How a B.Com Graduate Became a Data Analyst- Roshni’s Inspiring Journey

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Analytics is an exciting career due to the high demand for professionals who can analyze data and provide insights,, the chance to work with cutting-edge tools and technologies, and the satisfaction of making a significant impact on businesses and organizations. Starting a career in analytics as a fresher from a non-tech background can be challenging, but it is not impossible. 

Today we bring to you the inspiring journey of Roshni Jain, a student at Ivy Professional School  who successfully migrated from a non-tech background to the role of a data analyst in Capgemini.

Ivy: Tell us about your academic background.

Roshni: I had no idea about data science before i joined Ivy Professional School. I studied in Rajasthan Vidyamandir Kolkata and then pursued a B.Com degree from THK Jain College (graduating in 2021). In the same year, I joined Ivy Professional School.

Ivy: How did you get introduced to data science?

Roshni: One of my relatives introduced me to the field of data science and I spent several hours researching about it online. I wanted to make an informed decision about the career prospects and the scope of an analytics career before taking a plunge. While researching, I discovered Ivy Professional School and was quite impressed with the course structure. I decided to enquire more about the course and received detailed counselling from Ivy to understand which course will suit me the most. I finally opted for the Data science with Data Visualization course.

Ivy: Tell us what were your learnings from this course?

Roshni: The course allowed me to learn all the crucial skills to kickstart my analytics career. Some of the important things I learnt include:

  • Dashboarding and automation using Excel
  • SQL queries and relational database management
  • Data visualization using Tableau
  • Business Statistics
  • Predictive Modeling with R
  • Data science with Python
  • Machine Learning Fundamnetals

Ivy: As you were completely new to the field, did you have any difficulty to pursue this course at Ivy? 

Roshni: When I started my data science course, I was completely blank about what data was and how to approach it. However, as I progressed through the classes, I began to understand the concepts better, and my motivation to pursue data science grew stronger.

What I appreciated most about the course was its short duration of just 8-9 months, with well-planned modules that were easy to follow. Additionally, having supportive faculty like Eeshani Ma’am and Prateek sir who were always available to help was a great benefit. I was amazed at how much I learned during the course, going from zero knowledge of data to gaining a deep understanding of every topic in the field. The experience was challenging but rewarding and made me feel confident in my ability to apply what I’ve learned in a professional setting.

I also benefitted from the course greatly as it emphasised on preparing reports and projects, which gave me practical experience and the opportunity to demonstrate my skills to potential employers. As someone from a non-tech background, this was particularly helpful in landing a job in the field. I am extremely grateful for the knowledge and skills I gained from this course and the support I received from my faculty. Joining Ivy was a life-changing experience that opened up a world of opportunities for me in the field of data science.

Ivy: Tips for freshers trying to build a career in data science?

Roshni : Work on projects to develop practical data analytics skills using real data:

As an aspiring professional in analytics, practical experience is essential in developing skills in data analytics. You must gain this experience by working on real data projects. Ivy made us work with real-world examples and gave us the confidence to showcase our skills to potential employers.

Try to explore the industry

To stay up-to-date with the latest data analytics developments, exploring the industry is essential. You must attend industry conferences, meetups, and webinars to learn about new technologies, tools, and techniques. Make it a habit to network with professionals in the field to gain insights into current trends and best practices. 

Sharpen your technical skills

To succeed in data analytics, you need to have a strong foundation in technical skills. You must have thorough conceptual knowledge of Python, R, and SQL, which are commonly used in data analytics. You also must be well-versed with statistical concepts, data cleaning, data preprocessing, and machine learning algorithms.

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