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Multiple job openings in the market require individuals to have data science-specific skills. However, the confusing part is that data science has a massive background that sometimes overlaps with other job descriptions. This creates confusion in an applicant’s mind about whether they have the required skills and qualifications or not.

This informational blog aims to provide the necessary information about various job roles in data science.

Data Science- Overview

Data science jobs are one of the most demanding careers in today’s era. The career is lucrative and rewarding, but the path to an established data science professional is not simple. It is not about having a master’s or bachelor’s degree; the career demands more than this. It involves various skills and expertise in numerous aspects, including business, technical, mathematics, reasoning, etc. Therefore, to inbuilt a relevant skill set, data science aspirants are recommended to know about data science course before getting enrolled.

Skills Required to Start With a Data Science Career

Data science skills depend on numerous factors; however, they may change according to the job requirements. But below are the common skills that are non-negotiable.


  • Collecting, cleaning, organizing, and manipulating data.
  • Coding, including R, Python, SQL, C++, and Java.
  • Using BI tools such as Power BI, Tableau, and Looker to visualize data.
  • Database modeling- understanding of the working databases.
  • Having statistical and analytical knowledge to gain insights.
  • Having mathematical knowledge to perform data analysis and metrics calculations.

Popular Job Roles in Data Science

As modern technology has allowed us to create and store increasing amounts of information, the volume of data is exploding. But this stored data can help many companies and societies around the globe if they know how to use it properly. That’s where data science comes into existence. For this reason, companies are recruiting data science specialists, for transforming knowledge and information into action, thereby leading to data scientist jobs.

Some remarkable job roles in data science are discussed below: 

Data scientists – These are the data science professionals who help companies by suggesting the best solutions to business challenges using data processing and analysis. The responsibilities of a data scientist are:

  1. Determining data collection sources for business purposes.
  2. Managing and automating data collection.
  3. Integrating, processing, and analyzing data.
  4. Using data science methodologies or tools for improvement processes.
  5. Collaborating with other engineering, product, or business teams.

Analytics and Data Managers – These professionals regulate the data science operations and assign tasks to the team according to expertise and skills. Their responsibilities include:

  1. Working according to industry trends and news.
  2. Making data analysis strategies.
  3. Leading a team of data analysts.
  4. Implementing analytics solutions.
  5. Ensuring quality by regulating data operations.

Business Analysts – Business analysts know how to use big data for the company’s growth. They can handle large amounts of data and separate low-value data from high-value data. The responsibilities of business analysts include-

  1. Pricing analysis
  2. Forecasting and budgeting
  3. Improving business processes.
  4. Designing, implementing and analyzing new systems and technology.
  5. Understanding organizational business.

Statisticians – Statisticians have the basic knowledge of data organization and statistical theories. They help engineers by creating new techniques. Their roles and responsibilities include-

  1. Synchronizing with cross-functional teams.
  2. Developing data collection processes.
  3. Predicting trends using statistical tools.
  4. Collecting and interpreting data.
  5. Advising on business strategy.

Data Architects – They are responsible for creating data management blueprints so that databases can easily be integrated and protected with the best security systems. They create effective systems and tools for data engineers. Data architects’ responsibilities include-

  1. Creating and executing data strategies.
  2. Recognizing data collection according to data strategy.
  3. Managing and planning data architecture.
  4. Ensuring the efficiency of database architecture.
  5. Working with stakeholders for better functioning of database systems.

Machine Learning Engineers – The job of machine learning engineers is to execute machine learning algorithms, develop data pipelines and perform A/B testing. The significant responsibilities of machine learning engineers are as follows:

  1. Designing and building machine learning systems.
  2. Exploring machine learning algorithms.
  3. Checking machine learning systems.
  4. Creating apps according to the needs of the clients.
  5. Visualizing and researching data for a better understanding.

Database Administrators – Database administrators ensure the proper functioning of the databases. They are responsible for database recoveries and backups. Their major responsibilities are:

  1. Executing database security measures.
  2. Managing and storing data by working on database software.
  3. Regulating manuals and developing documentation and reports.
  4. Data archiving.
  5. Designing database.

Data Engineers – They are responsible for developing and testing big data ecosystems for the companies. They also upgrade the existing systems to enhance the efficiency of the databases. Their responsibilities include-

  1. Working with other teams to complete organizational targets.
  2. Updating stakeholders by making reports.
  3. Predicting trends using data.
  4. Designing data management systems.
  5. Managing data collection.

Data Analysts – Data analysts do a variety of tasks including, visualization and processing large amounts of data. They have to solve the database issues timely. Their responsibilities are:

  1. Managing and creating databases.
  2. Predicting and analyzing data.
  3. Creating reports with recommendations.
  4. Taking out data from primary and secondary sources using automatic tools.
  5. Teaming up with other team members to improve data collection.

Data Storyteller – It is the newest job position in data science. The role is often confused with data visualization, but there’s a significant difference. Their responsibility is not limited to making stats and reports and visualizing data; it is about making a digestive narrative for data expression. Their responsibilities include;

  1. Finding the best way to describe data.
  2. Using different ways to express data.
  3. Simplifying data and focussing it on a particular aspect.
  4. Analyzing data behavior.
  5. Creating an interesting story to help comprehend data.

Business Intelligence Developers – These developers are also called BI developers. Their work is based on business orientation; hence, they need to have basic knowledge of business model fundamentals and how to implement them. Their responsibilities are;

  1. Developing and designing effective business plans and strategies.
  2. Finding ways to make quick business-related decisions.
  3. Using BI tools that help understand an organization’s systems. 

Data Science Career Path

There is no one way to start a career in data science. It depends on skills, education and relevant work experience. However, many people begin with data analysts. After this, they either choose to work with data infrastructure or data analysis, depending on their skills and interests.

Below is the stepwise procedure to become a professional in data science.

  • Get a bachelor’s degree in computer science, data analytics, engineering, data science, mathematics, or any other equivalent certification.
  • Make a data science foundation. This involves working on DIY projects, open-source works, or volunteering.
  • Get a master’s degree in data science (if required).
  • Work on networking. Connect with professionals in the data science field to get suggestions or any other help.
  • Go for an internship and build work experience.
  • Enroll in the best data science Bootcamp, or certification training programs to stay updated with the latest trends.

Conclusion

Data science continues to be a promising career for people interested in computer science, data engineering, or any other tech skill. It comes with numerous advantages both in terms of personal and professional life. It provides job satisfaction, a lucrative salary, and multiple career opportunities. Further, it enables an individual to learn new skills, which help in overall growth and development. A career in data science is a complete package of excitement, personal growth, challenges, and professional benefits.

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