Right now, the market for those knowledgeable in data is growing quickly. It’s estimated that US data professional job openings grew by 364,000 openings by 2020 alone. However, when you see terms like machine learning, data science and data analytics out there, it’s hard to know which one’s which. What is the difference between them?
What Is Machine Learning?
Let’s start with machine learning. It’s something you’ll hear a lot about, as it’s being used in all kinds of industries right now to get better results in marketing, sales and even HR. Essentially, it’s the practice of using algorithms to extract data, and learning from it to inform future actions.
You’re likely interacting with machine learning every day, without even knowing it. For example, Facebook uses machine learning to understand more about their users. It gathers information about the behaviors you exhibit on the site, and with that information it can offer more relevant ads and interests to you. Product recommendation on Amazon works in the same way too, as the machine learning AI gathers information on what you buy, and then recommends you similar products.
What Is Data Science?
Next, let’s look at data science. This is quite a broad term, and definitions have been changing over the last decade or so. In essence, data science is the combination of hacking skills, math and statistics, and subject expertise.
What does that mean in practice? Data science is used to tackle big data, and understand what information can be taken from it. As such, it can include data cleansing, preparation, and analysis. This data is collected from multiple sources, such as machine learning outputs, predictive analysis, and so on. With these multiple data sets, analysis can happen, and predictions can be made for the future. This is especially important when it comes to businesses, as they need to be able to stay ahead of the curve.
What is Data Analytics?
Finally, let’s look at data analytics. This is the process of understanding the data gathered for a business, and making recommendations based on the results. A data analyst will need to understand statistics, PIG/HIVE, coding, and more. They use all these skills to gather the results, and make sense of them.
This is something that is becoming crucial for businesses, no matter what industry they’re in. “As a business you have access to more data than ever before, and you need to be able to make sense of it” says Bill Styles, a tech blogger from Write My X and 1 Day 2 Write. “Data analysts are invaluable for interpreting the data and helping a business grow with it.”
Expertise In Each Role
As you’ve seen, machine learning, data science and data analytics are all different disciplines that all feed into each other. As such, if you’re looking to make your career in data, you’ll need to consider where you’ll start learning. All three areas have a lot of overlap, but there’s some differences that you should be aware of.
Machine learning: To work as a machine learning expert, you need to have a foundation in working with AI programs. As such, you’ll need expertise in computer fundamentals, as well as data modeling and evaluation skills, stats and probability knowledge, and in depth programming expertise.
Data scientist: To work as a data scientist, you’ll need a knowledge of machine learning, so there is some overlap here. You’ll also need strong programming knowledge, such as in Python, SAS, R or Scala. You’ll need to have experience with SQL database coding too. “There is a good amount of overlap with machine learning here” says Michelle Robin, a Origin Writings and Brit Student. “Whether you work in machine learning or data science, you’ll need to understand techniques like regression and supervised clustering.”
Data analyst: To work in data analysis, you’ll be required to be able to code in R or Python, as well as understand PIG/HIVE. A knowledge of data wrangling and mathematical statistics is critical, too.
While data science, machine learning and data analysis are all different, they do have a lot of overlap. Whether you’re a business looking to put them to use, or someone looking to make their career in data, it’s critical to know how they differ.