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Job Scope of Data Science- Popular career options

For modern businesses operating in a highly competitive environment, data is a game-changing tool. It comes down to the usability of the large sets of data obtained from different sources in any business. With its ability to add value to data and unlock its hidden potential, data science technology emerges as a savior in these situations.

Going by the same trends, data science is turning out to be a favorable job option recently. Many students prefer to go for detailed data science training programs when entering this field. So today, let’s understand the scope of data science jobs. 

Table of Contents

  • Data science- an overview
  • Data science as a career option
  • Data science job options
  • Factors expanding the scope of data science jobs
  • Popular tools used in data science
  • Conclusion

Data Science: An overview

Data science is the diverse field of extracting actionable results from big data for strategy and business planning. The data science lifecycle includes the following five key points:

  1. Data acquisition includes receiving structured and unstructured data.
  2. Managing unstructured data includes converting unstructured data into a usable form.
  3. Creating a data training model involves the use of data classification, mining techniques, and modeling to create the model.
  4. Deploying the data model using advanced analytics techniques, regression, and predictive analysis to make predictions and develop interpretations.
  5. Data visualization includes the detailed process of reporting which is essential for informed decision-making.

Data science as a Career Option

While many people are aware of the benefits of data science like data science training in bangalore, only a few know about its potential as a career option. Data science is in huge demand currently. The US Bureau of Labor Statistics has predicted the employment rate of data scientists to increase by 36% from 2021 to 2031 only. 

Data scientists are the key points to understanding data, interpreting it, and making useful decisions be it in the private or the public sector.

Jobs in the Data Science Field 

After going through data science career options, it comes down to its job scope. Following are the various jobs available in the data science industry –

  • Research Scientist

This position uses data science methods to discover new insights and conduct scientific research in different sectors like physics, biology, finance, and medicine.

  1. Big Data Engineer

This position helps in designing, building, and maintaining the systems and infrastructure handling big data like NoSQL databases, Spark, and Hadoop.

  1. Machine Learning Engineer

This position designs and creates machine learning models and helps with quick deployment in the production environment.

  1. Business Intelligence Analyst

This position uses analysis and data to support strategic planning and decision-making within the company.

  1. Data Engineer

This position helps in building, designing, and maintaining systems and infrastructure supporting different data science processes like access, management, and storage.

  1. Data Scientist

This position is responsible for analyzing, interpreting, and collecting different data sets to identify trends and patterns. It helps to create algorithms and models for quick decision-making and predictions.

Factors Expanding the Scope of Data Science Jobs

With the quick details of the top data science job opportunities in hand, it’s time to consider the driving factors behind them. These include:

  1. Digital Marketing

The different digital marketing and social media platforms generate large data volumes. It leads to the increase in data scientists’ demand for creating new data analysis methods. Data science is commonly used to understand consumer behavior and improve personalization and targeting in digital marketing.

  1. Cybersecurity

Businesses are looking for expert data science professionals to protect their sensitive information and detect data breaches. Data scientists can employ anomaly detection techniques and advanced analytics to improve cybersecurity levels.

  1. Business Intelligence

Companies can gain an advantage over their competitors by optimizing current business operations and identifying new opportunities. It is easy to predict future trends using predictive analysis in data science.

  1. Internet of Things (IoT)

The increase in the IoT environment has increased the data. Hence, data scientists may need to use new methods for collecting, processing, and analyzing data for actionable insights.

  1. Machine Learning

Data scientists can now create accurate models, automate different tasks, and improve predictions using machine learning.

  1. Big Data and Cloud Computing

Using big data and cloud computing technologies, data scientists can improve decision-making capabilities and extract valuable insights from large data volumes.

Popular Tools Used in Data Science

Data science necessitates detailed knowledge and application capabilities in some tools, such as the ones listed below:

  • Hadoop

It is an open-source framework for storing and processing large data sets over computer clusters.

  • Python

It is a high-level programming language for data science, scientific computing, and machine learning.

  • R

It is the statistical programming language for data analysis and visualization.

  • SQL

It is the standard language for managing and querying relational databases.

  • Tableau

It is the data visualization tool used for creative reports and interactive dashboards.


Let’s look at the key takeaways of this blog –

  • The demand for data science is increasing over the years. It has increased the demand for creating valuable results from large incoming data sets.
  • Data science provides a variety of career options depending on the aspirants’ interests.
  • Factors like digital marketing, cyber security, the IoT, and machine learning are driving the demand for data science.
  • The popular tools necessary to start a career in data science are Hadoop, Python, R, SQL, and Tableau.



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