Due to many technological inventions, there are large amounts of data that is being created around us. To some in the past, it meant nothing and was just an accidental collection of information. Over the years there is great excitement built around this data, called ‘Big Data’. Scratching the surface of big data opened gateways to insights that were beneficial for organisations. Very soon there was a conscious effort in collecting and storing this data, which was supposed to be priceless. Then institutions faced a new challenge, they could successfully accumulate data, however, interpreting and unlocking the real value from the data was becoming a daunting task.
At the core of the enterprise data warehouse, there are humungous amounts of data in structured, unstructured or semi structured format. Data science is the method through which innovative and creative solutions are applied, to get insights from this data, to generate business value.
And thus enters the ‘Data Scientist’.
Clearly one title with numerous definitions. Some call them Analytical Experts, some call then people who know how to perform Statistical Analysis, to some they are Mathematically Sophisticated Data Engineers, to some they need to know skills in Machine Learning and Predictive Analysis, the list is vast and long.
Who are data scientists –
Professionals who are an expert in analytical and technical skills required to solve complex problems and to have the inquisitiveness to find what problems need to be solved. Rest everything else is just semantics.
They are masters of many skills, part mathematician, computer scientists, part trend spotters etc…, they are the bridge that connects the business world with the IT worlds. Clearly, they are scarce in numbers, as to have so many sought after qualities in an individual is rare, hence they are well paid and headhunters are always looking for the perfect candidate to fill in the numbers.
They start their careers usually as a Data Analyst, or a Statistician and progress upwards. However, with times the approach towards big data has evolved specially with big storage and processing technologies entering the markets. Nowadays a data scientist’s role as an academic origin and one will find many universities who have upgraded their curriculum to accommodate this new requirement.
A data scientist job description is vast and may encompass many tasks, but more or less the role would involve.
- Working fluently with programming languages like R, Python, SAS.
- A working knowledge of statistical tests and distributions
- Understanding and functioning knowledge of analytical techniques such as Machine Learning, Text Analytics etc.,
- An insight to recognize patterns and ability to spot trends in data, which can assist in the overall functioning of a business
- Using data driven techniques to solve business related problems
- Transforming data into a more usable format
Even if you are not academically trained on skills required to excel as a data scientist, one can always learn technologies commonly used by a data scientist like Data Visualisation, Deep or Machine Learning, Text Analytics, Pattern Recognition, knowledge of coding languages, etc…,
Along with the technical know-how, a data scientist is also someone who has a Business Acumen and exceptional Communication Skills, after all he has to not only give insights and information on how to use it but also tell a story based on the technical findings to the non-technical staff, in a way where data driven decisions can be made.
The impact of big data with the correct insights and application is huge, a data scientist thus adds tremendous value in any industry or process, wherever applied, as it helps people take calculated and informed decisions based on facts and not feelings. If you are interested in pursuing your career as a Data Scientist, then join our Data Science Prodegree which will help you build your career on the right track.