Building a Career in Data Sciences

The data science industry is one of the fastest-growing segments in today's tech-driven world. It is estimated to grow at a compound annual growth rate of over 26% and reach a market cap of over USD 25 billion by the year 2026.

Often key people in an organisation need to make decisions based on data available to them. If this data is inaccurate or irrelevant, it can lead to sub-optimal decisions. This is where data science plays a major role. It refers to using and preparing data for analysis. 

Businesses across the world are rapidly adopting data science technologies since it allows them to increase the efficiency of their operations. It helps businesses analyse data and find insights that can increase productivity. 

The data science industry is one of the fastest-growing segments in today's tech-driven world. It is estimated to grow at a compound annual growth rate of over 26% and reach a market cap of over USD 25 billion by the year 2026. 

The growth of the industry is also supported by the multiple applications that it has across sectors. Data science is being used in fraud detection, targeted advertising, image recognition, speech recognition, and various other fields. It has innovative applications in the healthcare, travel, and public sector businesses as well.  

There are innumerable opportunities in this field and for people looking to build a career in data sciences, now is the right time. Let us look at this industry in detail and understand the various job opportunities that exist in this field. 

Job Opportunities in Data Sciences 

There are lots of job opportunities in data sciences and they vary from business to business. However, some of the key roles, in general, include – 

  • Data Architect 

Data Architect is tasked with creating and implementing the data management framework for an organisation. They determine what kind of data needs to be collected based on the strategy that the business has adopted. 

They create database systems and work on increasing the efficiency of existing systems. Data architects work in coordination with data engineers to build systems that are suitable to the specific needs of the enterprise and will help the business achieve its goals. 

  • Data Engineer 

Data engineers can process large data sets at a great speed. They are also able to interpret unformatted and unverified data. Data engineers are always in high demand by businesses and enterprises that are tech-driven. 

Usually, data engineers are tasked with creating data pipelines so that the right data is available to the right people at the right time to enable them to make informed decisions. They create a structure (using programming languages like Python and SQL) that will facilitate the smooth and correct flow of data from one point to another. 

  • Data Analyst

A person aspiring to build a career in data sciences usually starts by being a data analyst. Businesses across industries and sectors need data analysts on an ongoing basis. Their role is key to the success of the organisation since they deal with raw data. 

Data analysts deal with raw data that is generated by the various systems that an organisation employs. They also collect data from various departments like sales, finance, production, and human resource. The data analysts then study this collected data; and, clean and sort through it. They create reports based on the data for higher authorities to consider. 

  • Data Scientist 

Data scientists try to solve real-world problems. They don’t just analyse data but also provide actionable insights to management and other stakeholders. Such insights have a multi-dimensional impact on the organisation. 

Data scientists are also often expected to be good business strategists since their insights help organisations develop a winning strategy. They play a key role in defining the company’s path to success. 

Average Salary in the Data Science Industry 

The salary of people working in the data science industry varies based on their role, responsibilities, experience, and various other factors. Experience is the main criterion in determining the salary of a data scientist. It is estimated that a data scientist gets paid an additional $2500 for every year of experience. 

The salary for a data scientist, at the start of their career, ranges from $75,000 to $90,000 per year. For mid-level data scientist, it falls from $120,000 to $175,000. Experienced data scientists can draw over $250,000 as a salary per year. 

What skills do you need for a career in data sciences?

To be a successful data scientist, you must be inclined towards tech. Learning about new technologies, and staying up-to-date with new technological advancements are just a few ways in which you can stay ahead of the competition in a field like data sciences. 

Data science requires a plethora of technical and non-technical skills, such as – 

  • Technical Skills 

You should have sufficient knowledge about programming languages like SQL, Python, Java, and C++ among various others. You should understand data visualisation and other similar techniques; plus, you should know platforms such as Hadoop and Apache Spark. Knowledge of AI and machine learning is also important. When applying for a job in this industry, a degree in data sciences would be of great help. 

  • Non-technical Skills 

One of the key skills you need apart from knowledge of tech is analytical skills. You should be able to identify trends and patterns from a given dataset. You should also have the ability to work as a team since most projects require the collaboration of various data scientists to extract meaningful information from large datasets. Communication skills will also be of great help because as a data scientist, you will often need to communicate with various people in the organisation including the sales team, investors, management, and more. 

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