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How to Become a Data Scientist from Hitesh Nahata: MiQ's '40 Under 40 Data Scientist 2023'

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.

Question 3: For those who feel overwhelmed by the technical aspects of data science, how would you suggest they get started?

Nahata: Here are some strategies to ease into the field without getting discouraged:

• Start Simple: Begin by learning the basics — like understanding what data is, how it’s collected, stored, and accessed. Start with easy-to-use tools, like Microsoft Excel, where you can look at data and experiment with it.
• Pick Fun Projects: Choose small projects related to things you love, like music, sports, news, or gaming. There are lots of beginner-friendly projects and data files available on websites like Kaggle, DataQuest, or GeeksforGeeks. This way, you’re learning while working on something you enjoy!
• Learn a Little Every Day: Sign up for self-paced courses.  These can range from super easy to more advanced levels. Just set aside 30 minutes each day to learn something new, and take it one step at a time. 

Question 4: What core technical skills (like programming languages, statistical tools, AI etc) should students prioritise when building a foundation in this field?

Nahata: To build a strong foundation in data science, here are the core skills students should focus on, in order:

• Start with Microsoft Excel: Begin by learning how to work with small data files using Excel. It’s a simple tool to understand the basics of data.

• Learn SQL: Once you're comfortable with Excel, learn SQL to handle larger sets of data and manage databases effectively.

• Move on to Python: Python is a powerful programming language used widely in data science. Start with the basics to learn how to handle and explore data.

• Explore Data Visualisation: Understand how to create graphs and charts using Python libraries or tools like Tableau, which makes it easier to see patterns and trends in the data.

• Dive into Machine Learning: Once you’re comfortable with the basics, start learning about machine learning using tools like Scikit-learn, focusing on core algorithms and how they work.

Question 5: Given the rapid evolution of technologies, which tools, platforms, or programming languages should students focus on mastering? How crucial are cloud computing, AI, and machine learning skills?

Nahata: To build a strong foundation in data science, students should focus on the following core skills:

• Programming Languages: Python is the most important language for data science, AI, and machine learning. It’s easy to learn and widely used.
• SQL: Essential for managing and working with large databases.
• Data Analysis and Visualisation: Tableau or Power BI tools that help create interactive charts, graphs, and dashboards to better understand data.
• Machine Learning: Scikit-learn (Python) is a tool that helps build simple machine learning models.
• TensorFlow or PyTorch (Python): Used for more advanced AI and deep learning projects.
• Cloud Computing:  AWS, Google Cloud, or Azure are platforms that help store large amounts of data and make it easier to use machine learning models on a bigger scale.
• Big Data: Apache Hadoop or Spark are tools for handling and analysing very large datasets.

Question 6: In 2024, how much importance do employers place on formal education (degrees, certifications) compared to practical skills and hands-on projects?

Nahata: In 2024, having a formal education (like a degree or certification) is still important, especially for learning the basics and qualifying for entry-level jobs. However, practical skills and hands-on experience are becoming even more important.

Employers often prefer candidates who can show they know how to solve real-world problems and have done actual projects. These practical skills can make you stand out from other candidates who may have similar degrees.

Question 7: Can candidates break into data science through alternative pathways like certifications, bootcamps, and online courses?

Nahata: Yes, candidates can start a career in data science through alternative pathways like certifications, bootcamps, and online courses. While many formal programmes now offer specialisations in data science, those who haven't taken these programmes can still enter the field by upskilling themselves. However, they will need to be more active and focused when looking for the right job to start their careers.

Here are some popular programmes and platforms:

• Top engineering and business schools in India, like the IITs and IIMs, offer various short to medium-term certifications in data science.
• Online platforms also provide specialised data science courses and bootcamps.
• A certification or distance learning programme from a recognised institute or university can be more valuable when looking for job opportunities.

Question 8: A portfolio is often the gateway to landing a job. How should candidates build a portfolio that stands out?

Nahata: A portfolio is like a collection of your best work that helps you get noticed by employers. Here’s how to build one that stands out:

• Create and Share Projects: Work on different data science projects and share them on platforms like GitHub. This shows what you’ve learned and your ability to solve real problems.
• Join Competitions: Participate in online data science competitions on websites like Kaggle. This will help you gain experience and learn from others.
• Enter Hackathons: Join hackathons, which are events where you solve problems in a limited time. Many are organised by online learning platforms and tech companies.
• Freelance Projects: Take on small freelance projects from websites like 'Freelancer' to gain experience working on real-life problems.

Question 9: Any specific types of projects or challenges you’d advise them to tackle?

Nahata: When building your data science skills, here’s what to focus on:

• Start with Basics: Begin with simple projects that help you understand data cleaning and exploring. These steps might seem boring but are very important in data science.
• Work on Real Problems: Choose projects that solve real-life problems. For example, analyse data from your favourite sports team or study trends in your favourite music.
• Understand the Why: Before jumping into advanced techniques, make sure you understand the basic concepts and reasons behind them. This helps you use them correctly.
• Learn Both Technical and Business Skills: Besides technical skills, try to understand how data science applies to different businesses or industries. This will make you more versatile.
• Stay Grounded: Instead of focusing on the latest trends or fancy tools, make sure you understand how and when to use different techniques effectively.

Question 10: How can parents and educators actively support a student interested in this field, especially in terms of resources and exposure?

Nahata: Parents and educators can play a crucial role in supporting a student interested in data science by providing the right resources, exposure, and encouragement. Here’s how they can help:

• Building Excitement: Show students how data science is used in real life, like in their favourite apps or games. Share success stories of people who have made it in the field.
• Strengthening Skills: Help students develop their problem-solving and critical thinking skills, which are important in data science.
• Finding Mentors: Connect students with professionals who can offer advice and guidance.
• Encouraging Practice: Support students in doing hands-on projects and learning more about data science through online resources or local clubs.
  

Question 11: With automation and AI advancing rapidly, do you see data science jobs evolving or being at risk? How can students future-proof their careers in this field?

Nahata: I definitely see data science jobs evolving. As AI and automation grow, data science jobs will definitely change. Here’s how students can stay ahead and future-proof their careers:

•  Embrace Change: Understand that automation will handle some tasks, but this opens up opportunities to tackle more complex and exciting projects.
• Keep Learning: Continuously update your skills. Know the latest tools and techniques, but also focus on understanding how to apply them effectively to real-world problems.
• Understand the Business: Learn how data science and AI can solve problems in different industries. This will help you adapt and find new ways to apply your skills.

By staying adaptable and always learning, students can ensure they remain valuable in the evolving field of data science.

Therefore, in a world where data drives decisions and innovations, the ability to navigate and master this realm as a data scientist is a great career choice for the future belongs to those who can harness its power with skill and vision. 

In fact, interested candidates from non-STEM domains can also upskill and build a career in the overarching Data Science domain aided with the right guidance and knowledge.

So stay tuned, stay curious, and remember: the next great breakthrough could be just a wave away!

Whether you're a student navigating your academic journey, a parent supporting your child's education, or an educator shaping future minds, if there’s something on your mind, we would love to know. Share your views, feedback, and suggestions with us at editorial@tt-edugraph.com.

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