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6 Must-Have Skills For Data Analysts

Industry Advice Analytics

The majority of companies today realize the value of a data-driven business strategy and are in need of talented individuals to provide insight into the constant stream of collected information. According to a report from IBM, openings for data-driven positions will rise to 2.7 million by 2020, and the demand for analysts will only grow as we continue to digitize our physical world.

If you’re just starting your research and are wondering how to make the transition to a career in data analytics, you’re not alone. Scanning job postings for data-driven positions is a great starting point, but many analyst roles are highly nuanced, making it difficult to discern which skills are most important. As Northeastern prepares students for roles in the field of data analytics, we know the blend that employers are looking for. Here are the top six must-have skills data analysts need to stay competitive in the job market.


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Must-Have Skills for Data Analysts

1. 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 organization needs someone who knows SQL—whether to manage and store data, relate multiple databases (like the ones Amazon uses to recommend products you may be interested in,) or build or change those database structures altogether. Each month, thousands of job postings requiring SQL skills are posted, and the median salary for someone with advanced SQL skills sits well over $80,000. If you’re looking to work with Big Data, learning SQL is the first step, although even non-techies could benefit from learning the tool.

2. 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 lookups 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 led by instructors.

Pro tip: Excel is limited with big datasets, so learning a statistical programming language is often another must as you move up in your career as an analyst.

3. Critical Thinking

Using data to find answers to your questions means figuring out what to ask in the first place; this can be quite tricky. In order to succeed as an analyst, you have to think like an analyst. It is the role of a data analyst to uncover and synthesize 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.

4. 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 analytics on big data sets. And they’re both industry standard. To truly work as a data analyst, you’ll need to go beyond SQL and master at least one of these languages.

So which one should you learn? Both R and Python are open source and free, and employers typically don’t care what their employees choose to use as long as their analyses are accurate. Since it was built specifically for analytics, some analysts prefer R over Python for exploring data sets and doing ad-hoc analysis.

Pro tip: Wondering about SPSS or SAS? Learning R or Python over these tools is considered best practice because, like Excel, SAS programs are limited.

5. Data Visualization

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’re going to have a difficult time getting through to others. For this reason, data visualization 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 visualization software is considered an industry-standard analytics tool, and it is refreshingly user-friendly.

6. Presentation Skills

Data visualization 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. Forbes also has some helpful tricks on how to get comfortable presenting.

BONUS: Machine Learning

Machine learning and predictive modeling are quickly becoming two of the hottest topics in the field of data science. While not every analyst works with machine learning, the tools and concepts are important to learn in order to get ahead in the field. You’ll need to have your statistical programming skills down first to advance in this area, however. An “out-of-the-box” tool like Orange can also help you start building machine learning models.

Knowing which skills you’ll need to break into analytics and start working with data is key to advancing your data analytics career. Industries are buzzing about Big Data, and organizations are looking for hires with these in-demand, short-in-supply skills. Improving your data analytics knowledge today means more opportunity—and more money—for you in the future.


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