With the fast-growing landscape of data across industries and the need to process and handle huge volumes of data every day, data science is becoming one of the most in-demand careers in today's time. Organizations from different industries look for data experts to take care of various tasks as they produce voluminous business data.
A lot has been said about why Big Data is here to stay. The demand for data science professionals has been surging, making it a highly sought-after career opportunity for aspirants. In this post, we look at some of the best career options in data science and try to understand how you can make the most out of the World of Big Data. Here is a data science careers guide for your to consider.
Why Pursue a Career in Data Science?
The popularity of Big Data is gaining rapid momentum in this world that relies a lot upon machines and data. One should have better knowledge and understanding of data to be able to deliver useful insights and leverage its potential. This is where data science professionals can play a role by handling tasks like data mining, data analysis, business intelligence and predictive analytics.
Data science is considered to be a highly lucrative job at this time. With a large number of openings across sectors, data science jobs are only growing. As companies look for adopting data science in their operations, millions of new job opportunities exist in the world of Big Data, making it a hot career choice.
As the wave shows no signs of slowing down, companies want to hire professionals to do their Big Data right. The shortage of skilled professionals in a world that relies largely on data for decision-making creates a huge demand for data science experts in start-ups as well as established organizations.
Who is a Data Science Professional?
Professionals in the discipline of data science are broadly termed as 'data scientists' and cover business intelligence experts, data visualizers, analysts and everybody else dealing with data in an organization. As data science is now being integrated into the business operations across sectors, a data scientist should have a broader set of skills to be able to serve the employer as expected.
Data scientists analyse data, solve problems and devise insights into how the business can achieve an objective. They take a cohesive approach and work across areas like statistics, programming, data intuition, machine learning, linear algebra and software engineering. They work with raw data at hand, organize and analyse it and create and implement algorithms to come up with visualizations.
Career Options in Data Science
Aspirants interested in making a career in data science have several options to choose from. Let us discuss some of the most prominent data science designations and roles and try to trace the career path for them.
A highly trained data science professional is often called a data scientist. Their job involves processing huge amounts of data and transforming them into useful insights for the organization.
A data scientist proves to be a significant addition to a company as he provides information it requires to make important decisions. Data scientists are employed across industries and it is easier to find a job for this position as compared to other careers.
As a data scientist, you analyse the data to get useful insights and present them to others in the organization. You should do this in such a way that people without any technical knowledge can understand it well.
These professionals should possess skills in the areas of statistics, computer science, modelling, math and analytics. They also need a high degree of business sense depending on the organization's goals.
The position of a data scientist is generally higher than some other data science professionals. They are proficient in programming languages like R, Python and SQL. A data scientist is responsible for creating data processes used for the design of data models and algorithms. They often work with stakeholders to determine how data should be utilized to accomplish business goals and objectives.
A data science role that requires more technical knowledge, data analysts are also called business analysts. These professionals not only take care of data analysis but also data interpretation. Such a combination of skills makes this role significant for any organization in their decision-making. Data analysts are often hired to discover new opportunities for cost reduction and revenue increase.
Data analysts utilize different methods in their data collection and analysis. They gather raw data and transform it into information people can understand and use for the organization's benefit. These professionals collaborate with managers to prioritize information requirements, identify and understand trend patterns found within data sets and design strategies for optimized results.
To get the position of a data analyst, you should get a bachelor's degree in data science or data analytics. Some organizations prefer master's as well. You can take an entry-level job in data science to prepare for the position of a data analyst in a reputed organization.
Data analysts should possess knowledge of Python, Microsoft Excel, SQL and other languages. A data analyst handles different projects from time to time. He may work in the production department for a few months before being shifted to marketing.
These professionals possess a much better understanding of the business as compared to data scientists. They are responsible for the flow of data and processes and even handle the co-ordination of people wherever necessary.
A proficient data manager must be knowledgeable in areas like modelling and architecture, business intelligence, data governance, storage and operations, data quality and management of document data, master data and reference data.
A data manager is in charge of the data of a specific domain or sometimes an entire department or even an enterprise. As a data manager, you are responsible for the data integrity across the business processes and should make sure people who need it can access it efficiently.
The professionals responsible for the design, implementation and management of a company's data architecture, data architects hold a more senior position than other data scientists. This title is generally not given to entry-level positions.
If you aim to become a data architect, you should get a master's degree in computer science or data science. You can also apply for this position after 3-5 years of experience in data programming or administration, data modelling, database design and data management.
Data architects get roles in industries like business, education, insurance and finance. Technology and software are two of the biggest employers of these professionals. They are quite prominent in organizations that handle huge volumes of client data. As a data architect, you develop architecture, construct, test and maintain it. The architecture can even include large-scale databases and systems.
Considered to be a backbone of any well-established organization, a data engineer works at a more basic level as compared to other data science professionals. These professionals work with raw data and make it ready for data scientists for processing.
Companies generally hire data engineers to benefit from software development. These professionals should have proficiency in programming languages like Python, SQL, C++, Java and more.
This role demands a good understanding and extensive knowledge of programming. A data engineer, in most companies, creates data pipelines and corrects the flow of data within the organization to make sure the most relevant information reaches different departments.
These professionals work with data prone to errors and lack of validation. This type of data is more challenging to work with as it is not formatted and includes codes specific to systems.
Data engineers are experts in the field of data science and are capable of devising innovative methods for the storage and access of huge volumes of data. They design tools to make business data interpretation easier.
They are generally hired in the IT departments of businesses and technology companies. One of the most prominent goals of these professionals is to improve the efficiency of a business and help it accomplish its objectives more effectively.
Business Intelligence Developer
A business intelligence developer in an organization has a firm grasp of data analytics as well as software engineering. This type of professional is also referred to by roles like 'Machine Learning Engineer' or 'System Analyst'. The goals and tasks of this professional are different than those of a data scientist.
A business intelligence developer is responsible for developing working software. Some of the most important skills you need for this role include software engineering, system design, machine learning algorithms, data evaluation, statistics, computer programming, computer science and more.
As a business intelligence developer, you develop artificial intelligence systems and need advanced knowledge of machine learning techniques.
The upward swing in career prospects for data science professionals is likely to continue for a long time as businesses rely more and more on data.
Skilled data scientists will be continuously hired by small and big businesses and even government agencies.
With such a big potential, it is high time to up-skill in Big Data and Data Science to leverage the career opportunities coming your way.