Is big data for you? If so, the demand is here and now.
Big data is already playing a huge role in our lives and is predicted to increase at a rapid pace, and deep learning and growing interest in AI is stimulating futher growth.
A McKinsey report dating back to May 2011 predicted that big data jobs will be in high demand, quoting huge figures for employment and investment opportunities for 2018 — heres the full quote:
“Now, the US government has earmarked $200 million to support research in big data. But the advancement and use of big data technology can be inhibited by a lack of deep analytical talent. By 2018, the US alone can face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as a 1.5 million managers and analysts with sufficient knowledge, to use the analysis of big data to make effective decisions”
So here we are in 2018 and big data has become a necessity for enterprise level applications to survive. With this emergence, data scientists are in high demand.
A data scientist is a professional with a deep technical background who can expertly manipulate data, and be well versed in analytical tools for huge amounts of unstructured data. These professionals can be people with advanced training in the quantitative area, such as statistics, mathematics, and economics. If you come from these fields, you have a head start!
As a data scientist you need to have a combination of skills to handle raw data and unstructured data, strong technical knowledge, strong analytical skills and background. Being able to grasp both technical and business sides of an organisation is a must for a data scientist, manipulating data for practical application.
In other words, what makes the data scientist unique is the ability to use technical skillsets to solve actual real world problems.
A range of complex skills are required, advance quantitative knowledge, business skills, technical experience and problem solving capacity. An analytical, curious individual fits the roll well, who can tell a story with the data in overcoming business challenges.
The following qualities characterise a data scientist:
A variety of academic backgrounds acting as a foundation. Graduate or PHD level education in computer science, statistics, applied mathematics, physics or ecomomics provide solid foundations, where proficiency in applied mathematics is a must.
Technical expertise is required. Experience in software engineering, software programming or developing who have developed programming skills.
Mathmaticians or statisticians with advance qualitative skills.
An individual passionate about the idea of data, consistently seeking creative ways to solve data problems and collection of new data, or data mining. Someone who asks “what can we do next?”, as opposed to “what happened?”
Being skeptical of the work, consistently examining the work critically. Be willing to question old assumptions and to re-analyze business problems to come up with fresh solutions.
Understanding and experience of business, knowing and respecting the value of analytics and how analytical tools fit within the organization.
Business knowledge is essential in order to position yourself and your solutions correctly to ensure actionable insights can be derived, and therefore make a positive impact on the business. A data scientist needs to have experience in a variety of domains, and in working with different issues, so that solutions can be quickly realised, ranging from a new statistical model to adopting a particular machine learning algorithm.
Programming knowledge for Python, R, Hadoop, HBase, Cassandra or SAS is required; Python being widely adopted in the machine learning space for its simplicity and ease of use, making it a good entry into the space.
A data scientist can create the tools used to interpret and translate the streams of data into innovative new products.
Facebook’s data science team consists of 12 researchers. They use math, statistics, computer programming skills and social science to mine the data for insights, which keeps Facebook’s application intelligence up to date.
One of their innovations, the Like button, plays a very important role. Letting your network of friends know what you have liked and being able to see who else liked a social media item has fuelled the ability to categorise interests. Within the first 5 months after this feature’s launch, Facebook catalogued more than 5 billion instances of people listening to songs online; the like button is an extremely powerful yet simple tool.
Does the role of a data scientist an attractive proposition for you? If so, now is a great time to get into the role as big data continues to dominate enterprise level applications and the forefront of machine learning capabilities.
