The Top 5 Skills and Tools You Need to Thrive as a Data Analyst in 2023
Becoming a data analyst has become relatively easier in the last few years. There are several courses and programs you can enroll in, and many organizations now offer bootcamps and certifications in the field. Thanks to the wonderful folks at the Corporate Finance Institute, I recently became a certified financial analyst and business intelligence professional.
Achieving that was not easy and took a lot of effort and time. However, the best part of the journey was the focus on learning practical skills and tools used in the industry.
Of course, while my specialty is finance, the tools and skills I learned are applicable across most industries where data analytics is employed. That’s what we’re going to examine. Let’s jump in.
1. SQL & NoSQL
SQL, commonly referred to as “sequel,” stands for Structured Query Language, and no, it’s not a programming language. It’s fairly easy to learn, even without coding knowledge.
SQL is a required skill in many data analyst job descriptions, and potential employers may ask you to complete a technical task using the language during an interview. Consequently, learning SQL is important as you’ll use it to access, analyze, explore, and query data stored in a relational database on the job.
Several SQL brands exist, including MySQL, Microsoft SQL, and PostgreSQL. These are SQL database software created by different entities, and while they differ slightly in appearance, they’re similar in syntax, functionality, and ease of use.
Now, NoSQL. It is the opposite of SQL in that it’s a query language that explores datasets that aren’t structured along relational lines. It’s effective for analyzing non-relational database management systems, which are increasing daily.
Popular NoSQL systems include frameworks like MongoDB, OrientDB, and Apache Cassandra, albeit the latter isn’t as well-known as the first. These frameworks organize their databases using flexible hierarchies rather than tabular relations.
2. Spreadsheets (Microsoft Excel & Google Sheets)
Spreadsheets are popular tools for beginner data analysts to gain a foothold in their careers. Microsoft Excel is an industry leader in data analytics, and the list of things you can do with it is endless. Learning how to use Microsoft Excel is easy, given the plethora of free resources on the Internet.
Excel is immensely popular among data analysts and employers as it excels at automating features and commands to optimize your data analysis. Additionally, it now comes with a specialist Analysis ToolPak, which allows users to set parameters on provided datasets. The Analysis ToolPak then reads the data and deploys its inbuilt macro-functions to calculate and display the results of its analysis using tables and charts.
Finally, Excel VBA, which is Excel’s programming language, allows you to create macros (pre-recorded commands). After creating a macro, you can deploy it to handle repetitive tasks, including project management and payroll. These functionalities will save you hours of valuable time, which you can devote to other essential tasks.
Google Sheets is a newcomer to the field and doesn’t boast Excel’s history and advanced functionality. However, we should not sniff at its value, and you can use it for basic and slightly tasking data analysis projects, including data integration.
3. Statistical Programming Languages (Python & R)
Python and R are two of the most well-known statistical programming languages, although Python is also a general-purpose programming language. Both languages are open source and among the top ten most used globally, allowing you to perform advanced data analysis.
R’s syntax and structure are built specifically for data analytics and contain numerous inbuilt data organization commands. Conversely, Python can accomplish many of the same tasks efficiently. Additionally, Python is easier to learn, especially if you have no coding experience.
Learning either Python or R is a great choice. They’ll allow you to build complex programs to explore and analyze data. Finally, Python contains hundreds of specialized libraries, including Pandas and NumPy. These libraries are invaluable for understanding artificial intelligence applications.
4. Data Visualization Software (Power BI & Tableau Public)
Knowing how to visualize the data you’ve analyzed is a skill that can take your career to the next level. As humans, visual communication is our best way of understanding things. C-suite executives, colleagues, and others can better understand your work when its visuals tell a story that enables them to identify patterns and understand complex ideas at a glance.
Data visualization involves using charts, maps, and other visuals to display the results of your analysis and insights. It also allows you to go beyond traditional data analytics roles and can fundamentally change the way you work with data as it promotes creative data exploration.
Today, the two most popular data visualization tools are Tableau Public, and Power BI. Tableau Public allows you to create visually appealing, interactive data models, dashboards, and business intelligence reports. It’s also fairly easy to master, and the customization features it offers are so good you can create nearly any visual type imaginable. You can view one of my interactive dashboards here.
Power BI is also invaluable for analysts who spend most of their time working in business intelligence and crunching the figures that allow organizations to determine what next to focus on. Microsoft’s proprietary visualization software allows you to explore and pull data from SQL databases, Excel sheets, CSV files, and other dataset storage files.
These tools are essential if you want to take your skills to the next level, and learning either depends on personal preference and the industry you want to work in. Power BI is more commonly used in organizations focused on business intelligence, while Tableau Public is more suited to general data analytics and visualization tasks.
One final area you should try to be familiar with is:
5. Machine Learning
Machine learning primarily involves developing algorithms that can recognize patterns in huge datasets. As such, it is a key part of artificial intelligence, and machine learning algorithms like linear and logistic regression are based on statistical modeling. While you may not utilize advanced machine learning processes in your day-to-day role as a data analyst, understanding its key principles can be tremendously helpful.
Final Thoughts
Data analytics is an exciting field where endless possibilities exist. Whether you’re interested in applying AI technology to solving human problems or want to help companies implement better data-driven solutions, the field is broad enough.
Also, the career outlook is promising, and annual salaries range from $73,002 to $91,552, according to Salary.com. Of course, your remuneration will depend on several factors, including your skillset, experience level, and the industry you choose to work in.
Exploring data analytics can be your gateway to a successful career in tech, and burgeoning fields like artificial intelligence make for an even more promising future.
