Data Analyst Job Description

data analyst job descriptionMany enterprises and corporations are focusing on big data strategies for customer projections, marketing, research, and sales. The amount of data being created on a daily basis is staggering, with IBM reporting that 90 percent of the current data available throughout the world has actually been generated in the past few years. That’s a lot of raw data flying around, but it’s useless without someone skilled in figuring out how to use that data. That’s where data analysts and data scientists come in.

Learn the ins and outs of technical analysis.

What Data Analysts Do

At its most basic level, a data analyst collects, evaluates, and interprets data from various sources, such as research studies or a SQL database. The actual job is significantly more complex than looking at numbers and data all day, however. A data analyst has to make sense of the information. They draw logical conclusions from the data, provide valuable insight to a marketing or research team depending on their findings, and compile the data into a form that is understandable to non-data analysts. The analyst works with specialized applications to pull data from databases and other sources, create reports, and use the software to identify patterns and trends in the information. Data analysts work in the office, on-site, or at home, depending on the needs of the company. Generally you’re working in an office, however, interacting with each department that needs your services to help them meet their goals and deadlines.

Data analysts use different analytic models to work with the data. For example, regression analysis takes specific variables and estimates the relationships between them. This model is generally used in future forecasting and prediction, especially when casual relationships are suspected between dependent and independent variables. These data analyzing skills prove useful for increasing business, finding out problems in research, or determining what demographic to market to.

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Data modeling software is the staple tool in a data analyst’s pocket. The exact modeling tools used by the analyst varies depending on the data input. MicroOLAP Database Designer is used to sort through mySQL and PostgreSQL data, for firms that need a focused application. Other applications support a broad range of technology, such as CA Erwin Data Modeler. It supports Access, Teradata, Sybase, Progress, Oracle, MySQL, Ingres, Informix, and IBM DS2 databases.

A new discipline, called data scientists, focuses on a strong business background for data analyst. This discipline primarily grew out of corporations tackling big data strategies, as they desired analysts who can communicate their findings with upper management as readily as they could with the IT department. Another characteristic of a data scientist is bringing together data from several channels for comparative analysis, instead of looking through single sourced data.

How Much Do Data Analysts Make?

The median salary for a data analyst is $70,000 for salary, or $35 per hour for wage employees and contractors. The salary range varies depending on the industry the analyst works in, their familiarity with analytic software, and the job location. Most industries have a need for data analysts, but the most common hiring industries are technical firms, financial institutions, government firms, and marketing agencies.

What Education and Skills Are Required?

Above all else, data analysts must be comfortable with complex technology, experts at communicating high level tech down to laymen’s terms, be proficient at identifying data trends and patterns, and have strong math and logic skills, especially in statistics. Most data analysts major in computer science, actuarial math, statistics, general math, or a related hard science and math field. Entry level positions are available with a bachelor’s degree, although a masters degree is commonplace for mid to upper level management and supervisory positions. An analyst going beyond a masters tends to stay in academic research.