With over 17 years of domain knowledge and industry experience in IT and business analytics, Siddhartha was determined to secure data science jobs in Melbourne. He balanced a full-time job with part-time learning and became trained by industry experts in just 24-weeks through our part-time Data Science & AI program. It allowed him to leverage his current skillset and expand his career prospects. Siddhartha developed a solutions-focused Capstone project and a new set of in-demand skills to enhance his career.
This is his journey so far:
You have a strong background in IT and business analytics! What motivated you to upskill within the Data Science and AI industry?
“I have almost 17+ years of experience in the IT world. Since the beginning of my career, I was always really interested in learning new things. As a result, I have engaged in multiple roles – such as solution architect, business analyst and even presales consultant. With industry 4.0, data and applications around data, insights have become essential for any decision making in any industry. It is now essential to be data-literate. In the future, my dream is to become an AI product owner!”
What was the most challenging aspect of completing the course and what did you enjoy the most?
“I do work a full-time job. Hence, balancing my work and part-time studies was the most challenging part. It was quite a stretch. I would like to highlight and thank my wife for supporting me in this journey all throughout! The most enjoyable part of the course was attending the class in a classroom setup, just like I had done last in my MBA days.”
Having experience in the IT field already, how did you transfer and apply those experiences to apply for data science jobs in Melbourne?
“For any new learning area, it is important to understand the fundamentals and the building blocks. My experience has taught me to figure out those fundamentals and build a strong foundation. I believe that helped me in my learning journey. Data science jobs in Melbourne are quite broad and it is very easy to get lost in the overwhelming information overload. Having a step by step approach always helps to grasp the subject. Once the foundation is strong, it becomes easier and one can learn in more depth within any area. Another aspect that helped me was the understanding of programming languages & SQL. These are essential in the data world. Although I am not a developer, it is good to have that skill and Python as the language was easy to learn.”
What were your career goals before upskilling to data and how has your outlook changed?
“Well, I had a goal of moving to product management. Being part of multiple transformation programs across the globe, I have that drive to become a full-fledged product manager. With data & AI, that goal has shifted towards becoming a product manager for an AI product. The core underlying concept remains the same, only the area of work is now more channelized.”
Why did you choose to join the Part-Time Program in particular, what appealed to you?
“As highlighted above, I do work a full-time job. So, the part-time format was the most suitable for me. The most appealing part is the face to face learning in a classroom setup. I simply loved it.”
Tell us about your capstone project! How did you come up with your topic?
“Finding the right topic for the capstone project was the most challenging part. In the data world, you need to have a problem statement as well as the underlying data. Both are equally important to make the project successful. My topic was on predicting how soon a customer request can be fulfilled. Often, we have seen that customers log service requests to the service providers, and we get a blanket message that it will be done within X days. I feel being a consumer, this does not help, especially for services that need immediate attention.
The underlying data was service request data of NYC 311. This is one of the largest service providers in the city of New York – serving ~10Mn people. It is essential to identify the business objective before solving a data problem. From the same data, multiple dimensions can be analysed and solved. At the end of the day, if it does not yield business value then that outcome is of no use. Driving the data problem from the business problem, and aligning the outcome to business KPIs is the best part of the project.”
How did you find the process of completing your final Capstone project during the course? What did it teach you?
“Completing my capstone project taught me so many important and valuable lessons. It was important for me to start early – define the problem statement and the underlying data as time does fly quickly. I noticed the problem statement and underlying data can not actually match, so it was important for me to be flexible in tweaking the problem statement without compromising the business value. In addition, I noticed many individuals do get bogged down by the level of accuracy in their projects. I found it important to not be disheartened, the learning is the important aspect, not the accuracy. Finally, I discovered the necessity to keep experimenting till the end and make time for documentation and presentation preparation. The overall true beauty of data science is the necessity to continually experiment to the end, which I enjoyed thoroughly.”
What advice would you give someone developing their Capstone project during the program?
“Some of the learnings are as follows –
- Start early on defining the problem statement & the underlying data. Time flies very quickly.
- It might so happen that the problem statement & underlying data may not match. Tweak the problem statement without compromising business value.
- Data is often not clean enough. Data cleaning is a prerequisite for any ML problem. So, allot time for that activity.
- People sometimes get bogged down on the accuracy levels. Well, you need to understand it is a capstone project and not something you will run on the production. Don’t get disheartened. You will get ample opportunity in real life to match high accuracy levels. Accuracy is not that important, the learning is more important.
- Keep experimenting till the end. I believe that is another beauty of securing data science jobs in Melbourne. You never know till you experiment.
- Finally, keep time for documentation and presentation preparation. It is equally important for the completion of the project.”
What did you enjoy the most about the Capstone project?
“Performing while learning is a key part of the journey. The course was divided into multiple modules and each module had its own significance. I believe almost all the modules have some relation to the capstone project. Both the course and the capstone project were running in parallel, which is a unique method of teaching. When the course ended, the capstone also came to an end. This was a unique method and practical learning for me.”
What are some tips you would give someone preparing to present their Capstone project at the end of the program?
“For the presentation section, it is always important to engage the audience. One needs to narrate a story. No one is interested in what you coded or what language you have used. While technical information is important, it is critical you link the business problem to the outcome of the project. Ultimately, people appreciate the value delivered. Some presentation tips are:
- Have a clearly defined business problem to start with
- Highlight the solution approach – the various steps followed with some rational
- Highlight the key features of the data set used
- Highlight any challenges encountered & any mitigation strategy
- Finally, what is the result? Don’t stop on the accuracy of an ML model, highlight how the model adds value to the business.”
What do you enjoy the most about data science, data analytics, machine learning & AI?
“Data is the new oil. The next decade for data science jobs in Melbourne will be focused on the true value of data. With the advent of new technologies, the data space has become even more exciting. The hidden insights within data are of immense value to any business and it’s an interesting journey to be part of the data world. ”
Is there anything else you would like to add or any final thoughts?
“In conclusion, I would like to comment that a single course is not the end of the learning. One needs to be a lifelong learner in this world. So consider this course as your stepping stone in the world of data.”
If you are interested in learning more about data science jobs in Melbourne and want to boost your career prospects in 2021, schedule a consultation with a Data Industry Career Consultant today.