounds covered introductions and technical discussions. My familiarity with preparing files for accreditation, from my teaching experience, proved valuable.
Technical questions focused on data science applications and assisting with accreditation. They also inquired about my potential contributions to the internal quality assurance cell and the significance of a data analyst role in their team.
The final interview gauged my teamwork and behavioral skills, addressing my transition from education to a data analyst role and assessing how I’d integrate into their team.
Q5: Why INSAID? How did it help you?
I found invaluable assistance from Accredian as I navigated my career transition. Initially, I contemplated a direct shift to the industrial sector, considering my electronics engineering background and IoT interests.
I possessed contacts for direct industry entry. However, my department head encouraged a data-related path. With encouragement, I explored options and chose Accredian, which offered positivity and prompt answers to my queries.
Their team arranged a conversation with a successful transitioner, boosting my confidence. Initially, I joined the 6 month GCD program, but later upgraded to PGP during my journey.
This shift was smooth and supported by Neha Jain from the Academics team. Moreover, they accommodated my need to learn Tableau swiftly for my role at Amity University, providing recordings and live sessions tailored to my needs.
Accredian has truly been remarkable in my career shift.
Q6: What advice would you give to Data Science Beginners?
My advice is to dive in without hesitation and avoid overcomplicating things. The field isn’t overly challenging if you’re dedicated. If you find joy in Excel and working with data, learning is feasible, but it requires consistent effort.
Avoid sporadic studying; regularity is key. Even with work commitments, dedicating 45 mins to 1 hour for learning is effective. Concepts must be maintained to avoid forgetting, given the interconnectedness.
Additionally, while course materials provide a base, practical application is crucial for deepening understanding. Consistency and grasping fundamentals like math and statistics are essential.
We hope you found this success story interesting. If you have any Data Science questions, please fill this form and we will get back to you.
