Data has emerged as the currency of the world. Industries, governments, and societies depend on insights from data to set their agendas. Thus, the demand for data scientists is also growing across the board.
In simple terms, a data scientist identifies trends; analyses, manages, and extracts actionable insights from data; and arrives at conclusions to drive decision making. Data science is an interdisciplinary field, and requires an aspirant to have knowledge of mathematics, statistics and probability, calculus, and other optimisation methods. Though, in the long run, having the technical chops alone might prove insufficient to grow in a highly-dynamic data field. To wit, curiosity, problem-solving skills, and persistence go a long way in forging a rewarding data science career.
Below, we explore a few telling signs to check if you are made for a career in data science.
Interestingly, not all data scientists come from an engineering background. Candidates have backgrounds as diverse as economics, mathematics, and statistics. What matters, at least to start with, is an analytical bent of mind. In data science, you can pursue various trajectories, including data visualization specialist, management analyst, business developer, data analyst, and market research analyst depending on your aptitude.
People from industries such as banking and finance, climate, physics, and even arts use data science. Farsighted professionals adding data science skills as another string in their bow have become prevalent of late.
Though the field is background-agnostic, you still need technical skills and a common language to work with data. Meaning, sound knowledge of programming languages such as Python, R, or SAS, is critical for a data worker. Among the languages, Python is the darling of programmers, thanks to its user-friendliness, a large set of libraries, and strong community support.
Other technical skills include deeper knowledge of algorithms, GPU and CUDA, command on visualisation tools such as Tableau and Microsoft BI, and expertise in Hadoop.
Data science projects typically involve stages such as problem framing, data collection and analysis, model building, testing and evaluation, and real-world deployment of models. The kind of problems a data scientist deals with on a regular basis is very challenging. Depending on the scale and complexity of the project at hand, model building and application may take days, weeks, and even months to complete. Only a person with patience and passion for solving challenging problems would do well as a data scientist.
“As a part of a Data Science team, your role will be constantly around problem-solving. The ability to think scientifically, work with cross-functional teams, implement fast and iterate several times in addition to good communication skills helps one perform well in the field of Data Science,” said Hari Krishnan Nair, Co-Founder, Great Learning.
Technical know-how is the starting point for a career in data science. To make a mark as a data scientist, one needs to have good business acumen. The end objective of working and analysing data is to drive business. It is therefore essential for a candidate to understand crucial business pain points, recognise customer needs and use them to deliver business solutions.
“Data scientists are not only required to learn programming languages, database management, and how to convert data into visualizations, but through an analytical lens, they should be naturally curious about their surrounding environment. Data scientists can be meticulous with personality attributes that mimic quality assurance departments as they analyse vast volumes of information and look for trends and answers,” said Dr Dileep Kumar Singh, Head, School of Engineering and Technology, Jagran Lakecity University.
Curiosity is a personality trait, when combined with data science, can help professionals look beyond the surface and discover deeper patterns and anomalies in the data. Out of the box thinking is a prerequisite to create breakthroughs in data science.
Persistence is a great quality to have in general, but with data science, the RoI is even monumental. As already mentioned, the lifecycle of a data science project can be tediously long. From problem framing to final deployment, the path is long and winding. In such cases, qualities such as patience, persistence, and tenacity keep a data scientist on track.
“It started with designing data parsing pipelines in Pandas. That was my introduction to the field of machine learning and data science. As I started working on related problems, I steered to become a full-time data scientist,” said Anurag Upadhyaya, Product Manager (Data Science), AIM. He also said anyone can become a data scientist, provided he/she practices the right skills in a hands-on manner.
“I chose Data Science as a career because I am a product engineer at heart. I love how Machine learning revolutionised the way software products are built by giving us the ability to provide tailored customer experiences. I am also excited with the opportunity to use the power of Data Science to leverage historical data to train machines and manage repetitive tasks thereby providing better efficiencies to businesses,” said Deviprasad Thrivikraman, Head – Data Science Lab at Matrimony.com.
Bangalore-based data scientist Pallavi Dasgupta said, “I decided to pursue data science as a career because I had an inclination towards unfolding secrets using data! I’ve always believed in looking at data facts and taking decisions to back up any business decision. With the current booming scenario of data science, a lot of businesses have gained a lot of insight about their business weak points which can be tackled using brilliant machine learning techniques! I have always liked to be at the intersection of Maths, Statistics, and Technology to solve real-world problems, which is now possible with Data Science.
“If you’re good at Maths, have a logical approach in solving problems, love playing with numbers, you should definitely look up Data science as a career. It definitely gives you a power, an upper hand of maths and statistics to establish and back up your assumptions and make decisions for your business.”