Imagine standing at the edge of a vast, shimmering ocean where every wave represents a piece of data—an endless expanse of numbers, trends, and insights. In our modern world, we are all surfers on this ocean of information, riding the waves of data that shape our lives, from the apps on our phones to the decisions made by businesses and governments.
Welcome to the world of data science, where curiosity meets the power of numbers! In this era, data is not just a buzzword but the lifeblood of innovation and decision-making.
Whether you’re predicting business trends, analysing sports statistics, uncovering patterns in your favourite music, or developing the next big tech solution, mastering data science is the key to unlocking endless possibilities.
Over a decade ago, Thomas H Davenport and DJ Patil dubbed the role of a ‘Data Scientist’ as the “Sexiest Job of the 21st Century” in their groundbreaking Harvard Business Review (2012) article. Fast forward to 2024; while ‘sexiness’ might be subjective, there’s no question that data science is one of the most sought after and impactful careers in this data-driven age.
So are you an aspiring data scientist?
The Telegraph Online Edugraph brings you exclusive insights from Hitesh Nahata, the Director of Data Science & Analytics at MiQ; a trailblazer named among the ‘40 Under 40 Data Scientists’ at the Machine Learning Developers’ Summit in 2023. Moreover, under Nahata’s leadership, MiQ was recognised as the ‘Best Firm for Data Scientists’ by Analytics India Magazine recently.
In this masterclass, Nahata reveals 11 real-world strategies that will help you build a successful career in data science.
Question 1: Data science has evolved significantly over the years. In 2024, what new dimensions or shifts are you noticing in the field?
Nahata: In 2024, data science continues to evolve rapidly, with several key dimensions and shifts emerging:
- AutoML and MLOPs: The process of building multiple computer programmes called “models” at scale without really the need for hardcore coding and putting them into a scaled system where they can be better managed, improved and used for a large variety of use-cases
- AI for Everyone: With the availability of ChatGPT and other generative AI platforms there is a lot of focus on building applications on top of these systems with low/no code tools to quickly build solutions which are very specific to the business problems that the companies are trying to solve
Question 2: The role of a data scientist is often described as multidisciplinary. How would you break down this role into core functions, in a way that is comprehensible for young students aspiring to become data scientists?
Nahata: The role of a data scientist can be thought of as having three main parts:
- Data Pre-processing and Exploration: Imagine you have a messy room, and you need to clean it up before you can find anything useful. This is similar to what data scientists do with data — they clean it up by getting rid of errors or missing pieces. Then, they explore the data to find patterns or trends, which helps them decide what kind of model or “recipe” to use to solve a problem.
- Model Building and Validation: Once the data is ready, data scientists create a model. Think of a model as a tool or a computer programme/ system that can make predictions about the future by learning from past data. After building this model, they check carefully to see if its predictions are accurate, just like testing a science experiment to see if it works in real life.
- Visualisation and Storytelling: Finally, data scientists need to share their findings with others. This involves creating graphs, charts, or stories that make the data easy to understand. It’s like taking all the information and turning it into a story that helps people see what’s important, understand the model, and trust the predictions it makes.