Knowing how to gather, sort, and analyze data has become an essential part of almost any industry as advancing technology has rapidly expanded the types and amount of information we can collect. Data analysts can be found in a variety of industries, including criminal justice, fashion, food, technology, business, the environment, and government.
People who analyze data may hold additional titles such as:
- Analyst for medical and healthcare
- Analyst of market research
- Analyst for business
- Analyst for business intelligence
- Analyst for operations research
- Analyst of intelligence
What tools are used by Data Analysts?
Analysts frequently use a wide range of tools to improve the accuracy and efficiency of their work during the data analysis process. The following are some of the most commonly used tools in the data analytics industry:
- Excel by Microsoft
- Google Spreadsheets
- SQL
- Tableau
- R vs. Python
- SAS
- Microsoft PowerBI
- Jupyter Notes
How to Work as a Data Analyst?
There are several paths to becoming a data analyst. Whether you’re fresh out of school or looking to change careers, the first step is often assessing your transferable skills and developing the new skills you’ll need in this new role.
Technical Data Analyst abilities:
Database tools:
Microsoft Excel and SQL should be mainstays in any data analyst’s toolbox. While Excel is widely used across industries, SQL can handle larger data sets and is widely regarded as a must-have tool for data analysis.
Programming languages:
Learning a statistical programming language like Python or R will let you handle large sets of data and perform complex equations. Though Python and R are among the most common, it’s a good idea to look at several job descriptions of a position you’re interested in to determine which language will be most useful to your industry.
Data visualization:
Presenting your findings in a clear and compelling way is crucial to being a successful data analyst. Knowing how best to present information through charts and graphs will make sure colleagues, employers, and stakeholders will understand your work. Tableau, Jupyter Notebook, and Excel are among the many tools used to create visuals.
Statistics and Math:
Knowing the concepts behind what data tools are actually doing will help you tremendously in your work. Having a solid grasp of statistics and math will help you determine which tools are best to use to solve a particular problem, help you catch errors in your data, and have a better understanding of the results.
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