Data analytics and data science are two key terms thrown around in the tech and business world. What do they mean, and what’s the difference between data science vs. data analytics? Data analytics is concerned with performing statistical analysis on existing datasets to solve problems and find answers to current issues we don’t know the answers to. Data science focuses on creating actionable insights and predictions from raw and structured data, often in large quantities.
This article will give insight into the critical differences between data analytics and data science. First, we’ll explain what big data is, followed by a little more information on each role: data analyst and data scientist.
What is Big Data?
Big data can often be challenging to comprehend. Big data is usually more extensive and more complex than other large datasets and may contain multiple sources. Put simply, big data is too large to process and understand using traditional data processing methods. This is where data analysts and data scientists come in — their job roles are to interpret this data and present it to their company or organization.
The original definition of big data, prefaced by Gartner (2001) is as follows: “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
The Three V’s of Big Data
To better understand the deep learning of big data, whether as a data analyst, scientist, or curious individual, we must apply the three V’s: Volume, Velocity, and Variety.
When it comes to an understanding of big data analytics, the volume of this data matters significantly. Big data requires you to process an increased volume of unstructured data, e.g., Twitter feeds, sensor-enabled equipment, forum responses, or comments and reviews on webpages or mobile apps. This data can be difficult to comprehend; however, it’s crucial that there’s a lot of it in order to make valid claims. The volume of big data depends on the organization’s size and the questions that are being asked.
In regards to big data, velocity is the speed at which data is received and then interpreted. Some pieces of software can do this automatically, depending on the complexity and structure of the data. However, this is not always possible, making the velocity much slower as it’s done manually by a data analyst or data scientist.
Finally, we have variety. This refers to the different types of data that are available, both structured and unstructured. For example, data types may include audio, text, video, comments on forums, reviews, and other metadata. In the last few years, we’ve seen a rise in unstructured data (such as interviews, which then need to be transcribed), audio recordings, and video interviews.
Value & Veracity
Although the three Vs mentioned above are the go-to for big data, more recently, two new Vs have been introduced: value and veracity. For example, all data contains an intrinsic value, but this value cannot be understood until the data is understandable. Some data contains more intrinsic value than others, and this is determined by the data source and the truthfulness of the data, e.g., can you rely on the data source?
Big data is becoming more and more mainstream, especially for large tech companies (and others that deal in large quantities of data) to better understand their users and their products. For instance, companies such as Apple use big data to understand and map user experience and intentions and to help create new products that customers will actually be interested in — solutions to problems that others don’t yet recognize as obstacles.
Data Analytics vs. Data Science
As mentioned previously, both data analytics and data science are somewhat similar and often confused. To eliminate this confusion and to better help you understand the difference, we’ve provided a brief description of each role below.
What does a data analyst do?
A data analyst’s job consists of sorting through data to provide visual and written reports to uncover insights in a dataset. These datasets could be on any topic, whether a crime, government funding, or within the sports performance industry. Often, many data scientists practice first as data analyst, learning the ropes and better understanding data as a whole to become a big data professional.
What does a data scientist do?
A data scientist’s role is to collect and analyze data to gather valuable insights, later sharing these with their organization or company. Similar to a data analyst, the role of a data scientist exists across many different industries.
Unlike data analysts who provide insights via representations of data, data scientists are more significantly involved by creating their own experiments, cleaning data, finding patterns, building algorithms, and finally, sharing their data and newly found insights with their team in an easy to understand process.
What is the difference between data analytics and data science?
1. Data science is multidisciplinary
One of the main differences between data analytics and data science is that data science incorporates numerous disciplines, including data analytics, data engineering, machine learning, and software engineering, to name a few. In particular, data science relies heavily on machine learning and data analytics. Without traditional data analytics, whether performed by an analyst or a data scientist, it would be difficult and nearly impossible to understand big data.
Ultimately, a data scientist’s role is to understand and re-structure big data, identify patterns, and educate business leaders and decision-makers on their findings to adjust current practices for better, more effective results.
2. The unknown vs. the known
A data scientist’s role is to predict future events or further data by analyzing past data patterns. On the other hand, a data analyst looks at current data and perspectives to better understand current events. This fundamental difference is paramount – and a critical distinction between the two sets of expertise. Essentially, data scientists focus on the future, and data analysts center their attention on the now.
3. Hands-on machine learning experience
Data analysts are not expected or required to have hands-on machine learning experience. Similarly, those within this role are not likely to build statistical models or conduct advanced experiments to understand this big data better.
Data scientists, on the other hand, are expected to have hands-on machine learning experience and are required to build their own statistical models and conduct their own experiments. As you can see, the roles are somewhat similar, but a data scientist’s role is more advanced and a step up from a data analyst. This is why many data scientists start out as data analysts.
4. Addressing vs. formulating questions
Generally, data analysts are given questions to address by their business or organization. The request usually has to do with understanding a specific dataset to better benefit the business and their regular operations, e.g. cutting costs, increasing footfall, or understanding sales trends of distinct products or services.
Conversely, data scientists formulate these questions and provide solutions that will benefit the business. Usually, these questions are about events that haven’t happened yet; with greater focus on predicting the future as opposed to understanding current data and events.
5. Multiple sources vs. single sources
Data analysts typically use and interpret data from a single source, such as a CRM system, while data scientists collect and gain insights from multiple data sources — sources that are often disconnected and more complex to understand. This is why processes such as machine learning and statistical models are used to understand this big data better.
6. Visualization skills
Data analysts are not always required to possess business acumen or exceptional data visualization skills. Instead, their role is to interpret the data in an easy-to-understand fashion, not to implement changes to a business setting or real-world scenario. By comparison, data scientists are required to show business acumen and advanced data visualization skills, putting newly understood data to work in a business setting and contextualizing potential impacts on a business and its current decisions and processes.
Frequently Asked Questions
Can a data analyst become a data scientist?
Yes, data analysts can become data scientists. Many data scientists often start as data analysts, learning the big data world’s ropes and the various methods involved in interpreting and making sense of data. With this being said, an advanced degree is not necessary but may support you during the transition process.
Which is better for business: analytics or data science?
Business analytics is concerned with the analysis of data to make key business decisions, while data science uses statistics and various other methods to complement and inform business decisions. While there’s no correct answer, if you think you’d like to be more involved in a business decision, then a business analyst role is probably for you.
Data analyst vs. data scientist salary — which is better?
According to Glassdoor, the average salary for a data analyst ranges from $83,000 to $115,000, while data scientists earn, on average, upwards of $168,000 a year.
Data analytics and data science have different roles within the same industry; however, they’re somewhat similar. As we’ve discussed, data analysts focus on sorting through current datasets to provide insights and visualizations in response to a business or organization’s question or current problem. On the other hand, data scientists formulate their questions as well as the subsequent answers and solutions that will benefit the business, focusing typically on events that have not yet happened.
Many data scientists often become data analysts first, helping them to better understand big data and the many processes involved in its analysis. Think of a data scientist as a more advanced data analyst — they ask questions, use machine-learning, build statistical models, and conduct experiments. However, both roles share the critical goal of a better understanding of big data.