Data science vs. data analytics

differences between data science and data analytics

In such a faced-paced world, it's not surprising we sometimes confuse certain technical terms, especially when they evolve at such dizzying speeds and new scientific fields seem to emerge overnight. That's why 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, two very closely related, but distinctly different areas.

Although both are found at the crossroads between maths, stats and development, the purposes they serve have clearly differentiated tangents, meaning the profiles of professionals working in the two fields are also very different. It's essential that anyone looking to specialise in big data knows what kind of knowledge and skills they will need to acquire should they decide to focus on either data analytics or data science. So, if you're currently studying with us or thinking about joining Ironhack’s Data Analytics bootcamp, pay close attention.

The differences between data science and data analytics

For decades, experts have tried to narrow down the field of activity of one discipline or another, but they've not always been successful. However, since 1996, when the term "data science" came into use thanks to an article by Gregory Piatetsky-Shapiro, the definitions have come a long way and it seems that we can now clarify the scope of both fields. Below you'll find an updated Venn diagram that incorporates the different specialisations and their respective roles.

Data science vs. data analytics

What is data science?

Data science is currently considered to be a branch of big data and 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. 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 visualised correctly so they are clear and legible by those who make decisions based on said data.

What about data analytics?

When we talk about data analytics on the other hand, 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. In this case, 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.

As such, the main difference between data science and data analytics is the branch of big data each field focuses on: whilst the former is found on the road to discovery with its sights set wide, the latter is more focused on the operations of different businesses that apply and seek solutions to existing problems.

So, whilst 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.

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What are the applications of each discipline?

On this basis, another major difference between the two disciplines is how they are applied in different 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.

Netflix Machine Learning Algorithms

In the case of data analytics, they are used more frequently in sectors such as healthcare, allowing health centres to care for their patients more efficiently. This discipline is also frequently used in other industries such as energy management, since, thanks to data analysis, they can optimise where resources are used and even choose to automate certain services, thus avoiding unnecessary costs. Analysts are also highly sought-after by the hospitality industry, as they can help hotels discover travellers' preferences and offer them alternatives that best suit their tastes and needs.

Dashboard Booking Insights by Country

As you can see, there are many factors to consider before entering into the world of big data. Data analytics and data science are very closely-related disciplines, but are not without their differences, so we know it might be difficult choosing which path to take. Here's summary of the main differences we've talked about throughout this post: 

Data science

Data analytics

  • Creation of predictive models and algorithms
  • Wider and more diverse field of activity
  •  Expert in statistics and mathematics
  • Experience with SQL
  • Skilled in Python, R, SAS and Scala
  • Advanced knowledge of machine learning
  • Tends to work with unstructured data
  • Apps in sectors such as artificial intelligence, health, blockchain, or website search engines
  • Draws conclusions from different sources of data
  • Field of activity limited to the business sector
  • Familiarised with data warehouse, ETL tools and business intelligence
  • Strong command of Python and R
  • Expert in data wrangling
  • Skilled in data visualization
  • Business knowledge and decision-making skills
  • Application in sectors such as retail, travel, healthcare or marketing

If you are looking to specialise in the data sector and still have some questions you want to ask, don't hesitate to contact us at Ironhack and enquire about our Data Analytics bootcamps.

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