After years hiding in academia, these terms have entered the mainstream. But just what is data analytics vs data science?
Historically, data analytics and data science were the preserve of specialists in universities. But with big data booming, computer processing power expanding, and the rise in AI making data accessible to everyone, even the smallest startups can now harness the power of big data.
So, what exactly is the difference between data science and data analytics? And which should you get learning? In this post we’ll answer this question, covering:
- What is data science?
- What is data analytics?
- What’s the difference between data science and data analytics?
- Data science vs. data analytics: FAQ
- Key takeaways
Ready to get to grips with data analytics and data science? Let’s get cracking!
1. What is data science?
Data science is a multidisciplinary field that, as the name suggests, focuses primarily on data. An area of scientific study, it can be applied in any number of areas; from finance to retail, healthcare, e-commerce, and much more.
As a multidisciplinary field, data science brings together skills ranging from data analytics and machine learning to computer science and artificial intelligence, to name a few.
The aim of data science—in a nutshell—is to parse and research vast amounts of raw, unstructured data to devise strategic questions that will help drive an organization forward. If this sounds ever-so-slightly nebulous, that’s forgivable! Data science is a highly varied and complex role, and a data scientist’s exact responsibilities depend on the specific priorities defined in their organization’s strategic plan.
Because of this, an indispensable aspect of a data scientist’s job is to maintain oversight of both their organization and the internal and external factors that affect how it operates. Based on the information they can glean from big data, a data scientist will research and identify new opportunities that a business could pursue or areas of interest they might deem worthy of further exploration.
As a rule, data science involves many complex and interlinked tasks. It could involve data modeling, building algorithms from scratch, managing large teams or stakeholders, building and implementing new data structures, and generally being the ‘go to’ data expert within an organization.
The main takeaway, though, is that a data scientist’s focus is less on micro, day-to-day concerns and more on asking macro, longer-term strategic questions. For this reason, data science is usually quite a senior role.
Learn more: What Is Data Science? A Comprehensive Guide
2. What is data analytics?
Data analytics is a single discipline within the umbrella of data science, as well as being a standalone field in its own right.
While data science focuses on asking broad, strategic questions, data analysts generally have a more narrow and specialized role, seeking out the answers to specific questions.
For instance, a data analyst’s job might involve identifying which particular product features users prefer. They might have to uncover how marketing spend improves conversion rates in order to help target it better. Or they may have to identify how customer buying habits change depending on the weather.
While data analysts require fewer skills than a data scientist, unlike data scientists, they’ll usually have a more niche understanding of a particular area of a business. Rather than total oversight, they might operate in an individual department such as sales, accounting, DevOps, marketing, and so on.
One of the reasons that data science and data analytics are so often confused is because both work with big data. However, by the time data analysts use these data, they’re usually organized into a more structured format suited to the specific question that the analyst needs to answer.
For this reason, data analysts take a much more organized approach to understanding data. Their process involves following a relatively strict series of steps, using tools and techniques like Python, SQL, and data visualization software such as Tableau, to collect, clean, and analyze a dataset. This process (and the associated tools) helps data analysts create actionable insights that a business can execute. These insights commonly support decision-making.
You can learn more about data analytics here. Now that we can define the two disciplines, we can ask the big question: data analytics vs. data science—what’s the difference?
3. What’s the difference between data science and data analytics?
As we’ve touched on, the main factor distinguishing data science from data analytics is its end goal: data analysts tend to focus on a niche and specific area (e.g. sales, e-commerce) while data scientists take a holistic view of how an organization runs.
This requires a broader understanding of the business landscape, what competitors are doing, how different departments interact, and so on.
Many data professionals start their careers as data analysts before proceeding into data science. While the line between the two is blurry at times, we can largely divide them as follows:
- Data science skills include data modeling, predictive analytics, advanced knowledge of maths and statistics, and a high level of expertise in software engineering/programming (using numerous languages).
- Data analytics skills include business intelligence tools, solid statistics knowledge, intermediate programming skills, and the ability to explore data using SQL and Python.
- Data science focuses on the macro, asking strategic level questions and driving innovation.
- Data analytics focuses on the micro, finding answers to specific questions using data to identify actionable insights.
- Data science explores unstructured data using tools like machine learning and artificial intelligence.
- Data analytics explores structured data using tools like MS Excel and data visualization software.
Will explains the difference between data analytics and data science in the following video:
Data science vs. data analytics: an analogy
Since all this can be a little hard to grasp, it can help to use an analogy.
Let’s suspend disbelief for a moment and imagine a business as a human body. In this case, a data scientist would be a general practitioner, while a data analyst would be a specialist consultant. Both have crucial roles to play in guaranteeing the health of their patient (or business).
Firstly, the data scientist’s (or GP’s) job is to take a holistic understanding of the entire patient. Broadly, they must know how different elements work and interact while understanding the impact outside factors have on the patient’s overall health. This knowledge allows data scientists to uncover illuminating questions about a patient’s well-being that others might not know to ask.
Meanwhile, the data analyst (or specialist consultant) focuses on a particular body part (or business area). The data analyst is capable of answering specific questions about their area of expertise (say, the heart, or the brain) using specialist knowledge. As such, they can identify solutions to specific problems (heart palpitations, for example). However, the GP/data scientist will still take oversight of the patient’s overall health.
In short, data scientists and data analysts both play vital roles in the healthy running of a business, and both inform each other’s work. However, despite overlapping skills, their overall objectives differ.
4. Data science vs. data analytics: FAQ
Next up, we’ll answer some of the most common questions about data analytics and data science.
How does a data analyst become a data scientist?
As mentioned, data science requires a more sophisticated skill set than data analytics. However, many data scientists start their careers as data analysts before progressing into the field.
While data analysts usually (although not always) require an undergraduate degree in a field like math or statistics, to move from data analytics to data science, you will most likely need to gain a higher level qualification. Most data scientists hold a Master’s or Ph.D. in a field like information technology or finance or a particular domain area, such as the sciences.
In addition to a qualification, you will need to start expanding your skillset. Data analytics is vital to data science, but it is just one of many skills you will need. In addition, you should start training skills like machine learning, artificial intelligence, higher-level statistics, and broader business knowledge.
This means developing your leadership, management, and communication skills, on top of the specialist technical skills required for the role.
Read more: How to Make the Transition From Data Analyst to Data Scientist
Should I study data analytics or data science?
Naturally, the answer to this question depends on your career goals. But there isn’t necessarily a compulsion to study either—at least, not if you only want to become a data analyst.
Are you looking to enter data analytics and already have an undergraduate degree in an unrelated subject? Don’t worry: you can usually ‘top-up’ the necessary entry-level skills with a certified short course or data analytics bootcamp.
Meanwhile, if you’re looking to jump straight in as a data scientist, you will, in all likelihood, need a postgraduate degree. This being the case, you should aim to study data science or a similar field. Most data science degrees at this level include data analytics as part of the package. There are also data science bootcamps on the market, if you’ve already got experience as a data analyst and want to move up in the field.
What skills do I need to become a data analyst?
In an ideal world, before becoming a data analyst, you will have a Bachelor’s degree in a data-related subject.
However, you’ll find that these days this is not by any means a strict requirement. As long as you can demonstrate the following baseline skills with another qualification or certification:
- Solid understanding of probability and statistics.
- Strong management and communication skills (for interacting with teams).
- Analytical and problem-solving mindset.
- Expertise in managing relational databases and other data systems.
- Data wrangling and manipulation skills.
- Knowledge of business intelligence and visualization tools, e.g. Power BI or Tableau.
- Baseline knowledge of SQL, Python, R, and MS Excel.
What skills do I need to become a data scientist?
In addition to the skills outlined for data analysts, data scientists require a great many other skills. While the list depends on the role at hand, some of the following are required:
- Masters or Ph.D. in a data-related subject.
- Domain expertise in the field you will be working in, for instance, finance or product development.
- Complex mathematical modeling skills.
- Machine learning, deep learning, data analytics, and artificial intelligence.
- Big data manipulation tools, e.g. Apache Spark, Hadoop, TensorFlow, MySQL.
- A high degree of proficiency using different programming languages, including Python and R, but potentially others like JavaScript, C, and C++.
- Aptitude working with structured and unstructured data.
5. Key takeaways
In this post, we’ve explored the answers to the question: data analytics vs. data science—what’s the difference? As we’ve seen, the distinction between the two is not always clear-cut, which is why the terms are sometimes used interchangeably.
The main takeaways are that:
- Data science is a scientific discipline that explores all aspects of unstructured data. It asks complex strategic questions and aims to drive innovation.
- Data analytics is a specific process for answering known questions. It uses existing, structured data to produce actionable insights that drive decision-making.
- Data science is generally considered more senior than data analytics, but data analysts may have more in-depth knowledge of a particular domain area than data scientists.
If you’re considering a new career in data analytics or data science, you’re in luck. Whichever discipline feels right for you, both roles are in high demand—a trend that does not look set to change any time soon.
To learn more about data analytics and data science topics, why not sign up for this free, 5-day data analytics short course, or check out the following articles: