Data Science vs. Machine Learning (Roles and Skills)

By Indeed Editorial Team

Updated September 25, 2022

Published January 3, 2022

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.

There are many job opportunities in the technology industry. Although data science and machine learning share various similarities, they're different career paths with unique responsibilities. Understanding the difference between them can help you decide what career path or specialization is best for you. In this article, we discuss data science vs. machine learning, explore their roles, identify career options in both fields, and highlight the skills required in these fields.

Data science vs. machine learning

Understanding data science vs. machine learning can help you choose the right fit for you. Data science is a broad interdisciplinary field that involves extracting useful information from sources and interpreting data. It includes the use of technologies, systems, scientific approaches, and procedures to extract insights and information from organized and unorganized data.

As a subset of data science and branch of artificial intelligence, machine learning is a set of techniques used by data scientists to help computers learn autonomously from a wide range of data with minimal human intervention. They also teach computers to build models autonomously by identifying patterns and improving over time. Data scientists develop programs, which the computer uses to test different solutions against available data, find the best option, and make predictions. One of their primary duties is data automation, which involves using algorithms from analyzing large data and implementing them.

Related: Everything You Need to Know about Data Science as a Career

Roles of a data scientist and machine learning professional

Here's a list of some roles of a data scientist:

  • gathering a large amount of data from different sources

  • sorting, organizing, and analyzing data to derive insights and predictions

  • automating data collection, organization, and analysis with computer programs

  • organizing data into new formats to make reading and analysis easy

  • applying predictive and sentimental analysis to data

  • preparing reports on their findings to influence decision making

  • solving business problems using statistical methods and programming languages

  • developing, designing, and implementing tools and processes for monitoring the performance and accuracy of models

  • updating models to reflect changing business objectives and data inputs

Some duties of a machine learning professional include:

  • developing tools and programs to sort through large datasets to find commonalities

  • conditioning computers for learning by accessing and monitoring data sets before inputting them into machine learning systems

  • building algorithms based on statistical models for developing and improving software-based systems

  • converting and transforming data types

  • researching and implementing machine learning algorithms and tools

Related: How to Become a Data Scientist in Five Essential Steps

Careers in data science

Here's a list of careers in data science for you to consider:

1. Data analyst

National average salary: $76,740 per year

Primary duties: A data analyst is typically an entry-level role in the field of data science. These professionals collect, sort, and analyze structured and unstructured data. They also analyze data using computer programs and calculations and report their findings to help inform decision-making.

2. Database administrator

National average salary: $70,254 per year

Primary duties: A database administrator is responsible for the security and efficient access to a database. They work alongside the IT department to create new datasets and organize unstructured data. Database administrators also create alternative access options and prepare backups in case of a system failure.

3. Data scientist

National average salary: $80,857 per year

Primary duties: Data scientists build algorithms from raw databases to help companies understand customer trends and behaviour. They also build models that help analyze data to make predictions for businesses. Businesses use these predictions to make long-term decisions and create strategies.

4. Data engineer

National average salary: $107,997 per year

Primary duties: Data engineers build the infrastructure used by data scientists and analysts. Knowledge of programming languages and structured query language (SQL) is important for this role, as data engineers are responsible for building structures used to collect and transform data. Data engineers also create and manage databases and processing systems that data analysts and scientists use.

Careers in machine learning

Some of the careers available in machine learning include:

1. Software developer

National average salary: $77,605 per year

Primary duties: Software developers build programs or software that allow users to perform multiple tasks on various devices. They're also responsible for the entire software development process. Although not compulsory, a degree in computer science is an added advantage. Skills required to be a software developer are statistics and probability, data structure, and computer architecture.

2. Machine learning engineer

National average salary: $98,905 per year

Primary duties: A machine learning engineer is responsible for the smooth functioning of machine learning libraries and building platforms for machine learning projects. They use programming languages to conduct experiments that show that production tasks function optimally while adding new functionalities by following software best practices. A machine learning engineer requires skills and knowledge in data modelling, machine learning algorithms, systems design, programming and probability, and statistics.

3. Computational linguist

National average salary: $103,945 per year

Primary duties: These professionals work with the voice recognition aspect of machine learning, helping computers learn how to understand human spoken languages. Computational linguists also help computers learn to translate words across spoken languages and learn different speech patterns while improving existing systems. This job role uses a combination of technology and arts, as it requires a degree in linguistic, mathematics, or computer science. Skills required include a strong understanding of spelling, grammar and syntax, language fluency, machine learning and deep learning, computer or software processing, mathematics, and statistics.

Skills required for data science

Data scientists usually require a variety of hard and soft skills, including:

Soft skills

These are productive personality attributes that can help data analysts function optimally in the workplace. They include:

  • Communication skills: Having strong communication skills is helpful in conveying complex programming concepts in simple terms. It may also help you understand feedback from managers and team members.

  • Analytical and critical thinking skills: Honing your analytical skills as a data scientist helps you identify and understand patterns and trends. You can also use these skills to properly evaluate data and information to arrive at sound conclusions.

  • Teamwork skills: Working as a data scientist may involve working with various other departments. It's important that you know how to work with different people and collaborate to reach a common goal.

  • Adaptability skill: Adapting easily to newer technologies, trends, and processes is an important skill for data scientists to possess, as their field is constantly developing and updating different technology.

Hard skills

Data scientists often undergo training for technical skills. Some of those skills are:

  • Statistics: Knowledge of mathematics and statistics is foundational for a career in data science. It can help you get more insight into the data you analyze.

  • Data visualization: This includes transforming data into easily readable formats and is important as you may be dealing with a large amount of data in the role.

  • Machine learning: This involves automating models for analyzing data. Although it isn't compulsory, it's an added advantage for companies that deal with big data requiring automation.

  • Programming skills: Data scientists require basic knowledge of programming languages. These languages allow data scientists to interact with computer systems and they're important for analyzing data.

Related: How to Learn Data Science (A Complete Guide)

Skills required for machine learning

Skills required to be a machine learning professional include:

Hard skills

Here's a list of hard skills commonly necessary for machine learning roles:

  • Neural network architecture: These are algorithms used to implement machine learning tasks. The neural network architecture helps you understand how the human brain works and provides insight into parallel computations.

  • Data modelling and evaluation: This skill is important in working with large data to derive useful predictions. It also involves understanding concepts such as a confusion matrix, mean absolute error, and classification accuracy.

  • Reinforcement learning: Machine learning professionals use reinforcement learning methods to train machine learning models. This skill is important to help understand artificial intelligence.

  • Programming skills: These include computer science fundamentals, software engineering, and systems design. Although not all these skills are necessary, they can give you a competitive advantage when job searching.

  • Applied mathematics: This helps you choose the right algorithms to employ and understand validation strategies.

Soft skills

Soft skills required for machine learning include:

  • Time management skills: A machine learning professional usually has multiple responsibilities. As a result, it's important to learn effective time management to complete tasks efficiently.

  • Leadership skills: This skill is necessary as a machine learner often works with other team members. It's important that you know how to be a leader and coordinate with others to help you achieve set goals.

  • Communication skills: It's advisable that you know how to communicate complex techniques to others on the team properly. This can include writing, reading, speaking, and listening skills.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organization and a candidate's experience, academic background, and location.

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