In such a faced-moving industry, it's not surprising that we sometimes confuse certain technical terms, especially when it seems like new tech topics are popping up overnight. And in the world of big data, which involves working with enormous and complicated amounts of information, some people still confuse certain concepts, tasks, and roles found within this emerging and growing discipline.
One of the main points of confusion in this field is the difference between data analytics and data science. While some people may use the two interchangeably, they’re actually two different, albeit related, fields. And that’s exactly why we’ve written this article: to help you fully understand the differences and make the best choice for your future tech career. Let’s dive right in!
The Difference Between Data Science and Data Analytics
Although both are found at the crossroads between math, stats and development, data science and data analytics serve two distinct purposes, meaning the profiles of professionals working in the two fields differ significantly.
But how, exactly? Well, let’s break it down:
Data science handles the more technical aspects of data, working with tech teams on actually creating and maintaining the programs that guide data analysis, such as AI models.
Data analytics, on the other hand, focuses on the decision-making process that comes from the work that data scientists do, transforming the data into understandable figures for better decision making.
Still not quite clear? Let’s put it like this: in data analytics, you’re making sense of information and turning it into business and in data science, you’re looking to train computers to be able to run this type of data analysis that is so necessary for quality decision making.
Now that you have a better understanding of the differences, let’s dive a bit deeper into the intricacies of each so that you can choose the right career path for you.
What is Data Science?
Currently considered to be a branch of big data, data science aims to extract and interpret information derived from the huge amount of data gathered by a particular company, whether for their own use or for operations they might carry out with third parties. To achieve this goal, data scientists are in charge of designing and implementing mathematical algorithms based on statistics, machine learning, and other methodologies that allow companies to use tools that provide them with the grounds to act one way or another according to the circumstances and timing.
Among other things, data scientists in training will learn:
Basics and advanced machine learning techniques such as deep learning and neural networks for specific and high-tech data tasks
Supervised and unsupervised machine learning
Available areas for specialized training, such as computer vision, NPL, or MLOps
How to deploy solutions using cloud computing
More advanced tools such as TensorFlow and Keras for complex data tasks
It's also not just about obtaining information from the data gathered and being able to use it; data scientists are also given the task of ensuring the detected patterns are visualized correctly so they are clear and legible by those who make decisions based on said data.
Practical examples of data science
Looking for a more in-depth explanation of exactly what data science looks like in the real world? Here are a few examples:
Using data science to detect unusual patterns that might indicate fraud by analyzing transaction data
Predicting customer buying habits based on previous shopping habits and what similar customers have bought
Using data science to better forecast weather by analyzing large amounts of climate data
Using data science to predict product demand and optimize inventory levels, reducing waste and costs using data from various sources
Helping a financial planner predict stock market trends
What is Data Analytics?
When we talk about data analytics, we're usually talking about a more specific and precise application of data science. That's why in industries that have incorporated data analytics, the analysts' role has been to search for unprocessed sources of information in order to try and find trends and metrics that could help companies make more accurate decisions and obtain better results.
We need to be careful not to confuse their work with that of someone in business intelligence, which deals with a much smaller amount of data, meaning that its capacity for both analysis and prediction is more limited. Students learning data analytics will:
Learn the basic and essential data analytics skills
Become familiar with business intelligence through Tableau
Explore the various applications of data analysis across industries
So while data scientists are masters in predicting the future, basing their forecasts on patterns from the past detected in the data, data analysts extract the most relevant information from the same data sets. You might say that, if the former asks questions to try and map out what will happen in the next few years, the latter is responsible for answering questions that are already on the table.
Practical examples of data analytics
Having a hard time picturing what exactly real-world applications of data analytics would be? Take a look at some of the most common examples here:
Sales numbers: identifying the most popular products or services that a company offers
Website traffic: figuring out where visitors come from–and how to attract more
Traffic data: adjusting traffic light timings and better planning road layouts to reduce traffic
Watching habits: recommending movies and TV shows to suggest content viewers may enjoy
Patient data: using data to identify which treatments are most effective and improve patient care
Applications of data science and data analytics
Another major difference between the two disciplines is how they are applied across various industries. In fact, data science has had a huge impact on search engines, which use algorithms to provide better responses to users' queries and in the shortest time possible.
Similarly, data scientists have had a significant impact on the development of recommendation systems. In terms of primarily visual content, such is the case with Netflix, or purchasing sites such as Amazon, these systems offer customers much more accurate recommendations, which greatly enriches the user experience.
If you’re interested in the specific outcomes available to you once you hit the job market, take a look:
Roles in data science: data scientist, AI engineer, data engineer (junior roles), NLP engineer, machine learning engineer, MLOps engineer, and computer vision engineer.
Roles in data analytics: data analyst, business analyst, business intelligence, data analyst consultant, data engineer, and data scientist.
Both of these fields are incredible ways to get into tech and find the exact path that’s right for you; take the time to review the differences between the two, familiarize yourself with what’s required for each, and set yourself up for success by choosing the one that best aligns with your skills and interests.
We’re now offering bootcamps in both Data Analytics and Data Science and Machine Learning, allowing you to choose the best path forward for you. Ready?
