5 In-Demand Skills You Need to Excel as a Data Analyst




It’s no gainsaying that the modern world lives on data. In 2020, the analysis of both structured and unstructured data stored electronically ran into more than 44 zettabytes. The figure rose to a staggering 2.5 quintillion bytes in more recent research.  

Owing to this and more, the demand for data analysts keeps rising. Job openings in this field are expected to grow by more than 23% between 2021 and 2030, according to the Bureau of Labor Statistics. If the role of the data analyst is as significant as projected, what skills are needed to excel in the career?

This article examines 5 in-demand skills needed to excel as a data analyst. But before examining these 5 skills, let’s understand who a data analyst is.

Who is a Data Analyst?

Data Analyst

A data analyst is a professional who uses analytical and statistical methods to examine and interpret both structured and unstructured data. Data analyst work with data. They use software tools to process, clean, and interpret data in the form of analysis. The goal is to communicate their findings to stakeholders and inform further business decisions. 

The data analyst role is not limited to technology industries. Healthcare, finance, and retail companies need data analysis to thrive, beat competition and achieve overall productivity. To excel as a data analyst, you need these five skills. 


data analyst

Structured Query Language (SQL) is the standard language used to communicate with data. As such, the knowledge of SQL helps you to collect information and interpret it. Also, you need SQL to access your company’s information in relational database and modify data structures (schema). SQL is also important for processing large information databases, which spreadsheets cannot do.

Because of the many things a data analyst would need the knowledge of SQL for, it is seen as the most required skill to excel in the job. 


Data analyst

“Mention excel to techies, and it’s often dismissed with a sniff,” says Anne Walsh, Irish business writer. 

Microsoft Excel might be old clunky and not so techie like other software, but the knowledge of excel can make your work easier as a data analyst. Some organizations prefer that you have the knowledge of both SQL and Excel. The simplicity and ease of using spreadsheets make them incredibly useful in analyzing and reporting data information. 


Data analyst

After you have collected and analyzed your data, communicating your result to the company is the next step. If you can’t communicate your findings in a very simple and understandable way, this might affect your performance in your career. As such, the ability to communicate findings and insights to both technical and non-technical stakeholders is important to thriving in your career as a data analyst. 

If you want to start a career as a data analyst, up your communication skill. Learn how to simplify highly technical points to others in a way that supports and does not confuse further business decisions.

Critical Thinking

Data analyst

Data analysts collect information from different search engines. To do this successfully, you should know what to ask the search engine. Data analyst thinks like an analyst. They uncover and synthesize unclear information, unravel the confusion, ask the right questions to clarify issues, and arrive at a more comprehensible result. Data analysts are critical thinkers.

Asking critical questions during your research helps collect valid information and sieve unwanted ones. This further enhances your result and impact your decisions.

Machine Learning

Data analyst

Automation is essential to analyze large-scale data. The go-to way to do this is Machine Learning (ML). Machine learning enables computers to learn from data without being explicitly programmed. It involves using algorithms and statistical models to analyze and understand patterns in data and then using that understanding to make predictions or decisions without human intervention. 

As a data analyst, you should be able to create, apply and train the most appropriate model and algorithm to datasets to produce a good automation result.

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