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  • Writer's pictureDavid Avery

5 Technical Skills Every Successful Analyst Needs to Have

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I recently had the pleasure of attending the Center for Business Analytics Career Panel event at Villanova University where myself and other analytics professionals sat on a panel to discuss the future of analytics with the students.

My hope is that it helped inform them of possible career paths they can take with their future degrees and inspired (at least one of!) them to pursue a career in strategic analytics as I’ve done.

In mulling over the panel discussion and content, it got me thinking about my past and current experience and what I’ve found to be the core practical and technical skills I’ve needed to be successful in my career (besides my natural charm and charisma, of course). So, whether you’re currently a seasoned analytics professional like me, or are considering starting a career in data, here are the top 5 technical skills you’ll need to succeed:

  1. Knowing Excel (yes — we still use it). Don’t fight it; it’s universal. I’ve found through conversations with internal colleagues and external peers that it’s still widely used at every company and across all professions. True story: I even met someone in a creative design role who likes using it because her tables look better in it.

  2. Learning databases, because they also aren’t going anywhere. I’ve had experiences similar to this speaking panel where I’ve been asked what my job experience is like and nearly every single time, the question about whether or not I understand databases has come up. And every time, I recommend learning at least basic SQL because it’s the most common relational database language and will provide a good foundation. You don’t need to be a DBA, but recognizing that databases are here to stay and learning to interpret schemas, honing ETL techniques and practicing query languages will take you a long way.

  3. Becoming fluent in at least one language (read: programming). And if I had to pick a particular programming language, I’d recommend Python or R, since both are widely used for advanced analytics and predictive modeling. In general, though, if you go with another one, you can’t go wrong because language concepts such as for-loops, variables, debugging and nested “if” statements are common among all languages. Learning one will help you grasp others quickly.

  4. Familiarizing yourself with a BI or data viz tool. Of course, bias is leading me to say that Qlik is the best BI tool of choice, but there are other ones on the market you can consider. Overall, the point of learning a BI tool is that it makes you more well-rounded as an analyst and a more effective data storyteller. And the more versatile your skillset, the more likely you are to be pulled into challenging, compelling, business-transforming projects.

  5. Mastering logic. This sounds like something Aristotle might say, but getting a really good handle on IF-THEN-ELSE logic will help you grasp algorithms and complex structures more easily. In doing this, you’ll then be able to quickly pick up native solutions such as CRM and ERP systems and run complex reports.

The bottom line is, whether you’re an external consultant, the CEO or a business analyst, spreadsheets still can more easily do a lot of things that BI tools can’t. Embrace it. Master it. Love it.

Improving logical thinking also helps you more easily figure out business problems or foreign data sets that are dropped into your lap. In my current role, I’m always inheriting new datasets and investigating processes that I need to break it down into parts.Mastering logic will help you in any operations profession, but especially in analytics.

As a bonus, I recommended to the students that they master PowerPoint. Seems unrelated, but after you’ve assembled the world’s most amazing data and performed genius analysis to then form a game-changing recommendation, it can fall on deaf ears if it isn’t presented to stakeholders in the correct way. You’d be surprised what a well-ordered slideshow with simplified graphics and succinct bullet points will do when conveying a complicated data-driven narrative.

That’s my list — but maybe it doesn’t stop at just 5 skills. Do you have any other items a current or aspiring analyst needs in their toolbox? Let me know in the comments below!

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