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 Analyst Career Path and our Data Scientist 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 Michelle, a curriculum developer here at Codecademy.
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 Analyst 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 science is a broad field that includes data analytics. It also covers making predictions with machine learning, working with big data, and developing artificial intelligence.
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 tells us.
To learn more about data science, watch the following video with Sophie from our Curriculum Team, or check out our article on 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 learning more about data science? Check out our Data Scientist Career Path, which provides everything you need to know to become an entry-level Data Scientist, from the ground up.
If you finish the Career Path and find that you want additional specialization in machine learning, we suggest taking the Build Deep Learning Models with TensorFlow Skill Path. If you want to learn more about working with text data and natural language processing, we recommend checking out the Apply Natural Language Processing with Python Skill Path.
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 Analyst Career Path. From there, you can switch over to the Data Science Career Path at any time. If you decide to make the switch, all of the data analytics coursework that you’ve completed will transfer over too.
“Figure out if it’s right for you. If you’re really excited about working with data, and you want to learn machine learning, then you can jump into the Data Scientist Career Path or take the Build Machine Learning Models with Python course and get that experience in addition,” says Mariel, a curriculum developer here at Codecademy.
“In general, if you want the most direct path to a job working in data, then go with the Data Analyst Career Path. It also opens up the opportunity to get into data science later on,” Michelle tells us.
If you’re interested in learning data analytics, our Data Analyst Career Path will set you up with the tools you need to become a Data Analyst. If you're interested in learning data science, the Data Scientist Career Path will guide you through what you’ll need to know to become a Data Scientist.