How to become a Data Analyst in 2022.
The skills needed (both hard and soft) to assist in your journey to land one of the most trending jobs today.
As big data becomes more prevalent, the new(ish) fields of Data Analytics and Data Science are growing faster than ever. This is the very reason many of these positions often have overlapping or vastly different job requirements in the descriptions per company, per industry, per location. That being said, listed below are universal skills that every analyst should strive to have and continue to improve on to be successful.
Hard Skills
1. SQL
SQL, or structured query language, is what I consider the bread and butter of any analyst’s toolset. This programming language allows the analyst to “talk” to the database. Becoming a SQL guru would place a potential candidate miles ahead of the competition when it comes to applying for a data analyst position. SQL is used to manipulate, wrangle, aggregate, consolidate, and organize data for easier consumption. Just like with any language, if you’re “fluent” enough in SQL, you can guarantee success as an analyst.
However, this also comes with the caveat that one would need to familiarize themselves with the common relational database management systems (RDBM) that companies utilize. This means getting used to the ways that SQL can work within these databases to create, update, extract, and house the data. For the most part, utilizing SQL doesn’t differ much across the various databases, so being familiar with one or two can easily carry over to others.
2. Excel
Excel is one of the most common tools utilized in data analysis. One can argue that this Microsoft tool is easily replaceable with more appropriate methods of utilizing an RDBM with SQL or that of Python and its libraries. However, companies will always continue to utilize Excel in one way or another. Hate it or not, stakeholders and end-users, especially those with more traditional mindsets, will prefer Excel reports in some cases.
The foundational skills related to Excel that I will highlight below are some of the most necessary functions and concepts for data analysts:
- VLOOKUP / INDEX, MATCH
- Pivot Tables
- Graphs / Charts
- Calculated Fields
- Sorting / Filtering
- VBA (Not required but nice to have)
3. Tableau / Power BI
Whether you decide to go towards the Tableau or Power BI paths, data visualization is becoming the norm in most data analyst positions. These powerful tools allow you to transform the data that you’ve taken 70% of your time to manipulate, de-duplicate, and clean, into easily digestible visuals for your end users to view and easily consume.
Dashboards and stories built in Tableau or Power BI are easy-to-use (granted you create and present them appropriately) and can easily be the piece de resistance to solving your customers’ problems.
4. Python (not required for junior level positions but nice to have)
Python is one of the most powerful (and popular) programming languages to learn today. This is due to not only the easier learning curve for foundational concepts — compared to other languages — and understanding of the language itself, but also the versatility in use-cases for this tool. Using Python for data analysis is only one small facet of the vast potential of this amazing and adaptable language.
Being able to learn and be comfortable with utilizing the common libraries/packages that this beautiful language has to offer can improve your hire-ability and overall value as an analyst. However, for most junior level positions, this language, though preferred, is often not a requirement.
See Also: How I landed a Data Analyst job at a Fortune 20 company with no prior experience.
Soft Skills
1. Communication / Presentation
Communication as well as presentation skills are hands down, the most important soft skill a data analyst can have. Data analysts not only manipulate and clean data, create beautiful dashboards, and solve problems like a badass, but they also have to thoroughly communicate with stakeholders and other end users about the expectations of a project, as well as present the final product.
Effective communication with your customers will often lead to fewer iterations of the “final” product; therefore, less back-and-forth and wasting of time that would have occurred from misalignment of expectations vs. reality. Identifying needs vs. wants, and setting the right expectations go hand in hand with customer satisfaction of the end product.
2. Time management
With being a data analyst comes a variety of different projects and goals on a weekly, and even daily basis. To add to that, the environment of a data analyst team is one of a dynamic nature, in that no one day is the same. Projects fly in and out of the radar and a data analyst’s job is to balance the ever-changing priorities that can float around any given day.
3. Detail-oriented
Handling data is an enormous responsibility. There are many crucial steps into getting a data analytics project from A to Z. Within those steps, anything could go wrong if there is no attention to detail. No matter how you slice it, data that starts off wrong will ultimately yield the wrong results in a dashboard or story. Attention to detail will go a long way in identifying miscalculations, duplications, and even spelling errors for any given field or value. The QA process of any project or step in a process will ultimately save you headache and time in the long run.
Conclusion
In the end, with the fields of data science/analysis being relatively new to the world, the requirements are constantly changing. As technological advances and resources to learn these tools are evolving as well, we must also do our due diligence in keeping up with the trends of these common-used tools. This is the one and final tip for becoming a successful data analyst; continue to learn, grow and try new things. Don’t rely on the “If it ain’t broke, don’t fix it” mindset. Question how things were being done, propose newer, better, more efficient solutions. More often than not, things can always be done smarter and not harder. Thank you for reading.
—
If you’re curious about my journey as a data analyst, check out my other posts related to how this former Marketing graduate, with no technical background, made a change to his path and ended up an HR Analyst in a Fortune 20 company.
