Data Science vs Machine Learning: What’s the Difference?
With so many tech careers popping up with so many similarities, how can you tell the difference? For example, data science and machine learning might seem similar, but they have two very different purposes.
Machine learning relies on artificial intelligence, while data science focuses on statistical methods and predictive analysis. The skills you need for machine learning aren’t all necessary for data science. In this article, we provide an in-depth look at data science versus machine learning.
What Is Data Science?
Data science is an area of study that combines programming languages, mathematics, and statistics. In data science, professionals extract insights from structured and unstructured data in order to perform an analysis. Data analysts and businesses use these insights to solve an organization’s obstacles and issues.
The data science process involves working with and analyzing huge data sets from various sources. These sources include mobile devices, websites, and consumer behavior. Professionals who specialize in data science are called data scientists.
Data Science Applications
Data science has various applications. One example is banking. Using the core concepts of data science, banks can optimize efficiency, segment consumers, perform fraud detection, manage consumer data, and apply risk modeling.
Another application for data science is manufacturing. Manufacturing companies use data science techniques to make strategic business decisions. As a result, these companies can reduce costs, increase productivity, and grow their profits.
A third application for data science is transportation. Having access to valuable insights like customer location, logistics, and economic factors allows companies to choose the best delivery routes.
The Limitations of Data Science
A significant limitation of data science is that it relies heavily on domain expertise. Even if you know statistical analysis and computer science, domain knowledge is imperative. This is why you might need to get a specific Bachelor’s Degree in Data Science to succeed.
Although data scientists may work hard to gather meaningful insights, this becomes unnecessary with arbitrary data. Arbitrary data won’t yield the expected results. Most times, this data can fail because of poor use of resources or neglectful management.
As data science is an umbrella term comprising statistical analysis, mathematical concepts, and programming languages, it can be difficult to master. The components that form data science are tricky to learn and require years of practice.
What Is Machine Learning?
Machine learning is a hybrid of computer science and artificial intelligence. This practice describes the use of data and complex algorithms to imitate how humans learn. Machine learning algorithms reach optimal accuracy if you train them regularly.
This practice aims to have computers learn automatically with little to no human intervention. Machine learning is a groundbreaking sub-field of artificial intelligence. You can see this technology at its best in self-driven cars or even Netflix’s recommendation engine.
Machine Learning Applications
Machine learning applications are present in our daily lives. Traffic alerts like Google Maps use machine learning to inform users of the quickest routes and routes with the most traffic. Google collects data from users like their routes and speed to provide traffic alerts and detailed information.
We also see machine learning in social media, particularly in tagging suggestions. For instance, Facebook software uses face detection and image recognition to match the face of users in its database.
You will notice a similar application of machine learning from Amazon. After viewing a specific product on Amazon without buying it, ads for that product might show up on your social media feed and in other advertisements. This happens because Google uses your search history to recommend relevant ads.
The Limitations of Machine Learning
A popular limitation of machine learning is the surrounding ethics. The simple concept of relying on data and algorithms over our judgment leaves room for error. For example, if a self-driven car hits another driver, or if a machine replaces a human’s job and something goes wrong, whose fault is it?
Machine learning also requires huge chunks of data to draw accurate and valuable results. Gathering, cleaning, organizing, and storing this data demands tons of resources, time, and effort. Unfortunately, you can’t reuse data for machine learning. This means you will be working with massive amounts of data.
Machine learning is highly susceptible to errors, too. Inaccurate data can cause a negative customer experience, cause your project to decline, or set off more issues in the datasets. To fix these issues, you have to search through large amounts of data.
How Are Data Science and Machine Learning Different?
Data science and machine learning are different because you need different skills to perform the varying lifecycle and data requirements. We have listed key differences between data science and machine learning below.
Despite a few similarities, like knowing how to code, the key skills you need for data science vary from machine learning. Data science skills include data mining, data cleansing, deep learning, and statistics. Data scientists must also learn SQL and use tools like Hadoop.
For machine learning, critical skills include natural language processing, statistics, probability, data modeling, and data evaluation. Machine learning experts should also have an understanding of or experience with computing tools like TensorFlow.
Data scientists need to know about the deep learning process, which is the next step for machine learning engineers. However, both professions need skills in machine learning algorithms.
Principles and Techniques
One of the most notable differences between machine learning and data science is the techniques and principles. Data science refers to a broad term that houses machine learning fundamentals. Machine learning techniques are a separate process that is part of data science.
The focus for machine learning techniques is managing complexities in algorithms and machine learning models. Machine learning engineers also need to assess the mathematical concepts behind algorithms to determine if they are working as intended.
Data science covers components of machine learning techniques, but there are still key differences. Data scientists spend their time mining and analyzing data to find compelling patterns. Professionals must interpret these patterns and use business intelligence techniques and analytical tools to find valuable insights.
Unlike machine learning engineers, data scientists can work with raw, unstructured data. These professionals can also work on structured data. They will use their analytical skills to convert the data into tangible business benefits.
As much as machine learning experts need analytical skills, they only work with structured data. This is because these experts need to input data into algorithms. The data must be in optimal condition for machine learning engineers to work with it.
The input data for machine learning and data science also varies. Humans can generally interpret the input data that data scientists use. However, machine learning engineers use input data that only the algorithm can immediately understand.
For these two disciplines, the lifecycles are significantly different. There are four major components in a data science lifecycle. This includes working with key business leaders to identify the problem, then investigating, mining, and cleaning data, and finally creating a business model with the data.
Machine learning life cycles have six stages. These stages are gathering, preparing, and wrangling data, performing data analysis, training the machine learning model, testing the model, and deploying it.
While the length of a data science and machine learning lifecycle depends on many variables, data science projects usually take longer. A machine learning project typically takes up to one month to complete. However, data science projects can take as long as six months. This is because preparing the data requires a lot of time.
How data scientists and machine learning engineers measure the results of their projects is very different. For machine learning, the performance criteria are standard for every project. Each machine learning algorithm has a measure indicator that reveals if the model works well with the training data.
For data science projects, how you measure performance varies from project to project. Data scientists aim to help solve an organization’s problems. If the problem changes, the performance criteria will change. Typically, data scientists need to evaluate a model’s usability, querying, and data quality, and if the model successfully fulfills its purpose.
Data Scientist vs Machine Learning Engineer
The responsibilities of a data scientist include working with stakeholders to identify an organization’s challenges. Professionals need to create decision trees, statistical models, and predictive models to help a company make the best business decision.
Machine learning engineers focus more on converting data science prototypes into functional algorithms and performing statistical analysis. These engineers also need to test the artificial intelligence components they design. Machine learning engineers primarily focus on three algorithms: unsupervised learning, supervised learning, and reinforcement learning.
To become a data scientist, you need a bachelor’s degree. You can earn a Bachelor’s Degree in Computer Science, Data Science, Statistics, or any quantitative field. You can also become a machine learning engineer with a Bachelor’s Degree in Computer Science or a tech-related field.
Who Earns More, Data Scientists or Machine Learning Engineers?
On average, machine learning engineers earn more than data scientists. This may change depending on experience or position. PayScale confirms that the average salary for machine learning engineers is about $112,806 annually. PayScale also reports that the average salary for a data scientist is $96,750.
There is no accurate report on the job growth for machine learning engineers or data scientists. However, the Bureau of Labor Statistics (BLS) confirms that all computer and information technology jobs are predicted to grow 11 percent between 2019 and 2029. This means there will be a large growth in the number of job opportunities in this field.
Can a Data Scientist Become a Machine Learning Engineer?
Yes, a data scientist can become a machine learning engineer. This is because data science incorporates various machine learning techniques. In many cases, you will need to learn machine learning to become a data scientist.
Since data scientists have the skills that machine learning engineers need, data scientists can become machine learning engineers. However, depending on your experience and understanding of machine learning, it might be best to attend machine learning courses. This will help you build your machine learning expertise.
What Is the Future of Data Science and Machine Learning?
The future of data science and machine learning is bright. Artificial intelligence is rapidly gaining popularity in the technology industry. With this comes a tremendous demand for machine learning skills.
As digital businesses are on the rise, the need for data scientists will also increase. This is because of the value these professionals offer companies. In the 21st century, most computer and information technology professions offer job security and high earning potential. This is not different for machine learning and data science.
There’s a massive difference between machine learning and data science, yet the two fields share several similarities. If you have data science expertise, machine learning is a realistic career venture and vice versa.