The 8 Must-Have Data Analyst Skills

Many organizations today depend heavily on data regarding their customers, clients, products, operations, and market. There is now an unprecedented shift in how data is accelerating business transformation, and with that we see more and more organizations requiring talented and qualified professionals who can extract information, analytics, and insights from complex data.

But what skills are organizations looking for? In the field of data analytics, there are a number of skills and qualities that set great analysts apart from the competition. Below we have outlined the 8 must-have data analyst skills and habits that can help you become an in-demand and proficient data analyst.

1) Versatility

The best data professionals will have a solid foundation of the core technical data analyst skills needed to succeed in the field. For example, it is important to develop a strong foothold in today’s most prolific data languages such as SQL, NoSQL, Postgres/pgAdmin and MongoDB. It is also helpful to learn advanced topics like statistical modeling, forecasting and prediction, pivot tables, and VBA scripting. 

Learning today’s critical programming languages help a data professional stand out among the competition. A new developer should commit to achieving deep proficiency with core Python and data analytics tools like NumPy, Pandas and Matplotlib. You might also consider exploring specific libraries for interacting with web data such as Requests and BeautifulSoup.

Finally, it is important to learn the inner workings of web visualization. Building visualizations is of little benefit without an effective way to communicate the message. Consider exploring the core technologies of front-end web visualization such as HTML, CSS, JavaScript, Bootstrap, Dashboarding and Geomapping with JavaScript libraries. Learning to use these tools will help any developer create new, interactive data visualizations that can be shared with everyone on the web.

If you would like to learn each of these platforms and more, take a look at our SMU Data Science Boot Camp.

2) Thinking ‘Simple’ Over ‘Complicated’ When it Comes to Algorithms

As a data analyst or scientist, you’ll have to get used to the idea that not everyone in the office is going to know what you’re talking about. A good developer should be able to simply and easily explain even the most complicated ideas in a way that an entire team can understand. Many times even a project manager won’t be able to decipher complex data jargon, so be sure to keep this in mind for all future projects. There’s little to no value in an analysis that doesn’t make enough sense to put a plan into action.

3) Results-Driven

Many times you’ll get a better idea of the consistency of a data set by finding additional sources, versus finding additional data from the same source. If you spend a good portion of time limiting yourself to only one source of data, you might not be seeing the whole picture. Tap into resources to pull more data and you’re likely to gain greater insight into the situation.

4) Reputation Management

Creating a name for yourself allows you to attract new clients while establishing a trusted reputation. As you analyze complex data for different clients, they may offer helpful referrals and positive word-of-mouth within their respective industries. With that reputation will come recognition. When a client is happy with the work, they may request more. Over time, strong relationships may form with these clients, which in exchange turns them into long-term customers.

5) Continuous Learning

Sometimes the best decision is to stick to what is tried and true. A good data analyst will value tools that are reliable and trustworthy. This is not to say that you should choose a tool and stick with it forever; it’s important to choose quality tools and take the time to learn them properly. Even when forced to jump right into an important project using a new software, take the time to learn the inner workings of that tool in order to avoid any critical mistakes from the beginning.

6) Collaborative and Network-Driven

The best data analysts know the importance of soft skills and making meaningful connections with others in the field. Employers and businesses not only hire based on skill set, but they also look for data professionals who are collaborative and great at working across dynamic teams. Each time you connect with someone new or complete a successful data assignment for a client, potential opportunities for future collaboration may arise.

Strong collaboration requires ongoing communications between you and other key stakeholders. As a data professional, you may notice goals shifting halfway through a project, and should that happen, the necessary changes must be communicated to everyone involved. Events like this may require a stop from gathering one form of data and beginning with another. 

7) Adaptability

As a data professional, it is important to identify the skills gap you have based on future goals. For example, the skills needed for data professionals in marketing is going to be somewhat different from that of a data scientist in the medical field. A critical component of long-term success as a data professional is the ability to adapt your skills and knowledge to evolving business needs.

8) Digital Dexterity

Digital dexterity involves the capacity and desire to use existing and emerging technologies to drive better business outcomes. Your digital dexterity is a strong indicator of agility and individual ability to prosper in today’s digital workforce. As the digital aspect of many roles is growing, we see many moving into more hybrid technology positions that require some level of data analytics, coding, application integration and other data analyst skills to take advantage of new technology and business processes.

If you would like to learn more about improving your digital dexterity, we put together a collection of must read books in our previous blog post. Our list of 7 Data Analytics Books You Should Read in 2019 covers Machine Learning, Big Data, Artificial Intelligence, Data Science, Python, Business Intelligence, Deep Learning, Forecasting and much more. 

Conclusion

Becoming a data analytics professional is now more accessible than ever before, allowing newcomers to learn at their own pace, or with the help of a data analytics boot camp. It is critical for potential professionals to learn the most relevant and current data analyst skills, techniques, and habits. Data analytics courses via a boot camp are a great way to learn the most up-to-date material and practices.

Ready to become a data professional? Explore SMU Data Science Boot Camp designed to equip you with the skills to succeed in this high-demand industry.