Summary: For all those who have heard of Data Analytics and Data Science, but still don’t fully understand what the differences are between them. Also for those who have not heard of Data Analytics or Data Science and want to learn something general about them.
Like every year before Christmas, I met with my friends. A few years ago, we all studied Computer Science at the Warsaw University of Technology. This time because of the pandemic we met online. While enjoying our online time together, my friend asked about the differences between Data Analytics and Data Science. At that time, I realized that this would be a good topic for my first article.
In recent times, the use of data has become a large part of the tech world. We have been able to observe increasing computing power and other favorable conditions, such as scientific developments. New ways and tools that use a huge volume of data have emerged. As a result, we hear a lot about Data Analytics, Data Science and other areas around it. It is problematic for many people to define the differences and similarities between these concepts. Today, I will briefly try to explain Data Analytics and Data Science and I will give you short examples of possible tasks for people who work in this area.
Data Analytics
Data analytics involves examining large data sets, drawing meaningful conclusions from them and answering questions that drive strategic business decisions. Data analysts study data to identify trends, create graphs, visualize and describe findings in reports. The responsibilities of data analysts can vary significantly from industry to industry and company to company. Generally speaking, data analysts find solutions to existing problems using historical data.
Example problem and task to be performed
Company X runs an online shop. The team of data analysts got the new task. This task was to visualize and check the company’s profits from the sales of specific products. Data analysts have at their disposal historical data on the sales of the products concerned. From this data, they calculate the company’s profits for each month and visualize them by creating graphs. They save these charts along with their observations and create a report of their analysis.
Data Science
If Data Science is the home for various methods and tools, then Data Analytics is one of the rooms in that home.
Unlike Data Analytics, Data Science is not limited to answering specific questions and problems that already exist. A person working as a Data Scientist asks questions, tries to build relationships and solves new problems. The questions posed may be so non-obvious that no one previously realized that the answers could introduce further innovations and profits. All this sounds rather abstract, so let’s try to make it more concrete. Data Science is an interdisciplinary field using statistics, econometrics, data analysis, artificial intelligence methods and domain knowledge. Data Scientist builds and creates statistical models and machine learning algorithms mainly for predictive purposes using structured and unstructured data. Many people who try to define what Data Science is, write that if Data Science is a home for various methods and tools, then Data Analytics is one of the rooms in that home.
Example problem and task to be performed
Company X is not happy with the profits from products’ sale about which a Data Analyst has previously provided a report. This company decides to ask the Data Scientist group if they see any way to increase these profits. People working in the Data Science group ask themselves questions such as whether customers who have bought a specific product have any distinctive characteristics. Data Scientists use different methods and look at what customer characteristics might have made them more likely to buy a particular product. With such information, they can tell which customers to target with various advertising campaigns. Such actions can increase sales and profits.
Typical skills
The skills and knowledge of standard tools can vary significantly among both Data Analysts and Data Scientists. It all depends on the industry and often the companies in which people in these areas work. A typical and visible difference is often programming. It is sometimes possible to be a Data Analyst without knowing programming languages, but it is quite impossible to be a Data Scientist.
Typical Data Analyst skills:
- Structured query language SQL
- Critical thinking skills
- Presentation skills
- Statistics
- Data visualizations
- Report writing
- Microsoft Excel
- R or Python programming language
Typical Data Scientist skills:
- Structured query language SQL
- Critical thinking skills
- Presentation skills
- Statistics and linear algebra
- Data visualizations
- At least one programming language: R, Python, Scala
- SAS software – especially in banking
- Machine learning algorithms
Conclusions
Although there are differences and similarities between Data Analytics and Data Science, there is one thing without which neither of them would exist – data, of course. We can observe a high demand in the job market for people specializing in Data Analytics and Data Science. It is unlikely to change. Many expect that it will grow further. Areas such as Machine Learning, Deep Learning, and Artificial Intelligence are also strongly associated with Data Science and this will be one of my next article topics. Are you interested in such areas? Follow my blog! 🙂 Most probably you will find something for yourself here.
References
- Data Analytics vs. Data Science, Kristin Burnham
- Data Science vs. Data Analytics — What’s the Difference?, Dana Liberty
- Data Science vs Data Analytics — How to decide which one is right for you?, Springboard India
- Data Science vs. Data Analytics vs. Machine Learning: Expert Talk, Srihari Sasikumar
- Photo by Myriam Jessier on Unsplash