Difference between Data Science vs Data Analytics

The Difference between Data Science vs Data Analytics

What is Data Science?

Data Science is the process of using scientific methods, tools and systems to shape raw data into meaningful information. Data scientists use machine learning algorithms to build complex, predictive models that find patterns and trends in the data. Data Science software is used to manipulate, organise and build predictive data models.


What is Data Analytics?

Data Analytics is the science of analysing either raw or processed data to derive useful insights, that can then be turned into actionable plans or strategies. Data Analytics often builds on the work first done by Data Science, using the predictive information to make decisions.


What is Business Analytics?

Business Analytics is the application of Data Analytics tools and techniques in a business context. The historical data of a business is statistically analysed in order to understand past performance, predict future market trends, create more accurate budgets and much more.


The Function of Data Science vs Data Analytics

The function of Data Science is to build the foundation from which Data Analytics then works on. The key functions associated with this field are;


  • Programming: Coding algorithms and computer models that can analyse large data sets. The most common programming languages used in Data Science is R, SQL and Python.


  • Data wrangling: Cleaning the data and then organising the data coherently so that it’s both easier and more readily available to use.


  • Statistical modelling: Using statistical assumptions and mathematical models such as regression analysis, k-mean clustering and more, to identify relationships between two or more variables. This function is tied to Quantitative research methodologies.

The function of Data Analytics is to apply a set of analysis specific frameworks and tools to data sets in order to generate information that can be used to make decisions. These frameworks are;


  • Predictive Analytics: The use of past trends, patterns and historical data to make predictions about future events, and act accordingly. An example of this would be to increase the inventory count of an item that sees spikes in sales during a specific month or season.


  • Prescriptive Analytics: This uses all available data to determine the best strategy, action or plan that should be taken in a specific scenario, in order to reach the objective. It is considered a more advanced form of Predictive Analytics. An example of this would be e-commerce websites that show consumers a specific product they know would entice a purchase, based on that consumer’s lifestyle data, browsing patterns and previous purchase history.


  • Descriptive Analytics: The means of summarising data to analyse, understand and describe ‘what happened’ either in real-time or at a particular point of time in a business. An example of this would be KPI reports or dashboards that depict the current figures against an established benchmark.


  • Diagnostic Analytics: This uses data to understand and analyse ‘why something happened’. An example of this would be identifying why a social media campaign faired either very poorly or did very well, in order to either avoid or duplicate the parameters.


Stafford offers Online Data Science courses at a Postgraduate Certificate, Postgraduate Diploma and a Full Masters level. Speak to a Higher Education Consultant for a personal consultation. Available degrees are;



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