subject: Why Are Data Scientist Jobs, Data Mining Jobs And Algorithm Jobs In Greater Demand? [print this page] In the last few years there has been a huge increase in demand for big data analysis and therefore more need for data scientist jobs, data mining jobs, algorithm jobs and machine learning jobs with many people choosing data mining careers. But why has there been such an increase in demand for jobs in data mining? What has changed that makes big data analysis so important for many of the larger companies?
More companies have data that needs to be analysed
Nowadays organisations have access to a lot more data - storage is cheaper and simpler, so they need people with data mining jobs to sort, analyse, and make sense of this data, and people with algorithm jobs to write the algorithms to create the new products based on this data.
Traditionally companies had specialists with data mining jobs to trawl through data and statistically analyse it for patterns and add some meaning to the numbers. However nowadays, with more data available and the competition for consumers increasing, companies dont just need data mining jobs they need people who will not only mine the data but also perform big data analysis and make sense of it from a business point of view so that companies can make important decisions based on their findings. The people who can perform both roles are data scientists and recently with the increase in big data analysis there has been a huge increase in demand for data scientist jobs.
What is the role of people with data scientist jobs and data mining jobs?
Data mining jobs
Individuals with data mining jobs in a data mining career need to make useful information from seemingly random data. They need to know about the technology they are working with and analyze huge amounts of data converting the raw numbers into useful information.
Data scientist jobs
A data scientist job takes this one step further and they then need to work out how to monetize this data. In the past the two processes in a data scientist job have been separated. The professionals with the data mining jobs, usually from an IT background, used to sort and analyse the data and then another business team would work out how to monetise this data. The role of individuals with data scientist jobs is therefore a hybrid of the two. Data scientist jobs involve the whole process from analysing the data to converting it into useful business information.
A person with a data scientist job is therefore someone who can obtain, explore and interpret data mixing statistics and machine learning jobs whilst appreciating that the final result is not only useful information but a first class monetizable product.
Profile of a data scientist job / data mining job
The sort of people that succeed in a data scientist job or data mining job will typically need technical, mathematical, and business skills. To have a successful data mining career you cannot simply produce reports but you must also be able to understand the questions a business needs to ask and use the data from your big data analysis to provide the answers. This requires programming knowledge as well as maths.
What essential skills are necessary for a data scientist job or data mining job to be effective?
Individuals with jobs in data mining obviously require very strong analytical skills as they will will need to obtain strong conclusions from their big data analysis. But individuals with data scientist jobs will also need to be creative and have excellent communication skills. Whilst a data mining job requires the skills to extract the information from the data, a data scientist job will involve hypothesis generation. Choosing the right problems to solve and then communicating their findings across the company through sales to finance and across every product group.
With the increase in big data and therefore big data analysis, data mining jobs and data scientist jobs will therefore be in increased demand as companies will need to make use of the huge amount of data they have to both improve and develop new products and stay ahead of the competition.
