A data scientist turns raw data into valuable insights that an organization needs in order to grow and compete. They interpret and analyze data from multiple sources to come up with imaginative solutions to problems.
Organizations are increasingly using and collecting larger amounts of data during their everyday operations. Data scientists turn data into information using algorithms and machine learning. They take on projects to meet a particular customer or business need and present their results using clear and engaging language.
Types of Data scientists
You can work across a broad range of areas, including:
- scientific research
- information technology
Employers of Data scientists
- Universities and research institutions
- The government
- Large retailers etc
- Big data companies
- Marketing agencies and marketing departments at major companies
- Use strong business acumen, as well as an ability to communicate findings, and mine vast amounts of data for useful insights
- Use these insights to influence how an organization approaches business challenges
- Use a combined knowledge of computer science and applications, modelling, statistics, analytics and maths to solve problems
- Extract data from multiple sources
- Sift and analyze data from multiple angles, looking for trends that highlight problems or opportunities
- Communicate important information and insights to business and IT leaders
- Make recommendations to adapt existing business strategies
A degree will often be required, but it does not necessarily have to be in a computer or science based area. Having strong quantitative skills is a good base, but an interest in data and being able to solve problems logically and methodically are often bigger factors.
A degree in statistics, maths, business administration or computer science is a viable option to pursue a career as a data scientist. You will also be expected to know some programming languages such as R or Python and have strong database design and coding skills.
- exceptional communication skills, in order to explain your work to people who don’t understand the mechanics behind data analysis
- great attention to detail and the ability to problem solve
- experience in (or a willingness to get to grips with) database interrogation and analysis tools, such as Hadoop, SQL and SAS
- drive and the resilience to try new ideas, if the first one doesn’t work. You’ll be expected to work with minimal supervision, so it’s important that you’re able to motivate yourself
- good planning and organizational skills
- a collaborative approach to sharing ideas and finding solutions.