Data Analytics | Data Science | which training course ??
As Big Data becomes an integral part of the data-driven enterprise, businesses are encountering problems securing the skills they need to make sense of their data. Collecting data is relatively easy – extracting value from it, less so.
As a result, there has been a surge in demand for professionals who can put raw data to work for the business. Spiralling salaries mean that employers may be better able to control costs by retraining or upskilling their existing workforce.
But as they build-up their Big Data programs, businesses will need to understand the differences between data scientists and data analysts. Although they sound very similar, there are some differences between the jobs – which is very important when trying to build an effect Big Data team.
Unfortunately, there is no industry consensus on exactly what “data science” actually means. Most experts do agree that data science is a statistics-based discipline; almost every activity performed by a data scientists has a goal tied to generating and interpreting statistics.
Applied statistics may sound less-than-exciting, but Harvard Business Review once claimed that data scientist was “the sexiest job of the 21st Century”.
Data scientists don’t just generate statistics either. They also manage and manipulate unstructured data sets to create the links which reveal deeper insights. Ultimately, data scientists build and test statistical models against data sets, identifying the questions that a business needs to answer in order to reach its strategic goals.
To make this happen, data scientists will need to be skilled in cleaning and formatting data so it is ready for analysis. Familiarity with Big Data management technologies like Hadoop and NoSQL are essential. Most scientists will also develop routines and frameworks to assist with these data management tasks, typically using the Python and R programming languages.
Python is a useful skill for Big Data teams, thanks to an extensive range of data science libraries. The Pandas, Agate and Bokeh libraries are all tailored to the needs of the data scientist, helping to dramatically reduce the development time (and cost) for Big Data applications – so long as your developers know how to use them properly.
Alternatively, R offers a selection of classes and methods which can be used to create statistical software for use in Big Data operations. Again, these pre-built classes help to reduce development costs.
In advanced Big Data programs, scientists will be heavily involved in developing machine learning algorithms and artificial intelligence. These systems may later be used by data analysts as part of their job, and will help to establish the frameworks used by your business to extract value from data. Without machine learning and artificial intelligence, it will be impossible to cope with the ever increasing volumes of data collected by your business.
On the face of it, data analytics is very similar; analysts sift through vast information stores and unstructured data sets to derive actionable insights for the business. Unlike data science however, analytics activities are aligned with business goals. Analysts are typically given a business problem, and then use all available data to formulate a solution; they start with a specific goal, and all efforts are focused on it.
In terms of technology, data analysts will also be adept at manipulating and managing Big Data stores using NoSQL queries, but they will not be required to transform data. Generally, analysis is performed using business intelligence (BI) tools, like Power BI, Pig and Hive, rather than the custom code and frameworks that scientists have to build themselves. This level of abstraction allows analysts to remain focused on business goals, rather than the low-level technologies and statistical models that drive them.
Importantly, tools like Power BI greatly simplify the process of querying, sorting and reporting on data. Once connected to corporate data stores, Power BI allows data analysts to unlock insights, and visualise data quickly and easily. The quicker these insights can be created and shared, the faster your business can put them into action, shortening the time to ROI and eventual profit.
A subtle, but vital, difference
Because their roles are complementary, data scientists may work alongside analysts – but their responsibilities are actually distinct and separate. Both are also essential to successful Big Data operations.
To learn more about retraining your team to work with Big Data – as a scientist or an analyst – please get in touch.
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