Top 10 data science careers that are shaping the future
With the exponential rise in generation of data, almost every organisation today needs skilled data scientists who can analyse and interpret the data in a way that helps the organisations prediction future scenarios
One of the biggest by-products of today’s connected world is data. Every time someone is logging into social media platforms, buying something from e-commerce websites, or rating a café, they are creating data. “Data is the new oil”, this saying captures the importance of this new commodity that is powering the digital world. But all this data would be futile if it’s not interpreted and analysed for more information. That’s where data science and data scientists come in. Data science uses various tools, algorithms and machine learning principles to anatomize the raw data and make decisions and predictions based on the analysis.
With the exponential rise in generation of data, almost every organisation today needs skilled data scientists who can analyse and interpret the data in a way that helps the organisations prediction future scenarios. Data scientists are also responsible for building the various processes and algorithms used to collect, process, store and optimize the data. A recent Mint article showed, “The demand for data science professionals is at an all-time high”. As of 2019, the Indian analytics market stood at $3.03 billion in size and is expected to double by 2025. So this is an exciting time to be a data scientist and to choose from a range of options to chart a successful career.
Few of the lucrative options available today as a carrier options are:
Be it healthcare, sales, Logistics, Power or technology — almost every industry today relies on data analysts to convert large data sets into a suitable format and analyse the data. Data analysts play a crucial role in an organisation as the analysed data help in the decision-making process. Data analysts also ensure all the programs and systems are running efficiently and at an optimised level.
It is a more technical position than a data analyst. A data scientist’s role is to design the methods to gather, store and analyse large sets of structured and unstructured data to help companies make strategic decisions. Data scientists combine computer science, statistics and mathematics to analyse, process and model data. They also use data analysis software to uncover patterns and trends in the data and predict market patterns.
Data engineers create and maintain database architectures and frameworks. It’s the responsibility of data engineers to understand what the particular business wants to achieve from the company datasets and then develop algorithms to provide easier access to the raw data for data scientists. They also have the task of optimising the data retrieval process along with creating dashboards, reports and other data visualisations for the various company stakeholders.
Business Intelligence Analyst:
Business Intelligence (BI) analysts are the creators of efficient business models and strategic plans for businesses. They define KPIs (Key Performance Indicators) and implement DW (Data Warehouse) strategies. They also mine big data by using advanced software, use tools to identify business intelligence and thereby aid in better business decisions.
As the name suggests, data architects are the masterminds behind building the database framework for any company. They design, create and launch a company’s overall database architecture depending on business requirements. Data architects are also responsible for maintaining the company’s data ecosystem. It’s one of the most popular and highly paid jobs worldwide as well as in India.
A company may hire a statistician or statistics analyst for gathering and analysing data and presenting it in a non-technical way to the stakeholders. The findings and insights are used to make critical business decisions. They also predict and identify potential opportunities based on the data analysis.
Organisations need specialists who can identify the right IT technology for data analysis that suits the company’s business strategy. That’s where enterprise architects come into play. They are the technological backbone of any company responsible for setting up the proper IT architecture models to achieve company goals. They also maintain the IT frameworks and stitch together a company’s data science, goals, and IT systems.
Infrastructure architects check the various databases, applications and software for efficiency and ensure they are functioning properly. They also make sure that a firm has the necessary tools for analysing big data. They are the people who detect any failure or inefficiency in the system.
Machine Learning Engineers:
Machine learning engineers create algorithms needed for data analysis. They use big data tools and programming frameworks to refine the raw data that is being captured through data pipelines. They are responsible for creating data funnels and data science models that can ingest vast amounts of data in real-time and generate accurate results. Machine learning engineers need to have skills in both software engineering and data science. Additionally, they also need to know technologies such as deep learning, artificial intelligence, etc.
They are the ones who build various applications for companies. Almost every organisation needs applications and user interfaces to run its business efficiently. Hence, firms need application architects who can choose the right applications or build them specific to company needs. They also track the functioning of various applications and user interactions within the firm.
Data science can create a smarter future decision making and also address critical issues such as improving healthcare through wearable devices or enhance the accuracy of diagnostics in the medical field. With a wide range of applications, data science is playing a critical role in every industry. UPES is one of the premier institutions offering a degree course on data science. Along with learning about the data science tools and machine learning algorithms, students at UPES will also be part of an innovative and future-oriented learning environment.
(The writer is Assistant Professor, School of Computer Science)