You may have heard the phrases “data analytics” and “data science” mentioned before. If you are new to the world of data, you might be wondering what these terms mean — and if you're interested in a career in data, which is the right path for you?
Though there is a lot of overlap between the two areas (and disagreement about the exact definitions), the main difference is how much they rely on machine learning. In general, data analytics covers everything from collecting data to spotting trends to communicating insights. Data science is a broader field that includes data analytics, and often involves making predictions with tools like machine learning or conducting experiments with data.
Companies collect a great deal of data. Almost all of them can benefit from data analytics to help make sense of it. But not as many require building algorithms that predict the future or apply patterns to new information.
Interested in working with data, but not sure where to start? In this article, we’ll explore data analytics and data science in more detail, to help you decide between our Data Scientist: Analytics Specialist career path and our Data Scientist: Machine Learning Specialist career path.
What is data analytics?
Data analytics is all about helping organizations make decisions based on data. Page visits can inform marketing strategies, housing costs can affect policy changes, and patient outcomes can impact a hospital’s operations. Data analytics helps us find patterns and tell stories from the large quantity of data organizations have.
To do that, Data Analysts take a business question and translate it into a data question. Part of their job is collecting and reformatting data, analyzing it with statistics and probability, and sharing actionable insights in the form of visuals and reports.
“Every company is collecting some data. And a lot of companies need to leverage their data to make good data-driven decisions. There’s a huge opportunity for Data Analysts to really put that data to work.” says Codecademy Data Science Domain Manager Michelle McSweeney.
Data analytics languages and tools
What languages and tools are used for data analytics? Generally, Data Analysts use SQL and Python or R. SQL interacts with data housed inside databases, and Python and R analyze and graph the data to show trends and patterns.
Getting started with data analytics
Interested in learning more about data analytics? You can dive into our Data Scientist: Analytics Specialist career path to learn everything you need to become a Data Analyst.
If you want to learn a specific Data Analyst skill, check out the following Skill Paths:
Even if your ultimate goal is to become a Data Scientist, gaining a solid foundation in data analytics is a good first step to take.
What is data science?
Data Scientists create algorithms to automate data processes, recognize patterns in new information, and make recommendations based on past behavior. They work on things like forecasting the financial future, creating customer-facing chatbots, detecting tumors in X-ray images, and making suggestions of things you might like.
“Data science tends to be more specialized than data analytics, because not every company needs to make predictive data decisions, and not every company needs to leverage big data,” Michelle says.
To learn more about data science, watch the following video with Sophie from our Curriculum Team, or check out our blog all about: What is data science?
Data science languages and tools
Data Scientists also commonly use SQL and Python or R. Python’s popularity among Data Scientists has been growing as more libraries are created that focus on working with data. But Python isn’t the only language, and depending on what industry you go into, you might need to pick up other data science languages.
Getting started with data science
Interested in becoming a Data Scientist? Our Data Scientist career paths will teach you everything you need to know to become an entry-level Data Scientist from the ground up.
- Want to learn how to create neural networks and predictive machine learning models? Check out our Data Scientist: Machine Learning Specialist career path.
- More interested in using data to figure out why things happen and designing your own experiments? Our Data Scientist: Inference Specialist career path could be right for you.
- If you’d rather build your own chatbots and work with artificial intelligence, try our Data Scientist: Natural Language Processing Specialist career path.
- There’s also the Machine Learning/AI Engineer career path for those who want to build machine learning applications like the recommender systems used by apps like Spotify and Netflix.
- Looking for the fastest way to break into data? Our Business Intelligence Data Analyst career path is designed to teach you the essential skills you’ll need to analyze data and present your findings, and you can be job-ready in as little as three months.
Still not sure where to start? If you want to work in data, but you’re not sure in what capacity, we suggest starting with the Data Science Foundations skill path. From there, you can switch over to your preferred specialization at any time. If you decide to make the switch, all of the coursework that you’ve completed will transfer over too.
“If you’re really excited about working with data, and you want to learn machine learning, then you can jump into the Data Scientist: Machine Learning Specialist career path or take the Build Machine Learning Models with Python course,” says Mariel Frank, a Software Engineer here at Codecademy.
“In general, if you want the most direct path to a job working in data, then go with the Data Scientist: Analytics Specialist career path,” Michelle says. Analytics Specialists have the biggest job market as most organizations generate large amounts of data.
Regardless of which path you choose, you’ll use your new skills to build unique projects you can use to build a portfolio — and we’ll also help you prepare for the hiring process with interview prep courses and other helpful resources you can find in our Career Center.