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A Data Analyst is responsible for collecting, processing, and analyzing data. They usually translate these numbers into actionable insights that help their employer make better business decisions. Data Analysts can work in many industries including retail, tech, medicine, and government agencies. With such broad opportunities, the day-to-day life of each Data Analyst varies. Data Analysts’ daily work involves gathering data, organizing it, inferring useful conclusions from that data, and then communicating it to stakeholders or clients.
Most Data Analysts work on a team to make data understandable through reports, pattern recognition, team collaboration, and data infrastructure set up. Data analysts might specialize in analyzing data within the realms of business, research, or finance.
Data Analysts should have strong analytical and communication skills. They should be proficient in Microsoft Excel, Access, and Sharepoint as well as SQL. Some Data Analyst positions may require programming with R or Python and the use of some mathematics. Data Analysts will need to be team players, good at pattern recognition, detail-oriented, and passionate problem-solvers.
It is essential for Data Analysts to dial in their soft skills. These skills will depend on whether their employer is a large organization where they will have to communicate their findings to upper-level management and C-level positions, in the medical field working with doctors and writers to create reports, or working for startups where they will communicate directly to a team of founders. Some Data Analysts might only be communicating directly with a small group of clients. These communications will essentially be educating the person(s) receiving the analyzed data.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is used to write scripts, automations, algorithms, manipulate data, and create frameworks. Python prioritizes simplicity, easy to learn syntax, readability, and versatility.
SQL stands for Structured Query Language. It is a computer language used to store, manipulate, and retrieve data which is stored in a relational database.
Excel is a spreadsheet developed by Microsoft that runs on Windows, macOS, Android, and iOS. It is used for calculation, graphing, data visualization, and Visual Basic for Applications (VBA) macro programming.
Data analytics uses analysis techniques to infer conclusions about raw data. Algorithms and machine learning have optimized data analysis over time to find trends and answer questions more efficiently.
Tableau is a data visualization tool. This tool can be used to simplify raw data, reformat data, and perform efficient data analysis. Tableau can also be used to create data visualizations, dashboards, presentations, and worksheets.
Power BI is a Microsoft business analytics services. It provides interactive visualizations, business intelligence, and simple interfacing for report and dashboard creation.
A Data Analyst in the United States makes, on average, $69,154 annually, according to Indeed.com.
Salaries for Data Analysts vary by region within the the United States. Listed below are some Data Analyst salaries for specific areas with the United States compared with the average national salary:
You do not need a higher education degree to become a Data Analyst but some Data Analysts have a bachelor’s degree in mathematics or data science. Some Data Analysts get their start in academia during a graduate or Ph.D. program. Regardless of education, it is more important for Data Analysts to demonstrate skill proficiency and thought processes than to have a degree.
Data Analysts can work in a wide variety of environments including corporate companies, retail conglomerates, medicine, academia, start-ups, or for a government entity. They can find jobs in nearly any industry making predictions, solving problems, and presenting data in a consumable way.
You can find Data Analyst jobs on these sites:
Along with a polished résumé, a Data Analyst should have a well-rounded portfolio that showcases their thought processes and technical knowledge. A good portfolio might include case studies that use real-world data manipulated into reports inferences alongside the thought processes that lead you there. Posting these portfolio examples on a cleanly designed blog is common practice so that potential employers can view your work.
Data Analysts should also be utilizing LinkedIn to its fullest potential. Your LinkedIn profile should be up-to-date, include all past work experience , and include keywords relating to Data Analysts' skills and responsibilities. It should also show and tell how your past experiences offer transferable value to your position as a Data Analyst.
To get a leg up, try to connect with a point person with a letter of introduction at each company you send an application to, whether you applied via LinkedIn or not. This will provide you name recognition and sometimes first-hand advice. You should also make these connections with people who might be your manager at any company you would like to work for regardless of whether they have posted job openings.
Data Analysts can apply for a variety of positions that may be narrowed down based on industry, location, company size, and interest in specializing. Data Analysts will likely start out in junior or entry-level positions but will find that rising the ranks to a Senior Data Analyst is possible relatively quickly and will result in a better annual salary. Here are a few options you might be qualified for as a Data Analyst:
Many people confuse Data Scientists and Data Analyst. They both work with data and the biggest difference between the two is what they do with that data. Data Analysts identify trends and create visualizations with those patterns. Data Scientists also interpret data but also have coding and mathematical expertise. They can do the work of a Data Analyst but usually work on algorithms, predictive models, and creating processes for data. A Data Analyst can upskill to a Data Scientist position by learning more about the mathematical and algorithmic necessities of that position. Data Science positions pay more than Data Analyst positions but often require a four-year degree in mathematics or computer science.
Data Analysts might find related careers like Business Analyst, Business Intelligence Analyst, Financial Analyst, Data Scientist, or Product Analyst. Most of these positions pay similar or slightly higher salaries to a Data Analyst and have similar day-to-day operations but require additional technologies and industry knowledge. Some ways to upskill into these positions would be to learn financial theories, business theories, the product lifestyle, and design thinking.
If you’re a Data Analyst, you might find that pivoting toward business or financial analysis enticing. This would include more communication with upper-level management and c-level positions, making more predictions, using math as much as technology, and understanding business principles. These positions might provide more satisfaction as the results of your work can be seen relatively quickly.
Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine data sets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making.Learn about becoming a Data Scientist
Business analysts use business, technology, and project management skills to analyze business problems and propose data-driven solutions. Grounded in technical expertise, business analysts perform risk analyses, manage project plans, and translate technical information such as diagrams and blueprints. Experienced business analysts can become business or project managers, which puts their professional expertise to work with the management of project deliverables and other people. Business analysts can put their skills to work across a variety of industries, companies, and job functions.Learn about becoming a Business Analyst
Digital analysts work with a marketing team to analyze the effectiveness and reach of digital marketing campaigns. They use Google analytics and site tagging tools to harvest user data. This data is analyzed and interpreted to provide insights into how to improve the user experience and the effectiveness of the digital marketing campaign.Learn about becoming a Digital Analyst