Everything You Need To Know About Data Science as a Career

By Indeed Editorial Team

Updated October 23, 2022 | Published July 26, 2021

Updated October 23, 2022

Published July 26, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Companies rely on data to understand market trends and provide value to their customers. As a data science professional, you can help companies leverage data to optimize their business and reach their goals. However, you may need some hard and soft skills to begin a successful career in data science. In this article, we discuss what data science is, share the most common job duties for data science professionals, explore the benefits of working in this career field, and provide steps to help you start a successful career in data science.

What is data science?

Data science involves interpreting data. People who work in data science may extract, store, sort, and analyze data to obtain valuable information. Most businesses gather data through their daily operations. This can include data on consumer behaviour, demographics, and spending power.

These pieces of data may fall under the structured or unstructured category, depending on how you obtain them. Structured data refers to precise data that you store in a defined format, while unstructured data refers to a group of various data types stored in their native format. A data scientist collects, organizes, and interprets the data to provide businesses with insight into the market they interact with. This helps companies make smarter, data-driven decisions.

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What are the job duties of a data scientist?

Data scientists obtain and manage data for businesses to help them improve their operations. Here are some of the most common job duties of a data scientist:

  • Collecting data from various sources, such as business records and market news

  • Collating and organizing data into new formats to make analysis easier

  • Building programs that automate data collection and organization

  • Extracting valuable information and insights

  • Analyzing data to find and record patterns

  • Preparing detailed reports that can influence executive decisions

Benefits of choosing a career in data science

A career in data science provides various opportunities to grow and develop as a professional. Here are some reasons you may consider a career in data science:

It's in high demand

Companies strive to stay ahead of their competition. To do this, they often use data to help them make important decisions and improve their processes. Many companies rely on data scientists to help them collect and interpret data to optimize their operations.

It pays well

Data science professionals often work in high-paying jobs. A data scientist makes an average salary of $84,005 per year . The amount you earn may vary depending on the industry you work in, but typically increases as you gain experience and develop your skills.

It's a prestigious career

Companies rely heavily on data scientists to make informed business decisions. A career in data science may help you achieve an essential and respected role within your organization. If you want to be a core part of a company's decision-making process, consider a career in data science.

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It involves automation

Data science often involves using programming and machine learning to create automated processes that eliminate redundant tasks. If you enjoy making repetitive processes more efficient, then a career in data science may be for you. You can use automation to help companies improve productivity and cost-effectiveness across various departments.

It's versatile

Data science is a versatile field relevant in almost every industry. Pursuing a career in data science can offer you a wide variety of opportunities to work in several sectors, such as health, finance, and insurance. There are also various areas of specialization you can work in, including:

  • Data warehousing and data engineering

  • Data mining

  • Database management

  • Cloud and distributed computing

  • Business intelligence and strategy

  • Data visualization

Careers in data science

Here are some common career paths you can explore in data science:

Database administrator

National average salary: $53,911 per year

Primary duties: Database administrators ensure efficient access to company data. They're responsible for creating new datasets, such as new employee registration. Database administrators usually work within the IT department to ensure the security of databases by creating alternative access options and backups in case a system failure occurs.

Computer systems analyst

National average salary: $69,961 per year

Primary duties: Computer system analysts ensure that a company's computer systems function effectively. They run tests to maintain, change, and develop computer systems to ensure they align with a business' needs. Computer systems analysts typically use database programs to optimize computer systems.

Data analyst

National average salary: $73,761 per year

Primary duties: Data analysts collate, organize and process available data to solve business problems. A data analyst uses computerized processes and calculations to analyze data and provide feedback to companies to optimize their strategies and operations. This is usually an entry-level position for those who want to gain specialization within the field of data science.

Data scientist

National average salary: $84,005 per year

Primary duties: Data scientists build data products and create algorithms from raw data that help companies understand their customers and the market. They also build machine-learning models that analyze data to make business predictions. These precise predictions help companies develop long-term business strategies.

Data engineer

National average salary: $110,294 per year

Primary duties: Data engineers concern themselves with the infrastructure of data analysis. Data engineers create, manage, and operate the databases and processing systems that data scientists and analysts use. They also build the structures they use to collect and transform data into usable formats. This role requires a fundamental knowledge of SQL and various programming languages.

How to start a career in data science

Starting a career in data science requires you to fulfill some educational requirements. You can follow these steps to start your career:

1. Choose the right role

Consider your interests and skill set before choosing a role. For instance, if you're interested in optimizing business performance, you might want to specialize in business intelligence and strategy. Choosing a role that interests you can help you create a plan to pursue the relevant academic coursework you may need to be successful. For example, you might take management and core data science courses if your interest is business intelligence and strategy. This helps you focus your attention on the most beneficial courses to help you hone your skills in your preferred area of expertise.

2. Obtain the necessary education

Most careers in data science require you to have an undergraduate degree in data science or a related field, such as statistics, mathematics, physics, or computer science. You can also complete certifications and online courses that may prepare you for a career as a data scientist. While it is optional, earning a master's degree in data science can help you gain advanced knowledge of programming languages and machine learning. This can set you up for success in your career.

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3. Get an entry-level job

Getting an entry-level job is an important step that can help you gain hands-on experience in data science. You reach out to people in your network or search professional job listing sites to look for job openings. Many data science jobs are remote, providing you with more opportunities to apply for open positions. Once you find an opening, research the company, craft a well-written resume, and prepare for your interview.

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Data science skills

Successful data science professionals have hard skills, such as technical knowledge, and soft skills, such as strong communication. Having both types of skills can help you show employers you're a well-rounded candidate. Here are some hard and soft skills many data science professionals have:

Hard skills

Data scientists work with several software tools and programming languages that require hard skills. Here are some of the hard skills that can help you succeed as a data science professional:

  • Data visualization: Data visualization is the process of translating data into formats that people can easily understand. Data scientists use data visualization tools like SAS, the R libraries, and Tableau.

  • Programming skills: Data scientists need solid foundational knowledge of programming languages such as Python and C++. These languages are essential for analyzing data and creating automated processes.

  • Machine learning: Although optional, machine learning can help set you apart from other job candidates. This skill involves automating analytical model building and can make you more attractive to companies that operate with large amounts of data.

  • Statistics: Having a strong knowledge of mathematics and statistics is crucial for data scientists. This skill helps data scientists gain deeper insight into the data they're analyzing.

Read more: Hard Skills vs. Soft Skills

Soft skills

Data scientists also require soft skills to remain productive members of a team. These skills include:

  • A**nalytical skills:** Honing your analytical skills can help you identify patterns and trends in data.

  • Teamwork**:** As a data science professional, you may work with various company departments to help them optimize their processes. Having strong teamwork and coordination skills can help you achieve business goals together.

  • Critical thinking: Honing your critical thinking skills can help you evaluate data and develop accurate conclusions.

  • Communication skills: Data science professionals rely on their writing and speaking skills to make complex information accessible and easy to understand. Having strong reading and listening skills can also help you analyze and comprehend feedback.

  • Adaptability: The field of data science is constantly changing. Adapting to new tools, methods, and information can help you ensure your data science knowledge is relevant and up to date.

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