Difference Between Data Science and Machine Learning

Quick Highlights:

The demand for data scientists is expected to grow by 35.2% from 2022 to 2032, while machine learning specialists will see a 40% increase from 2023 to 2027.
Both fields typically require a bachelor’s degree for entry-level positions, with advanced degrees often necessary for higher-paying roles, though certifications and boot camps can also be pathways into these careers.
Data scientists need a solid foundation in mathematics, programming, data visualization, and machine learning models, whereas machine learning engineers require strong knowledge of algorithms, data modeling, and programming languages like Python and R.
Machine learning engineers generally earn higher salaries than data scientists, with typical earnings ranging from $116,416 to $140,180 per year compared to a median salary of $103,500 for data scientists.

What is machine learning? Do I need to learn data science before machine learning?

The answer to these questions can depend on a lot of different factors. While it helps to learn data science before machine learning, you might not have to spend a lot of time on data science if you already have a strong background in computer programming and math.

Learning the difference between data science and machine learning allows you to determine which career field is right for you. Different academic programs and certifications are available for each of these subjects, so take some time to figure out which one you care about the most. Afterward, you can start a training program in either of these lucrative career fields.

Related:

What Is Data Science and Machine Learning?

From 2022 to 2032, the Bureau of Labor Statistics estimates that the number of data scientists needed will grow by 35.2%. Meanwhile, there is supposed to be a 40% increase in machine learning specialists from 2023 to 2027. By learning about the difference between data science and machine learning, you can choose the right degree program and prepare yourself for an exciting career field.

Related:

What Is Data Science?

What is data science and machine learning used for? Data science is a broad term for a field that involves many tools and skills. A data scientist gathers raw data and processes it for additional insights. This data can be used to understand new business problems or changes in demographic compositions.

This field revolves around understanding and analyzing data from real-world problems. For example, data science is often used in the following situations.

A rideshare company uses data analytics to determine when customers will need rides and when drivers will be willing to drive so that the company can change pricing in those locations.
A bank uses machine learning and data science to decide risk profiles for loans in a mobile app.
A hospitality company may use data science to encourage diverse hiring practices.
A police department uses data science to figure out the best places for deploying law enforcement officers to prevent crimes.
A medical app uses artificial intelligence (AI) to determine someone’s likely risk of having a heart attack or stroke.
A social media company uses data science to predict the type of content someone might want to see.

Data Science Education

A data scientist typically needs to have a bachelor’s degree to get hired in the industry. For advanced positions, you may even need a master’s degree or doctoral degree. However, you may be able to find a short certification or boot camp that can serve as an alternative to traditional schooling. If you do decide to choose an alternate route, you will need to have a solid foundation in mathematics and computer programming.

Data Science Skills

To succeed as a data scientist, you will need to gain the following skills.

Excellent teamwork skills
Solid foundation in mathematics, probability, and statistics
Ability to communicate with various stakeholders
Knowledge of Python, SAS, R, and other programming languages
Ability to work with algorithms
Knowledge of data visualization tools and skills
Understanding of data wrangling
Ability to process and analyze data for a business
Knowledge of machine learning models

Careers in Data Science

After learning about the difference between data science and machine learning, you can figure out which field you want to learn about. With a degree in data science, you can become a data analyst, business intelligence analyst, or data architect. You could also get hired as a data scientist or data engineer.

What Is Machine Learning?

In 1950, Alan Turing created the idea of the Turing Test. This test seeks to determine whether machines can think or not. Today’s machine learning push is a continuation of this test. Machine learning has enabled machines to beat chess masters, write code, and play Go. It might not be able to replace human intelligence yet, but it can drastically improve business operations.

At its heart, machine learning is a type of artificial intelligence. Programmers make software and algorithms that can predict future events and trends based on data. These tools are often used in data science because many data scientists incorporate machine learning into their work. The following list includes common examples of machine learning.

On-demand subscription businesses use machine learning to recommend specific videos or products.
Social media platforms use machine learning to forecast how someone will act and to predict their personal interests.
Law enforcement agencies can use machine learning for facial recognition tools.
Email providers use machine learning to predict which pieces of email are spam and to suggest responses to your emails.
Machine learning helps doctors predict who is likely to reduce treatment costs and improve diagnoses.
Schools, like Florida International University, are able to use machine learning to predict which students are at risk of academic problems before they have major issues.

Machine Learning Education

What is machine learning education? If you want to pursue a career as a machine learning engineer, you will need to get a bachelor’s degree in computer science or a similar field. An advanced degree, like a master’s degree or doctoral degree, can help you land high-paid jobs.

While most colleges do not specifically offer degrees in machine learning, you can often find a closely related field to study. For example, you may be able to get a master’s degree in data analytics, data science, or computer science. If you aren’t sure about which major is right for you, you can always talk to your academic advisor or career counselor.

You will have an easier time getting hired if you get a four-year degree before applying for machine learning positions. If you don’t get a bachelor’s degree, you will need to take a certification course. Then, you can use your certification to demonstrate your machine learning knowledge to prospective employers.

Machine Learning Skills

The day-to-day life of a machine learning engineer can vary based on where you work. In general, you will need to develop the following skills if you want to succeed in this field.

Solid understanding of computer architecture and algorithms
Knowledge of data modeling
Ability to use data science tools
Background in probability and statistics
Knowledge of data structures
Ability to use R, Python, and other computer programming languages

Careers in Machine Learning

Once you decide to study machine learning, you will discover many different career opportunities. With a background in machine learning, you can become a machine learning engineer or an AI engineer. You could also apply for a position as a computational linguist or a cloud engineer.

What Is the Difference Between Data Science and Machine Learning?

What is the difference between data science and machine learning? What is machine learning used for? While there are many similarities between the two subjects, there is one key difference. Data science seeks to make sense out of complex data sets. In comparison, machine learning is designed to learn from data analysis.

Machine learning is a part of data science, but data science is not necessarily machine learning. As a subset of data science, machine learning incorporates business intelligence and data analytics tools. Programs are trained to automatically process data and make predictions.

While machine learning is a kind of artificial intelligence, data science isn’t. Data science may use artificial intelligence, but its primary purpose is to extract meaning from structured and unstructured data. Then, the resulting knowledge is used to help businesses, non-profit groups, and government agencies function at their best.

Do I Need to Learn Data Science Before Machine Learning?

Have you ever wondered, “Do I need to learn data science before machine learning?” While it is helpful to have a solid understanding of data science before studying machine learning, it is not necessary. Many students begin machine learning courses without a significant background in data science.

While you might not need to study data science first, you will need to have a strong foundation of knowledge in place. Computer programming and mathematics are both important subjects for anyone who plans on pursuing machine learning as a career. Fortunately, any course you take will most likely give you a list of prerequisites. As long as you have completed the prerequisites, you shouldn’t have a difficult time taking machine learning classes.

Machine Learning Vs Data Science Salary

One big difference between data science and machine learning is the pay. When you look at machine learning vs data science salary ranges, machine learning comes out ahead. A typical machine learning engineer will earn between $116,416 to $140,180 per year. In comparison, a data scientist is paid a median salary of $103,500 annually, according to US News.

Find Out More About Data Science and Machine Learning Careers

What is data science and machine learning? More importantly, how can you use these tools in your day-to-day life?

Once you discover the difference between data science and machine learning, the next step is figuring out what you want to do with your knowledge. Both of these industries can lead to high-paid positions. Thanks to the large demand for these jobs, you shouldn’t have a problem finding a career in data science and machine learning after you have completed your college degree or certification program.