6 Things You Should Know About Data Science (And How It Can Help Your Organization)
Data science is an increasingly vital discipline that has applications in almost every industry and sector, so getting to know a little more about it is certainly sensible, especially as its importance is only going to grow.
With that in mind, here are a few things to note about data science which should demonstrate the benefits it can bring to all sorts of organizations.
Data science consulting can help you get started
The good news is that with the help of a professional data science consultant you can accelerate the rate at which your business adopts the tools and methodologies used to extrapolate meaning from the vast reserves of information which most organizations are responsible for today.
Most of all by working with an outside contractor, this means you do not need to go through the rigmarole of hiring a full time team member to take on this role, which could be costly as well as potentially being overkill, depending on the scale of your operations.
It encompasses many skills
Although it is independently defined, data science is a multifaceted practice which falls back on skills that cover an impressively broad array of subcategories.
Courses in data science include everything from mathematics and statistical analysis to data engineering, advanced computing knowledge and, perhaps most importantly, an ability to convey potentially complex findings in a way that non-experts can understand. Visualizing data for the purposes of reporting can help with decision-making in an organization, for example.
Data sources are varied
While data scientists across different businesses may have similar aims in their roles, the sources of the data involved can vary wildly.
Information can be sourced from the search terms that people use when looking for products to buy online, or from the biometric data gathered by fitness gadgets. It can originate from social media interactions, or from email marketing campaigns.
This means that while the end goal may be to allow organizations to better serve their customers, the routes to getting there can diverge significantly, as each source needs its own unique approach to optimize the accuracy of the insights gleaned.
Automation & machine learning are catalyzing data analysis
Modern data science specialists are not expected to trawl through the figures manually to seek out the most relevant data points, but instead often make use of automated solutions to assist them in their work.
Machine learning services, often powered by the cloud rather than being run on local hardware, are especially crucial when it comes to automating the analysis of large quantities of data. And as the name suggests, the algorithms at the heart of this process can automatically improve themselves over time, which further speeds up the rate at which insights are uncovered.
There are two main types of data
When it comes to scientifically scrutinizing information for the benefit of businesses, it is necessary to categorize the data in one of two ways; either structured or unstructured.
Structured data is easily categorized, making it suitable for storage and organization within a more traditional database infrastructure. Unstructured data, such as might be gleaned from social media, needs to be handled by more advanced tools.
Demand for data scientists is on the rise
Unsurprisingly because data science has so much to give to modern organizations, there is plenty of demand for specialists in this field, which explains why they command above-average salaries.
In some areas there is even a shortage of skilled workers, necessitating the aforementioned outsourcing of this to consultants. As more people train in the discipline of data science, this deficit should be addressed.
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