A data analyst is someone who scrutinises information using data analysis tools. The meaningful results they squeeze from the raw data help their employers or clients make important decisions by identifying various facts and trends. Typical duties include: using advanced computerised models to extract the data needed. The data analyst serves as a gatekeeper for an organisation’s data so stakeholders can understand data and use it to make strategic business decisions. It is a technical role that requires an undergraduate degree or master’s degree in analytics, computer modelling, science, or maths.
Skilled data analysts are some of the most sought-after professionals in the world. Because the demand is so strong and the supply of people who can truly do this job well is so limited, data analysts command huge salaries and excellent perks, even at the entry level. Data analyst jobs can be found throughout a diverse mix of companies and industries. Any company that uses data needs data analysts to analyse it. Some of the top jobs in data analysis involve using data to make investment decisions , target customers, assess risks, or decide on capital allocations.
Essential Skills For Data Analysts
- SQL: SQL, or Structured Query Language, is the ubiquitous industry-standard database language and is possibly the most important skill for data analysts to know. The language is often thought of as the “graduated” version of Excel, it is able to handle large datasets that Excel simply can’t. Almost every organisation needs someone who knows SQL whether to manage and store data, relate multiple databases or build or change those database structures altogether. Each month, thousands of job postings requiring SQL skills are posted, While even non-techies can benefit from learning this tool, if you are looking to work with Big Data, learning SQL is the first step.
- Microsoft Excel: When you think of Excel, the first thing that comes to mind is likely a spreadsheet, but there’s a lot more analysis power under the hood of this tool. While a programming language like R or Python is better suited to handle a large data set, advanced Excel methods like writing Macros and using VBA (visual basics for application) lookup are still widely used for smaller lifts and lighter, quick analytics. If you are working at a lean company or startup, the first version of your database may even be in Excel. Over the years, the tool has remained a mainstay for businesses in every industry, so learning it is a must. Luckily, there is an abundance of great free resources online to help you get started, as well as structured data analytics classes for those looking for a deeper understanding of the tool.
- Critical Thinking: Using data to find answers to your questions means figuring out what to ask in the first place, which can often be quite tricky. To succeed as an analyst, you have to think like an analyst. It is the role of a data analyst to uncover and synthesise connections that are not always so clear. While this ability is innate to a certain extent, there are a number of tips you can try to help improve your critical thinking skills. For example, asking yourself basic questions about the issue at hand can help you stay grounded when searching for a solution, rather than getting carried away with an explanation that is more complex than it needs to be. Additionally, it is important that you remember to think for yourself instead of relying on what already exists.
- R or Python–Statistical Programming: Anything Excel can do, R or Python can do better and 10 times faster. Like SQL, R and Python can handle what Excel can’t. They are powerful statistical programming languages used to perform advanced analyses and predictive analysis on big data sets. And they are both industry standard. To truly work as a data analyst, you will need to go beyond SQL and master at least one of these languages. Both R and Python are open source and free and employers typically don’t care which their employees choose to use as long as their analyses are accurate. Since it was built specifically for analytics, however, some analysts prefer R over Python for exploring data sets and doing ad-hoc analysis.
- Data Visualisation: Being able to tell a compelling story with data is crucial to getting your point across and keeping your audience engaged. If your findings can’t be easily and quickly identified, then you are going to have a difficult time getting through to others. For this reason, data visualisation can have a make-or-break effect when it comes to the impact of your data. Analysts use eye-catching, high-quality charts and graphs to present their findings in a clear and concise way. Tableau’s visualisation software is considered an industry-standard analytics tool, as it is refreshingly user-friendly.
- Presentation Skills: Data visualisation and presentation skills go hand-in-hand. But presenting doesn’t always come naturally to everyone and that’s okay! Even seasoned presenters will feel their nerves get the best of them at times. As with anything else, start with practice and then practice some more until you get into your groove.
- Machine Learning: As artificial intelligence and predictive analytics are two of the hottest topics in the field of data science, an understanding of machine learning has been identified as a key component of an analyst’s toolkit. While not every analyst works with machine learning, the tools and concepts are important to know in order to get ahead in the field. You will need to have your statistical programming skills down first to advance in this area, however.
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