Information has become the currency of modern businesses, and an increasing number of organisations rely on collecting, storing, and processing data to improve their business models and revenues.
But what is the difference between Data Science and Data Analytics? Which one should you choose and what skills will you develop if you study one or the other? Buckle up as we’ll dive deep into these details to clear any confusion and help you choose the discipline that works best for you.
Data Science vs Data Analytics: scope and focus
Before we even begin, let’s set some things straight: Data Science and Data Analytics have different goals, but the fundamental resource for both of them is data. These disciplines aren’t only related; Data Analytics is often considered a branch or subdiscipline of Data Science.
To sum them up in a few words, Data Science explores and tests new methods to use and interpret data, while Data Analytics focuses on analysing datasets and finding insights and solutions to problems.
Data scientists use prototypes, algorithms, and predictive models to discover new ways to make use of data or come up with new questions or patterns that can be useful in the future. These efforts help to drive innovation and bring questions for which we didn’t even know we needed an answer.
Data analysts use their skills to filter data, extract relevant information, and come up with solutions for businesses and institutions from various sectors, like healthcare, finance, insurance, travel, energy management, etc. Their insights are used to improve the decision-making process, to set KPIs (key performance indicators), and for other business purposes.
Here are a few universities we recommend for Data Science and Data Analytics degrees:
- Kansas State University, the US
- University of Leeds, the UK
- Institut Polytechnique de Paris, France
- University of Europe for Applied Sciences, Germany
- Barcelona Technology School, Spain
Data Science vs Data Analytics specialisations
If a general Data Science degree is too broad, there are other subdisciplines you can choose from, in addition to Data Analytics. Here are several options:
- Data Engineering
- Data Mining
- Database Management and Architecture
- Data Visualisation
- Business Intelligence
Due to constant changes in the sector and the demand for specialists with interdisciplinary skills, it’s not uncommon to find merged courses, such as Data Science and Analytics or Data Science and Business Analytics.
Data Science vs Data Analytics classes
You’ve probably heard it many times before, but it remains true: the classes or subjects you’ll study can vary greatly from one university or country to another. This is why you often hear people (including us) saying that you should check the curriculum of each course to find out if it meets your expectations.
Still, we want to give you an idea of what you can expect to study during a degree in either Data Science or Data Analytics. So, take a look at a few examples below:
Data Science classes
- Discrete Mathematics
- Intermediate Statistics
- Database Systems
- Principles of Data Mining
- Data Security
- Data Structures and Algorithms
- Software Development
Data Analytics classes
- Calculus and Linear Algebra
- Machines, Languages, and Computation
- Modelling and Statistical Decision Making
- Data Mining
- Essential Statistics
- Pattern Recognition
Data Science vs Data Analytics skills
We’ve listed several key abilities for each discipline. Even though there are important distinctions, keep in mind that some skills can overlap:
Data Science skills
- Attention to details
- Software development
- Machine learning
- Proficiency in big data tools: Hadoop and Spark
- Programming abilities: Python, R, Scala
- Expertise in SQL, Cassandra, MongoDB
- Knowledge of visualisation tools: QlikView, Tableau
Data Analytics skills
- Attention to details
- Database management and reporting
- Proficiency in R, SAS
- Knowledge of SQL, Excel, Power BI
- Business acumen
To stand out on the job market, you should take advantage of any internship or placement opportunity available during studies. While your professors will do their best to teach you, nothing compares to hands-on experience and applying your knowledge in real-life scenarios.
Data Science vs Data Analytics jobs and salaries
Spoiler alert: Data Science and Data Analytics jobs are here to stay.
The number of companies relying on data is growing, and so is their need for specialists who can manage and use data effectively. According to the Emerging Jobs Report from LinkedIn, Data Science is the 3rd fastest growing sector in the US, with a 37% annual growth.
Want to hear more good news? You will be well-paid. Don’t believe us? Take a look at the job titles listed below and the average annual salaries in the United States, based on data from Glassdoor and PayScale.
Data Science jobs
- Data Scientist - 95,950 USD
- Data Architect - 72,700 USD
- Data Engineer - 72,300 USD
- Machine Learning Specialist - 77,150 USD
- Statistician - 76,900 USD
Data Analytics jobs
- Data Analyst - 69,000 USD
- Business Analyst - 68,350 USD
- Operations Analyst - 54,250 USD
- Quantitative Analyst - 106,750 USD
- Data Consultant - 76,400 USD
Data Science vs Data Analytics vs related disciplines
We’ve already explained the main differences between Data Science and Data Analytics. But there are other related disciplines out there making things even more confusing for students. Let’s look at the most common ones and describe them in a short but easy-to-understand way.
Data Science vs Data Analytics vs Data Engineering
A data engineer typically interacts with data before a data scientist or analyst. He or she is responsible for creating data pipelines, eliminating errors, and making sure the data is reliable and ready to be used by data analysts or scientists.
Data Science vs Data Analytics vs Big Data
All data that cannot be handled using traditional data processing software is labelled as ‘Big Data’. To collect, sort, and store this type of information, big data engineers are brought in. They are responsible for managing a company’s Big Data infrastructure and tools.
Data Science vs Data Analytics vs Machine Learning
Machine learning (ML) engineers work closely with data scientists. They develop algorithms that help machines to identify patterns and learn. This is achieved by feeding the machine data, allowing it to learn, and then testing it in a new situation. When models are successful, it is the job of ML engineers to scale and apply it on large real-time data.
Data Science vs Data Analytics vs Data Mining
Data Mining is also referred to as Knowledge Discovery or Knowledge Discovery in Data (KDD). Data mining experts use complex mathematical algorithms to find patterns and future trends based on large sets of data. The job of a data mining analyst isn’t only to identify these trends, but also to predict their outcome and evaluate the accuracy of predictions.
Data Science vs Data Analytics vs Business Analytics
Business Analytics is very similar to Data Analytics. They both aim to identify trends and insights that can help businesses grow and make better decisions. The main difference is that data analysts only share their conclusions and leave the decision-making process in the hands of the management and stakeholders.
Business analysts, on the other hand, have a more significant role in this process. They come up with their own suggestions and are actively involved when business leaders decide the direction of their company.
Congrats, you’ve made it! Maybe it wasn’t easy going through all this information about Data Science and Data Analytics. But now that you know the difference, it should be a lot easier to choose a study programme.
Which of the two disciplines attracts you more, and why? Have Machine Learning or Business Analytics made you reconsider your choices? Share this article and let us know!
Remember, you can also apply for online Bachelors in Data Science or Data Analytics.