Data Science vs. Machine Learning

At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Dr. Thomas Miller of Northwestern University describes data science as “a combination of information technology, modeling, and business management”. Universities have acknowledged the importance of the data science field and have created online data science graduate programs.

Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules.

Data science and machine learning are both very popular buzzwords today. These two terms are often thrown around together but should not be mistaken for synonyms. Although data science includes machine learning, it is a vast field with many different tools.

>Data Science Workflow

The proliferation of smartphones and digitization of so many parts of daily life have created massive amounts of data. At the same time, the continuation of Moore’s Law, the idea that computing would dramatically increase in power and decrease in relative cost over time, has made cheap computing power widely available. Data science exists as the link between these two innovations. By combining these components, data scientists can derive more insight from data than ever before.

The practice of data science requires a unique combination of skills and experience. A skilled data scientist is fluent in programming languages like R and Python, has knowledge of statistical methods, an understanding of database architecture and the experience to apply these skills to real-world problems. A masters in data science may build upon existing knowledge to ensure that you are best prepared for a long career in this ever-growing field.

The Limitations of Data Science

Though it may sound obvious, data science relies on data. The massive growth of data science was spurred by the availability of massive datasets and cheap computing power. Only with these incredible resources is data science effective. Small datasets, messy data, and incorrect data can waste a lot of time, creating models that produce meaningless or misleading results. If the data doesn’t capture the actual cause of variation, data science will fail.

Careers in Data Science

Data science is needed wherever there is big data. As more and more industries begin to collect data on customers and products, the need for data scientists will continue to grow. To start on the path towards a career in data science, consider these skills to land a data science job.

Learn more about how to become a data scientist.

What is machine learning?

Machine learning creates a useful model or program by autonomously testing many solutions against the available data and finding the best fit for the problem. This means machine learning can be great for solving problems that are extremely labor intensive for humans. It can inform decisions and make predictions about complex topics in an efficient and reliable way.

These strengths make machine learning useful in a huge number of different industries. The possibilities for machine learning are vast. This technology has the potential to save lives and solve important problems in healthcare, computer security and more.

The Inherent Limitations of Machine Learning

Though machine learning may seem like a magic bullet to answer any question, it is not all-powerful.

Machine learning algorithms are better than ever at creating useful results with minimal intervention. However, we may still need engineers and programmers to constrain and optimize these algorithms to make them work on new problems.

There are also plenty of problems that machine learning isn’t particularly good at solving. If a traditional program or equation can solve a problem, adding machine learning might complicate the process instead of simplifying it.

Importance of Machine Learning

Machine learning is being applied in many industries. Cutting costs by letting a machine learning algorithm make decisions can be a lucrative solution to many problems.

Applying these techniques in industries like lending, hiring and medicine raise some major ethical concerns. Since these algorithms are trained on data created by humans, they incorporate social biases into their results.

Since machine learning algorithms operate without explicit rules, these biases may be hidden. Some machine learning algorithms are currently a “black box” -we know what goes in and what comes out, but not how it got there. Google is doing research to make it easier to understand how neural networks “think.” However, this work may need to go further before it can address data bias and other ethical issues with machine learning. Where do data science and machine learning intersect?

Machine learning is one of the many tools in the belt of a data scientist. In order to make machine learning work, you need a skilled data scientist who can organize data and apply the proper tools to fully make use of the numbers.

Data Scientist vs Machine Learning Engineer

Ever consider the growth of machine learning and data science to be the reasoning behind the best and popular job attributions that are given to these fields? It’s important to understand that as the technology and data fields grow, careers may very well. Technology careers often intersect, but the difference between a machine learning engineer and data scientist is important to distinguish. Here’s a list of common skills for data scientists and machine learning engineers:

Data science is a broad, interdisciplinary field that harnesses the widespread amounts of data and processing power available to gain insights. One of the most exciting technologies in modern data science is machine learning. Machine learning allows computers to autonomously learn from the wealth of data that is available.

The applications of these technologies are vast, but not unlimited. Though data science is powerful, it only works if you have highly skilled employees and quality data. To get involved in data science, take a look at some data science masters programs.

Last updated: June 2020