In the dynamic world of data, two terms are often used interchangeably – data analytics and data science. This causes confusion because while they share similarities, there are crucial differences. In this article, we will demystify these fields and explore the unique features of both.
How Did These Two Fields Evolve?
The origins of data analytics can be traced back to the early days of computing when businesses started using computers to analyse data for decision-making purposes. In the 1960s, the concept of Business Intelligence (BI) emerged, making it easier to analyse business data in a systematic manner. With advancements in technology, statistical methods, and the creation of more specialised tools, data analytics was able to evolve into what it is today.
On the other hand, data science developed as a particular field in response to the rapidly growing volume and increasing complexity of data. Data science as a phrase dates from the early 2000s. It brought together different areas of study including statistics, computer science, and industry knowledge to get valuable insights from huge sets of data.
Both fields have experienced rapid growth and transformation, thanks to new technologies. Currently, both data analytics and data science play pivotal roles in shaping business strategies, fostering innovation, and addressing complex challenges in a range of sectors.
What Is Data Analytics?
Data Analytics is the science of analysing raw data in order to make decisions which will improve performance. Harvard Business School explains the key concepts of data analytics this way:
Descriptive, which answers the question, “What happened?”
Diagnostic, which answers the question, “Why did this happen?”
Predictive, which answers the question, “What might happen in the future?”
Prescriptive, which answers the question, “What should we do next?”
Key concepts:
- Data processing: Organising and cleaning data for analysis.
- Statistical methods: Using statistical techniques to draw insights from data.
- Visualisation: Presenting findings in charts, graphs, or other visual formats.
- Historical focus: Emphasising the understanding of past data for current decisions.
- Decision support: Providing information to help in decision-making in various industries.
Tools and Techniques:
The choice of tools and techniques in data analytics depends on the specific requirements of the analysis, the nature of the data, and the goals of the organisation conducting the analysis. For example:
- Microsoft Excel: Widely used for basic analytics and visualisation.
- SQL: Used for managing and querying databases.
- Tableau, Power BI: Used for creating interactive data visualisations.
Industries and Sectors:
Data analytics is used in different industries like finance, marketing, healthcare and retail. In South Africa, banking and telecommunications are examples of sectors that rely heavily on data analytics to improve customer experiences and make operations more efficient.
What Is Data Science?
Data science is a broader field that includes data analytics. It involves finding useful information from all forms of data using a range of scientific methods, processes, algorithms, and systems. Data science doesn’t only look at the past and the present but also predicts future trends.
Key concepts:
- Interdisciplinary approach: Including statistics, computer science, and domain-specific knowledge.
- Predictive modelling: Building models to forecast future trends and outcomes.
- Machine learning: Using algorithms to identify patterns and make predictions.
- Big data handling: Dealing with large and complex datasets, both structured and unstructured.
- Innovation and strategy: Aiming to predict and shape future developments for strategic planning and innovation.
Tools and techniques:
- Python and R: Common programming languages for data manipulation and analysis.
- Hadoop, Spark: Used for processing and analysing big data.
- TensorFlow, PyTorch: Frameworks for implementing machine learning models.
Industries and Sectors:
Data science plays a crucial role in cutting-edge industries like artificial intelligence, healthcare, and e-commerce. In South Africa, emerging sectors such as fintech and agritech are increasingly adopting data science to innovate and improve their operational processes.
Data Analytics Vs Data Science: A Comparative Analysis
- Methodologies and approaches:
- Data analytics: Data analytics looks at past data to help make decisions.
- Data science: Data science looks at data holistically, using statistics, machine learning and predictive modelling.
- Case studies:
- Data analytics: Analyses customer buying patterns to improve marketing strategies.
- Data science: Creating a model to predict disease outbreaks using health information.
- Skills and education:
- Data analytics: Analytical and communication skills and usually, a bachelor's degree.
- Data science: knowledge of how to programme, an understanding of statistics, and usually a master's or PhD.
What are the different career paths?
- Data analytics:
- Data analyst: Analysing and interpreting data to find practical suggestions for action.
- Business analyst: Focusing on business processes and requirements to improve efficiency.
- Market research analyst: Studies market trends to help businesses make better decisions.
- Data science:
- Data scientist: Using advanced analytics and machine learning to solve complex problems.
- Machine learning engineer: Building and using machine learning models.
- Data engineer: Designing, constructing, and maintaining the systems that generate data.
What are the career opportunities and salary trends?
In terms of job market trends, both data analyists and data scientists are in demand in South African and globally. According to Talent.com, the average data analyst salary in South Africa is R542 810 per year. Entry-level positions can start at around R300 000 per year, with experienced analysts earning up to R700 000 annually. The average data scientist salary in South Africa is R874 140 per year with positions ranging from entry-level at R480 000, to experienced professionals earning up to R8 667 729 annually.
Educational Pathways and Resources
If you are keen to develop fundamental knowledge in big data, then The IIE’s Varsity College Postgraduate Diploma in data analytics might be the right course for you. It is available as full-time or distance study. When you graduate the programme, you will have the theoretical and technical skills in data analytics to inform business decisions or articulate into an appropriate Master's degree.
About The IIE’s Varsity College
The Independent Institute of Education (The IIE) of which Varsity College is a brand, is South Africa’s largest registered and accredited private provider of higher education. At Varsity College we understand that no two students are the same or learn the same. That’s why we make sure a student’s education is shaped around them; how they like to learn, what they are passionate about, what makes them tick, and what makes them thrive. Our Education by Design approach allows students to grow into their best, and creates a space where they can live, learn and play – their way.