Written by Coursera • Updated on Aug 11, 2022 Show
A data analyst gathers, cleans, and studies data sets to help solve problems. Here's how you can start on a path to become one. A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. They work in many industries, including business, finance, criminal justice, science, medicine, and government. 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? These are the types of questions you might be pressed to answer as a data analyst. 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. What is data analysis?Data analysis is the process of gleaning insights from data to inform better business decisions. The process of analyzing data typically moves through five iterative phases:
Data analysis can take different forms, depending on the question you’re trying to answer. You can read more about the types of data analysis here. 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. Hear from experts in the field about what data analysis means to them. Data analyst tasks and responsibilitiesA data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too. Here’s what many data analysts do on a day-to-day basis:
Is data analysis for me?If you have good critical thinking skills and enjoy working with numbers to solve complex problems, then a career in data analysis can be a fit for you. Start building job-ready skills from industry leaders with the Google Data Analytics and IBM Data Analyst Professional Certificates on Coursera. What tools do data analysts use?During the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry include:
Data analyst salary and job outlookThe average base salary for a data analyst in the US is $69,517 in December 2021, according to Glassdoor. This can vary depending on your seniority, where in the US you’re located, and other factors. Data analysts are in high demand. The World Economic Forum listed it as number two in growing jobs in the US [1]. The Bureau of Labor Statistics also reports related occupations as having extremely high growth rates. From 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. That’s a lot higher than the total employment growth rate of 7.7 percent. Types of data analystsAs advancing technology has rapidly expanded the types and amount of information we can collect, knowing how to gather, sort, and analyze data has become a crucial part of almost any industry. You’ll find data analysts in the criminal justice, fashion, food, technology, business, environment, and public sectors—among many others. People who perform data analysis might have other titles such as:
Data analyst vs. data scientist: What’s the difference?Data analysts and data scientists both work with data, but what they do with it differs. Data analysts typically work with existing data to solve defined business problems. Data scientists build new algorithms and models to make predictions about the future. Learn more about the difference between data scientists and data analysts. How to become a data analystThere’s more than one path toward a career as a data analyst. 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. Data analyst technical skills
What is big data?The term “big data” refers to the vast amounts of structured and unstructured data that many businesses have access to on a daily basis. These data sets are typically too large to process using traditional data analysis methods. Big data is characterized by the three Vs: high volume, variety of data types, and the velocity at which the data is received.
If that seems like a lot, don’t worry—there are plenty of courses that will walk you through the basics of the hard skills you need as a data analyst. This IBM Data Analyst Professional Certificate course on Coursera can be a good place to start. Learn more: How Long Does it Take to Learn Python? (+ Tips for Learning) Data analyst workplace skills
Learn more: 7 In-Demand Data Analyst Skills to Get Hired Paths to becoming a data analystAcquiring these skills is the first step to becoming a data analyst. Here are a few routes you can take to get them that are flexible enough to fit in around your life.
For more on how to become a data analyst (with or without a degree), check out our step-by-step guide. Data analyst career advancementBeing a data analyst can also open doors to other careers. Many who start as data analysts go on to work as data scientists. Like analysts, data scientists use statistics, math, and computer science to analyze data. A scientist, however, might use advanced techniques to build models and other tools to provide insights into future trends. Get started todayIf you’re ready to start exploring a career as a data analyst, build job-ready skills in less than six months with the Google Data Analytics Professional Certificate on Coursera. Learn how to clean, organize, analyze, visualize, and present data from data professionals at Google. professional certificate Google Data AnalyticsThis is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 4.8 (89,816 ratings) 1,198,455 already enrolled BEGINNER level Average time: 6 month(s) Learn at your own pace Skills you'll build: Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study Learn more: 15 Data Analyst Interview Questions and Answers Frequently asked questions (FAQ)Related articles
Article sources1. World Economic Forum. "The Future of Jobs Report 2020, http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf." Accessed December 23, 2021. Written by Coursera • Updated on Aug 11, 2022 This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. What is the first step a data analyst should take to clean their data What is the first step a data analyst should take to clean their data?Step 1: Remove duplicate or irrelevant observations
Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Duplicate observations will happen most often during data collection.
What is the first step a data analytics should take to clean their data?Step 1: Remove duplicate or irrelevant observations
Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Duplicate observations will happen most often during data collection.
Does data analyst clean data?A data analyst spends as much as 90% of the time cleaning data (fixing structural errors, handling missing data, removing irrelevant observations, and filtering out unwanted outliers) because clean data is critical for gleaning valuable and accurate insights.
What process do data analysts use to keep project related files together?What process do data analysts use to keep project-related files together and organize them into subfolders? Data analysts use archiving to separate current from past work.
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