What are the Minimum Requirements to Become a Data Analyst?

This is a Tricky Career Path since the Job Title May Imply Many Things

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The title data analyst is difficult to describe because every company or industry will have its own definition about the role itself, therefore requirements listed in the job description will vary too. Please be aware that a data analyst might be similar to a business analyst, financial analyst, research analyst, or any other variant with the word ‘analyst’. The following requirements are not exclusively technical, there are some other qualities and attributes that are also taken in consideration by potential employers. What most data analyst positions have in common when it comes to minimum requirements are the following:

• Degree in a Quantitative-related Discipline (ideal). Commonly anyone with a Bachelor’s Degree in Business, Economics, Finance, Computer Science, Engineering, Math, Physics, etc. is eligible to apply for a data analytics role. If you feel that your degree is not related to a quantitative field, that’s okay! Nowadays, many employers want you to simply have any Bachelor’s. Individuals who lack of a college degree may apply as well. However, keep in mind that it is more challenging to get hired as a data analyst without a degree because employers will typically expect an increased amount of work experience in lieu of a Bachelor’s or Master’s.

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• Microsoft Excel. This is probably the daily bread and butter of every analyst out there. Excel’s main aspects that everyone should know before applying for this type of role are tabular tables, pivot tables, Vlookup, how to make graphs / charts, be able to distinguish the different data types available and importing text files. If you want to go the extra mile, you should learn basic Visual Basic for Applications (VBA) or how to record simple Macros. Learning the previous attributes should take around 3 weeks. In addition, being able to identify trends and patterns within spreadsheets is a non-technical analytical ability that goes hand by hand with Excel. You will essentially need to understand the meaning behind the data and ask yourself what is it that you are trying to accomplish.

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• Microsoft Word. Nowadays most jobs out there need you to write documentation, reports, memorandums, etc. The main characteristics about Word that you must know are how to insert / remove objects (such as links or images), format text and change text styles or font sizes. This tool takes less time to learn than Excel, but it is crucial for you to have efficient writing (and hence communication) abilities. When writing, make sure all of your ideas are clear and straight to the point.

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• Microsoft PowerPoint. In most cases, data analysts will need to present their data (or findings) to co-workers, members within your organization, clients, vendors, etc. It is important for you to visually summarize whatever dataset is that you are working on and possibly present it. It is a common practice for analysts to use templates and insert data by coping and pasting. However, when that’s not the case you will need to know how to create charts / graphs from scratch, insert text or images, and manually style every content in each slide. As mentioned on the previous bullet point, communication or ‘Story Telling’ abilities are important too. Public Speaking may also be included as a soft skill, you never know when you will have to speak in-front of a crowd.

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• Outlook. If you are looking for your first job and you have never had a professional email linked directly with your employer’s name before, they probably won’t expected to know this Microsoft tool in an entry-level position. The only prerequisite for Outlook is to know how to send an email with attachments and the rest (writing etiquette included) can easily be learned on the workplace. When using Outlook, be careful of mistakenly sending something confidential to the wrong person, by personal experience, I’ve seen this happen!

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• Structured Query Language (SQL). If you work with data, you will probably work with SQL (either MySQL or PostgreSQL). SQL is the language used for extracting information from a relational database. Extracted data may serve for Quality Assurance (QA) or testing purposes, or you may also be expected to create reports (spreadsheets) based on your query’s results. Please be warned that learning SQL’s syntax for the first time is hard. It may take you several weeks to get a good understanding of it by watching tutorials or taking cheap online courses. Anyhow, it is not as difficult as understanding the logic and meaning behind the information inside any database. For instance, if you work with information based on the results of computational fluid simulations of an airliner’s airfoil, will that be the same as dealing with investment-management or financial data? Certainly not! Please note that PostgreSQL has minor differences among MySQL and SQL Server. Therefore, it doesn’t matter which SQL variant you want to learn.

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• Python 3 and R. Not every employer will expect their analysts to know you to know any general-purpose programming language but it’s good to have at least one under your belt since it will possibly make you stand among the crowd! R and Python are relevant is due to their data analytics/science libraries (bundles of code someone else wrote). In addition, Python is a powerful tool that can automate some repetitive tasks (throughout algorithms). Some relevant Python libraries are NumPy, Pandas, SciPy and Matplotlib. Learning the essentials of Python and R will most likely take you longer to learn than SQL and Excel. You may find guidance from inexpensive online courses or YouTube.

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• Tableau Public. This is a data visualization tool. This is a free platform unlike the rest of the Tableau tools. Not every employer will need you to know Tableau Public but it’s always good to include it on your resume. Learning this tool shouldn’t take you much time, however it’s easier if you know Excel beforehand.

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• Interpersonal and Communication Skills. These are universal in most (if not, all) workplaces. Analysts commonly grouped by teams, therefore it is essential for you to work and communicate with others. The downside of soft skills in general is that it is very difficult to prove these to potential employers. So how do you solve this problem when looking for first full-time job? My suggestion if you are having difficulty getting interviews in the first place is to get a part-time job that would allow you to interact with people / customers! Even voluntary work might do the trick. You don’t have to be the most socially skilled person in your team, but if you are extremely shy, you should work on that, otherwise that barrier will prevent you from getting amazing opportunities.

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• Basic Data Management Knowledge. Every employer works with data and its processes differently, so simply have a basic understanding of where data comes from and how it serves businesses will make you a great candidate. You may find short and inexpensive courses on the internet and learning this shouldn’t take you more than a week.

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• Mathematics. In order to become an efficient analyst you need to have a basic understanding of statistics (percentages, rates of change, averages, mins, max), elementary operations and algebra. If you never took a Calculus class or got a ‘C’ on your Statistics class, don’t be worried! Most reports and datasets will most likely be based on simple math instead of complex integrals or first-order separable differential equations.

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• Data-Entry skills. In most cases, datasets will not have ‘nice’ data that’s consistent. In many occasions data will have duplicated values, contain wrong formatting, you name it! Analysts have to manually manipulate the dataset in order to fix it. Data-Entry tasks are synonymous with ‘data cleansing’, ‘data review’, ‘data collection’, etc. So how can you demonstrate this skill? I recommend doing a data analysis project (if applicable) where you manually collect data from different sources and present your findings! Read the next bullet point to figure out the best way to display any project.

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• Basic HTML, CSS and Github (optional). You might be thinking, aren’t these web development tools? They are, however, if you want to go above and beyond as a candidate I would strongly suggest you design and deploy a simple static website and showcase projects or experiences. In addition, by doing this you can show that you are a quick-learner. By ‘static’ I’m referring to a simple page that has no complex object-oriented programming involved or it’s connected to some database. HTML provides the page’s structure, CSS takes care of the styling, and Github is the free platform where you deploy the your page into the Internet! The only prerequisite for Github is to know basic Git (version control) commands through your computer’s command line. All of these tools sound intimidating but there are plenty of tutorials on YouTube that will show you step by step on how to do this, however this entire process may take you in fact several weeks depending on your learning abilities.

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• Miscellaneous. As mentioned on this post’s subtitle here comes the tricky aspect… You will notice right away that several data analyst job descriptions includes specialized knowledge in things like Ad hoc reporting, KPIs, SEO, Advanced Data Science tools, Customer Service, sales software, specific datasets corresponding to a few industries, etc. Do yourself a favor and don’t attempt to learn EVERY requirement. Pick a few that you feel are the most relevant to you and stick with those.

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Conclusion:

After reading this blog post you might think that learning Microsoft Office, SQL, Python and developing the rest of the minimum requirement sound like a lot of work. Remember that each job description for analyst roles are different, some have less requirements than others since every company has its own definition of what a data analyst is. My goal is not to overwhelm you but to let you know the best way to prepare yourself before applying for a data analyst role.