![]() Programming languages: Learning a statistical programming language like Python or R will let you handle large sets of data and perform complex equations. While Excel is ubiquitous across industries, SQL can handle larger sets of data and is widely regarded as a necessity for data analysis. Data analyst technical skillsĭatabase tools: Microsoft Excel and SQL should be mainstays in any data analyst’s toolbox. Whether you’re just graduating from school or looking to switch careers, the first step is often assessing what transferable skills you have and building the new skills you’ll need in this new role. There’s more than one path toward a career as a data analyst. That’s a lot higher than the total employment growth rate of 7.7 percent. The Bureau of Labor Statistics also reports related occupations as having extremely high growth rates.įrom 2020 to 2030, operations research analyst positions are expected to grow by 25 percent, market research analysts by 22 percent, and mathematicians and statisticians by 33 percent. The World Economic Forum listed it as number two in growing jobs in the US. This can vary depending on your seniority, where in the US you’re located, and other factors.ĭata analysts are in high demand. The average base salary for a data analyst in the US is $69,517 in December 2021, according to Glassdoor. Some of the most common tools in the data analytics industry include: You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.ĭuring the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Present: Communicating the results of your findings will be a key part of your job. Interpret data: Interpreting data will involve finding patterns or trends in data that could answer the question at hand. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears. Model data: This entails creating and designing the structures of a database. ![]() Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed. This could include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.Ĭlean data: Raw data might contain duplicates, errors, or outliers. Gather data: Analysts often collect data themselves. ![]() Here’s what many data analysts do on a day-to-day basis: The role includes plenty of time spent with data but entails communicating findings too. Briefly, descriptive analysis tells us what happened, diagnostic analysis tells us why it happened, predictive analytics forms projections about the future, and prescriptive analysis creates actionable advice on what actions to take.Ī data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. You can read more about the types of data analysis here. ![]() The process of analyzing data typically moves through five iterative phases:Ĭlean the data in preparation for analysisĭata analysis can take different forms, depending on the question you’re trying to answer. What is data analysis?ĭata analysis is the process of gleaning insights from data to inform better business decisions. Read on to find out more about what a data analyst is, what skills you'll need, and how you can start on a path to become one. These are the types of questions you might be pressed to answer as a data analyst. What kind of customers should a business target in its next ad campaign? What age group is most vulnerable to a particular disease? What patterns in behavior are connected to financial fraud? They work in many industries, including business, finance, criminal justice, science, medicine, and government. A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. ![]()
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