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5 Tips to Start a Career in Data Analytics

Updated: Jun 16

Data Analysts prepare, structure, and visualize data through interactive dashboards and graphs to help stakeholders identify patterns, monitor trends, and make insightful decisions. Many people begin to discover the field of Data Analysis at later stages in their careers and struggle to figure out where to start.



We understand that taking the first steps into the world of Data Analysis can be confusing, and it surely does not help that most universities do not offer courses in the field. To help you set a roadmap, we have compiled 5 Tips to Start your Career in Data Analysis.


#1 - Start with Microsoft Excel


We know what you are thinking- there is an array of solutions that are more focused on Data Analytics than Excel. However, Excel is an excellent stepping stone for you to get familiar with the logical schema and structure that are used in more advanced tools.


For example, the functions used in Excel are very similar to DAX, which is the language used in Microsoft Power BI, one of the leading visualization tools in the world. Also, the tabular design that Excel follows to store data will give you an idea of how other relational databases like SQL are structured.


Moreover, Excel's pivot tables, charts, and graphs are powerful tools for analysts to summarize and highlight specific information from large amounts of data, and it will help you understand which charts are most suitable for which types of data.


If all those reasons are not enough, remember that almost all major organizations rely on Excel to store and keep track of their data, so your experience with manipulating data in Excel will always be in demand.


#2 - Gain Proficiency in SQL


SQL is a database language through which you can access, interact with, and manipulate your data. As a data analyst, you will need it to clean and structure your data before you start visualizing it.


Acquiring SQL will be a huge plus to your skillset because most companies that work with large amounts of data use it. The best part is that SQL is easy to learn because it has a simple and logical language structure, which makes it a perfect way for you to start learning programming languages as well!


As you advance into Data Science or Data Engineering, you will be executing more advanced queries with SQL. Think of SQL as the portal to everything Big Data.

Although a degree in Computer Science or Engineering helps, you do not need it to become a Data Analyst. In fact, one of our top analysts at Paragon Shift is a Philosophy major!

#3 - Master a Data Visualization Tool (Or Two!)


This is the fun part. There is a wide range of visualization tools and solutions that can help you transform your data into an exciting story. A few of the most in-demand ones are Microsoft Power BI, Qlik Sense, and Tableau.


Paragon Shift is a Qlik and Microsoft Partner, so our team members have expertise in both. However, other organizations may rely on Tableau or any other solution. Our advice to you is to start with Microsoft Power BI because it offers a free edition, and then experiment from there. The good news is that learning any of these solutions will equip you with transferable skills that can be used with others.


#4 - Get Certified


It might seem dull, but earning certifications not only helps you stay on track of your progress and evaluate your proficiency, but it also gives you the credibility you need to prove that you know your stuff. Even if you are excellent at deriving insights from data, if you don't whip out a laptop on the spot, it would be difficult to show it.


Most of the leading solution providers have academies and enablement programs that include courses and learning material to get you prepped for your next certification. At Paragon Shift, we encourage our Data Analysts to receive certifications in Power BI and Qlik Sense. However, you can follow the learning path of your preferred tool and modify as you go!


#5 - Learn Python


Although you can begin a career in Data Analysis without knowing a coding language, developing your coding skills will help you advance into more complex work with Big Data like Data Science and Data Engineering. We recommend you start by learning Python because it is relatively easy to learn and it provides a host of built-in analytics tools that help analysts manipulate, process, model, and structure data.


Remember, the World of Data is Endless!


Your journey to become a Data Analyst will be exciting and rewarding, and from Data Analysis, you can branch out into other Big Data fields such as Data Science, Data Engineering, and Business Intelligence Consulting. In addition to technical skills, there are soft skills that you will need to acquire to become a successful Data Analyst. Stay posted on our website to read the upcoming blog post: 4 Soft Skills that Successful Data Analysts Need.

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