Get – Set – Go! Give Your Life and Career a Boost with a Business Analyst Certification in SAS/SPSS

A business analyst certification in data analytics paves the way for a career as a data scientist, statistical analyst, etc. The onset of BigData has created job titles such as the aforementioned, which are not exclusive to BigData projects. Mastering in statistical analysis tools such as SAS and SPSS, etc. provides numerous job opportunities in a burgeoning job market.

What does a data scientist do?

A data scientist can be viewed as an evolution from a business or data analyst job function. This professional has a foundation in computer science, modeling, statistics, math and analytics. Apart from these skills, the data scientist has business acumen, communication skills, innovation, and problem solving ability. To be a good data scientist, it is important to be inquisitive, look at data and derive insights, spot trends and so on. Essentially, all effort is directed at bringing about organizational change.

Unlike a data analyst who analyzes data from a limited set of sources, a data scientists sources data from multiple disparate sources. This professional sifts through this data with the goal of spotting information, insights, trends, solutions etc. Therefore, the data scientist’s role is not just collecting and reporting information, but interpreting it, forming intelligent analysis, and recommending future course of action.

Who can become a data scientist?

Anyone with an analytical bent of mind can become a data scientist after a SAS or SPSS training. The job requires a mix of technical, functional, and business skills. The ability to communicate to stakeholders about findings is important too. It is a dynamic and highly challenging job with limitless rewards. As organizations look to innovate and bring newer products into the market, the data scientist presents new ideas, and estimates their feasibility.

Categories of data scientists

The following are the categories of data scientists based on the core specialty required of them.

• Statistics – The job is to develop statistical methods, be proficient in statistical modeling, sampling, data reduction, experimental design, predictive modeling etc.
• Mathematics – Involves collection, analysis and extraction of the value out of data. The role extends to operations research, forecasting, pricing optimization, quality control, yield optimization, and business optimization.
• Data engineering – Encompasses expertise in Hadoop, APIs, data plumbing, database, memory, file systems, Analytics as a Service.