As a data analyst, you’ll take raw data and extract useful information and insights from it. Data analysts figure out how data can answer questions and solve problems. An analyst considers outliers, calculates averages and refines data to create breakdowns. Data analysis is an important aspect to many private and public industries such as business, finance, medicine, non-profit organizations, and government.
For example, let's say you work for your state's department of education, and you need to decide whether to build a new high school in a certain district. As a data analyst you can help factor in information such as population growth in that district over time, and the cost of building a new high school compared to how likely it is to improve student outcomes. This is all important information that needs to be considered before breaking ground on a new building, giving the data analyst an essential role in the decision-making process.
In the private sector, understanding customer behavior and preferences can be vital for companies to succeed. For instance, a department store needs to know what clothes to buy for its summer line, or it may want data on its customers' style preferences, or how those preferences have changed over time. A data analyst can use data such as search histories, survey results and SEO data to help their company make well-informed financial decisions.
How much do data analysts make?
If you enjoy working with numbers and have sharp critical thinking skills, a career in data analytics could be a good fit for you. While the U.S. Bureau of Labor Statistics doesn’t have data specifically for data analysts, the similar role of operations research analyst earns an average of $82,360 per year and the field is expected to grow at a faster-than-average rate of 23% from 2021 to 2031.
Data analyst vs. data scientist: What are the differences?
While data analysts and data scientists both work with data, the main difference lies in what they do with it.
Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Whereas data scientists design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analyses.
The four types of data analytics
The kinds of insights that can be derived from your data depends on the type of analysis you carry out. In data analytics and data science, there are four main types of data analytics: Descriptive, diagnostic, predictive, and prescriptive.
- Descriptive data analytics
Descriptive data analytics are the most common type of data analytics. It answers the question “what happened?” This involves looking at historical and current data. The helpful visualizations used for descriptive analytics include pie charts, bar charts, tables, or line graphs.
- Diagnostic data analytics
Diagnostic data analytics dig deeper into the data to answer the question “why did it happen?” Techniques such as correlations, data discovery, data mining, and drill-down are used to communicate diagnostic data.
- Predictive data analytics
Predictive data analytics uses data and information to answer “what is likely to happen?” It involves using high quality data and techniques such as multivariate statistics, pattern matching, predictive modeling, and regression analysis to create predictive forecasting and models.
- Prescriptive data analytics
Prescriptive data analytics analyzes data to ask “what should be done?” This type of analytics is the most challenging and involves techniques such as complex event processing, graph analysis, heuristics, machine learning, neural networks, recommendation engines, and simulation to formulate prescriptive recommendations for your organization.
What skills does a data analyst need?
To succeed as a data analyst, you need a strong math background, particularly in statistics. While you likely won't need to know any computer programming languages, familiarity with computerized databases and how to operate them is crucial. Moreover, you'll need to be highly organized and able to meet deadlines, since many companies and organizations need data analysis conducted by certain times to proceed with their projects.
To get a job as a data analyst, you'll generally need a bachelor's degree. This is typically a requirement for entry level and higher data analysis jobs. What you study as an undergraduate is not as crucial. Professional data analysts major in fields such as computer science, mathematics, economics, and even psychology. All these fields teach you how to work with large sets of data and mine them for relevant and useful information.
There are technical data analyst skills that can boost your resume even further. Many employers are particularly interested in candidates with an understanding of machine learning, experience in data cleaning, data visualization or a coding background in Python.
Earn your online master’s in data analytics with Arizona State University
For undergraduates considering data analysis as a career, or for professionals looking to change careers ASU Online’s Master of Science in program evaluation and data analytics can help prepare you for your career. Organizations often look for candidates with a master's degree to fill higher-level data analyst positions, and this degree can assure them that you have the skills needed for the role.
Moreover, a master's degree focused on data analyst skills can offer you an academic background as well as hands-on experience working with data and completing projects. If you're interested in a career as a high-level data analyst, pursuing a master's degree online might be the right next step for you.