Subasree's Inspiring Journey: From Fresher to Data Analyst
Discover Subasree's inspiring journey to becoming a Data Analyst. Learn about the challenges she overcame, the skills she mastered, and the steps she took to achieve success in the data analyst field. Gain insights and motivation to start your own data analyst career today!
Kriti Maurya
In today's competitive job market, transitioning from a fresher to a Data Analyst can be challenging. With soaring demand, professionals from various fields are upskilling to seize new opportunities. Subasree’s journey from an IT professional to a certified data analyst showcases this transformation.
However, With proper guidance and unwavering determination, success becomes attainable. In this success story, we bring you Subasree's inspiring journey from being a BSc Mathematics graduate to securing a Data Analyst position.
Subasree's Inspiring Journey to Becoming a Data Analyst
Subasree’s journey into data analyst is a testament to passion, perseverance, and continuous learning, proving that with the right skills and determination, anyone can transition into this dynamic field.
Q1: Can you introduce yourself and share a bit about your background?
A: Yes, of course! I completed my BSc in Mathematics from BSG College, Bangalore. After graduation, I spent nearly two years searching for jobs. Initially, I planned to pursue MSc, but things didn’t work out as expected. Eventually, I decided to upskill myself and joined DataMites Institute to learn Data Science. That decision changed my career path, and today, I am successfully working as a Data Analyst.
Q2: What motivated you to switch to Data Science from a Mathematics background?
A: Mathematics is an essential foundation for Data Science. While I was exploring career options, I realized that my mathematical skills could be an advantage in this field. However, programming was new to me. I had only studied basic Java and C in college but had no exposure to Python. Fortunately, my mentor at DataMites Institute, guided me well, and I was able to grasp programming concepts over time.
Q3: How long did it take for you to become proficient in programming?
A: Initially, I found programming challenging. The basics were easier to understand, but I had to put in extra effort to master coding. I practiced on various platforms that provide coding challenges and self-evaluated my progress. Within 2-3 months, I became comfortable with programming.
Q5: What kind of questions were asked in your job interview?
A: The interview focused primarily on my projects. I was asked to explain the projects I had worked on and differentiate between Data Analytics and Data Science. Additionally, I was given a dataset and perform a scenario-based analysis using a dataset.
Q6: How did your mathematics background help in understanding Machine Learning?
A: My background in mathematics was extremely beneficial, especially in understanding machine learning algorithms like linear regression. While some of my peers struggled with mathematical concepts, I found it easier to grasp the logic behind algorithms.
Q7: Do you think understanding the mathematical intuition behind algorithms is necessary?
A: It depends on the individual. Some people prefer to focus only on implementation, but for me, understanding the mathematical foundations helped me grasp concepts better. Knowing the core principles behind an algorithm makes it easier to apply them in real-world scenarios.
Q8: Were there any SQL-related questions in your interview?
A: Surprisingly, I wasn’t asked any SQL-related questions. However, I was asked about my proficiency level in SQL, and I honestly mentioned that I was familiar with basic queries but hadn’t explored advanced SQL yet.
Refer to the article:
Q9: How challenging is it to secure a job as a fresher in Data Analytics?
A: It’s quite challenging because most companies prefer candidates with experience. Even after completing my course, I initially struggled to find job opportunities. Companies often ask for a minimum of one year of experience. Without strong knowledge in Python, Machine Learning, and SQL, it is nearly impossible to secure a role unless you have strong references.
Q10: How was your learning experience at DataMites?
A: At first, I was nervous about whether I had chosen the right path. However, my experience at DataMites Institute was excellent. The mentors were supportive, and they always provided prompt responses to my doubts. They guided me through projects, and their teaching approach made learning easier.
Q11: You pursued a Data Science course but got a job as a Data Analyst. How did you prepare for this transition?
A: Initially, I focused only on Data Science. However, I later realized that being open to Data Analyst roles would increase my chances of landing a job in Data Analyst career. I started learning additional data analysts tools like Power BI and prepared for Data Analyst roles alongside Data Science.
Q12: What projects did you work on during your training?
A: I completed projects related to Time Series Analysis, Regression, and Classification models. I deliberately chose challenging datasets that required extensive data cleaning. Handling missing values and outliers was one of the biggest learning experiences.
Q13: What was the toughest question you faced in your interview?
A: The toughest question I encountered was related to Large Language Models (LLMs). I had heard about them but didn’t have in-depth knowledge. I honestly told the interviewer that I was aware of the term but had not studied it in detail. I assured them that I would learn about it after the interview.
Q14: Did you include all your projects in your resume?
A: I included the four capstone projects and one client project in my resume. However, I also worked on some personal projects, which I stored in Google Drive but didn’t mention in my resume initially. Later, I planned to add links to showcase my work.
Subasree’s journey is a testament to the power of persistence, self-learning, and adaptability. Transitioning from a fresher to a Data Analyst career is no easy feat, but with dedication, hands-on projects, and the right guidance, it is possible. If you are an aspiring Data Analyst, take inspiration from her story—keep learning, stay consistent, and never give up!
Q15: Any advice for freshers aspiring to become Data Analysts?
- Build strong foundations in Python, SQL, Machine Learning and in top tools for data analyst.
- Work on real-world projects and showcase them in your resume.
- Master data visualization tools such as Power BI and Tableau.
- Be patient and persistent—job hunting can be challenging, but don’t give up.
If you don’t get Data Science roles immediately, explore Data Analyst job roles as an entry point.
Key Findings from Subasree’s Data Analyst Journey
Here are the key findings from Subasree’s inspiring journey to becoming a data analyst:
- Passion for Data Science and data analyst– Subasree’s curiosity and interest in analytics led her to explore opportunities in data science.
- Overcoming Challenges – She faced difficulties in mastering algorithms, data processing, and keeping up with industry trends.
- Building Essential Skills – She gained expertise in Python, SQL, machine learning, and data visualization to strengthen her foundation.
- Hands-on Learning & Mentorship – Enrolling in courses and seeking mentorship helped her apply concepts to real-world projects.
- Successful Career Transition – With dedication and continuous learning, she secured a data science role, proving the power of perseverance.
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- Data Analyst Course Fee in Pune
Subasree’s journey from a Mathematics graduate to a Data Analyst is an inspiration for freshers looking to enter the field of Data Science and Analytics. Her story highlights the importance of continuous learning, adaptability, and perseverance. If you’re aspiring to become a Data Analyst, take note of her strategies and stay committed to your learning path!
In today’s competitive world, mastering data science and analytics is the key to unlocking high-growth career opportunities. Sectors such as healthcare, finance, retail, and manufacturing are increasingly leveraging data analytics to enhance operations, improve customer experiences, and drive innovation. According to the "Instahyre Tech Salary Index 2025," Bangalore holds 35% of India’s tech workforce, with Delhi-NCR and Hyderabad each at 20%, followed by Pune at 15% and Chennai at 10%.
Moreover, NITI Aayog released the report 'India's Booming Gig and Platform Economy' on June 27, 2022. The gig economy and remote work models are driving a 10% growth in the tech sector, reflecting evolving work preferences.
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What sets DataMites apart? Our comprehensive, job-oriented curriculum ensures that every learner gains hands-on experience with real-time projects and internships, bridging the gap between theory and practical application. With live training, online classes, and on-demand offline learning, DataMites provides the flexibility needed to upskill at your own pace.
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DataMites Institute offers comprehensive offline Data Analyst courses in Pune and across multiple Indian cities, including Bangalore, Chennai, Hyderabad, Ahmedabad, Jaipur, Coimbatore, Delhi, and Mumbai, accommodating diverse learning preferences.
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