Divya Maggu’s Journey: From Learning to Working in Data Science

The world of data science is growing fast, and many people are wondering how to get started in this exciting field. They’re asking important questions like, “What’s the best way to start my data science career?”, “How do I choose the right place to learn data science?”, and “Do I need any prior knowledge to begin?”. If you’re thinking about becoming a data scientist, you’re not alone in trying to figure out these first steps.

In this article, we dive deep into the career trajectory of Divya Maggu, an AI/ML Engineer at ExamRoom.AI, to find the answers to these pressing questions. Divya’s path from completing his B.Tech in Computer Science to becoming an expert in AI and machine learning is truly motivating. Her experience highlights that with proper guidance, the right materials, and a strong commitment, anyone can build a rewarding career in the field of data science.

Let’s dive into the conversation:

Q1: Can you tell us about your background and how you ventured into the field of data science?

My name is Divya Maggu, and I am currently an AI/ML Engineer at ExamRoom.AI. I have a B.Tech degree in Computer Science, and during my college days, I developed a strong foundation in Python programming. My curiosity led me to explore what I could do after learning Python, and that’s how I stumbled upon data science. I researched extensively, created a roadmap for myself, and eventually found DataMites. I joined their Certified Data Scientist Course in January 2022, completed it by August, and landed a job at ExamRoom.AI within a month.

Q2: What was your level of expertise in programming and statistics when you joined the Data Science Course at DataMites?

My programming skills were centered around basic Python, and I had a foundational understanding of mathematics and statistics. However, joining DataMites was a game-changer for me. The data science course was structured to cater to various proficiency levels, and it meticulously took me through the basics to the advanced levels of programming and statistics. I found myself diving into machine learning and deep learning algorithms, which captivated my interest even more. The depth of knowledge I gained from DataMites was unparalleled, providing me with a holistic understanding of the data science domain.

Q3: How much time did you dedicate to learning outside the training hours at DataMites?

My learning schedule differed each day. There were days when I spent 3 to 4 hours studying, and there were others when I spent a bit less time. However, it wasn’t really about how many hours I put in, but more about how concentrated and devoted I was during my study sessions. In a field like data science, staying consistent and dedicated is key, and I made sure to stay committed to my journey, aiming to make the most out of every single study session.

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Q4: Can you share your experience working on data science projects at DataMites?

At DataMites, I got hands-on experience with a range of projects, really boosting my practical knowledge. I worked on two detailed image classification tasks, learning a lot about the Convolutional Neural Network algorithm and aiming for better accuracy. Besides that, I solved a regression problem and worked on a project for a client, seeing firsthand how data science works in different situations. This mix of theory and practice from the projects played a big part in building my data science job.

Q5: Did you face any challenges or rejections in interviews before securing your current job?

My path to becoming a data scientist had its ups and downs. I didn’t get through three job interviews, but I didn’t let that get me down. Instead, they provided me with invaluable insights into the interview process, teaching me the importance of context and subject-matter expertise. My fourth interview, facilitated by DataMites’ data science placement team, was where I finally succeeded. These experiences honed my interview skills, teaching me how to present my knowledge confidently and effectively.

Q6: What learning materials and resources did you refer to during your course at DataMites?

My primary resource for learning was the extensive study materials provided by DataMites. This included comprehensive coverage of mathematics, statistics, as well as recorded sessions on a plethora of topics. Upon completing these, I explored additional resources on platforms like YouTube and Udemy. This combination of structured data science training materials and supplementary resources ensured a well-rounded learning experience, solidifying my knowledge and skills in data science.

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Q7: Can you tell us about your current work and projects at ExamRoom.AI?

Currently, my focus is on computer vision projects, which I find incredibly fascinating. Prior to this, I worked on a Natural Language Processing (NLP) project. My initial two months were dedicated solely to data preparation, underscoring its critical role in the machine learning engineering process. The variety and complexity of the projects ensure a constant learning curve, keeping my work both challenging and rewarding.

Q8: How has your experience been working as a data scientist?

Working as a data scientist is enjoyable and satisfying. My team is great; they’re supportive and we work well together, which really adds to my positive experience at work. There’s always something new to learn in data science, and if you manage your tasks well, it’s a career that can bring a lot of fulfillment.

Q9: Can you walk us through the different rounds of interviews you faced before securing your current position?

I faced three interview rounds. The first one had questions about Python, machine learning, SQL, and deep learning. The mock interviews at DataMites really helped me get ready, teaching me how to express my answers and think about the questions in various ways. The feedback from the interviewers at the end was really helpful too, as it showed me what I was good at and what I needed to work on.

Q10: Is it necessary to have prior knowledge or experience before joining a data science course?

Before you start a data science course, it’s vital to know what you’re getting into. Ask yourself if you’re truly interested in this field and ready to stick with it for the long haul. You can learn the technical parts as you go, but it’s really important to be genuinely interested in the subject. So, take some time to look into what data science is all about, get familiar with the basics, and make sure this is something you’re excited to dive into.

Divya Maggu’s journey from a Computer Science graduate to an AI/ML Engineer is a testament to the transformative power of quality education and relentless dedication. Her story illuminates the path for aspiring data scientists, providing insights and valuable lessons from her own experiences. 

DataMites plays a pivotal role in nurturing talents like Divya, offering a plethora of courses including artificial intelligence, data analytics, data engineering, blockchain, tableau, machine learning, and Python. With physical centers in Bangalore, Chennai, Pune, Hyderabad, Mumbai, and Ahmedabad, along with comprehensive online data science training and data science offline training options, DataMites is committed to empowering the next generation of data science professionals. We invite aspirants to join us, embark on their learning journey, and unlock the doors to limitless career opportunities in the dynamic field of data science.

Watch the complete interview here