5 Top Competencies That Lay the Foundation for A Successful Data Analyst Role

5 Top Competencies That Lay the Foundation for A Successful Data Analyst Role | Data Analytics | Emeritus

The current data analyst job market is expected to grow by 23% by 2031 (Career Foundry). As data in organizations is increasing by leap and bounds, they are looking for professionals to collect and analyze them. However, recruiters prefer skilled data analysts instead of ones without any formal training. If you are serious about your data analyst career, start focusing on developing the following skills:

Data Visualization and Cleaning

Data visualization refers to a person’s ability to display data findings using graphics and illustrations. Data visualization aims to offer clear, data-driven insights, especially for people without any data analysis training. Data visualization enables data analysts to aid the decision-makers in a business in recognizing patterns and decoding complex ideas.
It enables data analysts to gather a better understanding of a company’s situation and convey meaningful insights to team leaders. It also helps improve decision-making inside an organization. Data visualization has the ability to change how data analysts deal with data and help them achieve more than they have traditionally.
Data visualization techniques will help data analysts to respond to issues more rapidly. They will also be able to conceive data differently with a better imagination, leading to creative data exploration.
If you consider cleaning to be an integral part of achieving success, data cleaning won’t be any different for you. It is a crucial step for putting together a functional machine-learning model. Data cleaning is often a large part of a data analyst’s regular responsibilities.
Cleaner and better data can beat the fanciest of algorithms. A clean dataset will offer remarkable insights using even the simplest algorithms. Data cleaning skills are in-demand because unclean data produces misleading patterns and guides a business toward the wrong conclusions.


The programming language SQL is crucial for managing relational databases. Data analysts often use SQL to run queries on the data within the databases. SQL is useful for processing large datasets instead of computing tools and spreadsheets like Excel.
Structured Query Language was created in 1970, but its importance is still evident. The functionality and effectiveness of SQL make it highly demanding among multiple companies. Apart from being an essential skill for data analysts, SQL is also suitable for introducing yourself to programming languages.
Data analysts also need to focus on developing their aptitude with NoSQL databases. NoSQL does not organize datasets along the relational lines of SQL. Instead, NoSQL frameworks can structure data in every possible way other than relational methods.
NoSQL databases are preferred by organizations because of their flexibility and horizontal scalability. NoSQL also comes with real-time analysis capabilities and data consistency. If you want to increase your knowledge about NoSQL structures, you can try to experiment with frameworks like MongoDB because it can organize databases along flexible hierarchies and not tabular relations.

Statistical Programming Languages: Python and R

Learning Python should be the top priority for every data analyst. The high-level programming language comes with multiple specialized libraries. Most of the Python libraries pertain to artificial intelligence, and the applicability of Python to AI development is crucial.

R is frequently used in data analytics because its syntax was created to aid different kinds of analytical work. The syntax of R includes various easy-to-use and built-in organization commands by default. Businesses also prefer R because of its ability to deal with huge volumes of complex data.

R or Python can do everything that Excel does but ten times faster. Python and R are powerful statistical programming languages and perform predictive analytics and advanced analyses on big data sets. They can even perform what Excel can’t.

Calculus and Linear Algebra

Advanced mathematical skills are a non-negotiable skill when it comes to data analytics. Some data analysts even have a major in statistics or mathematics to have a better understanding of real-world analytical practices.
Two particular fields in mathematics, linear algebra, and calculus are crucial for data analytics. Linear algebra is useful in machine and deep learning, and it supports matrix, vector, and tensor operations. Calculus is useful for building functions that enable algorithms to achieve their objectives.

Microsoft Excel

While talking about Excel, you probably only think about a spreadsheet. However, the tool has more analysis power than you know. Programming languages like R and Python might be better suited for handling large data sets. But advanced Excel methods like writing Macros are used for quick and light analytics.
Excel has its own programming language, VBA. It can create pre-recorded commands and helps data analysts save time on frequent and repetitive tasks. It has also designed the Analysis ToolPak to make data analysis and statistical modeling easier.
Data analysts at a startup or lean company usually have their first database version in Excel. The tool has remained important for businesses in every industry over the years. You can use online free resources or enroll in structured data analytics classes to develop an in-depth understanding of Excel.

The Importance of Soft Skills for Data Analysts

While technical knowledge is crucial for data analysts, they also need several soft skills to fulfill all their responsibilities. Critical thinking is one of the most important skills for data analysts. They should know how to use data to find answers to questions.

The role of a data analyst is to uncover connections that might not be prominent. Critical thinking skills also involve staying grounded while looking for solutions instead of getting carried away with complex explanations.

Communication skills are also crucial for data analysts because they need to convey their findings to others. Data analysts should have the ability to explain the patterns they see to those without technical expertise. Without proper communication skills, there’s no point in being an insightful data analyst.

Data analysts also need to be bilingual. They need to address technical points with trained peers. At the same time, they also need to deliver clear explanations to key decision-makers inside an organization.

Presentation skills are also crucial for data analysts. Your presentation skills should go hand in hand with your data visualization skills. Data analysts might struggle with their presentation skills in the beginning but will develop them with time.

Parting Thoughts

Acquiring the right skills is crucial for data analysts to advance their careers. Data analysts with proper skills are in high demand, with all buzz centered around Big Data. Remember that improving your data analytics skills will mean higher salaries and better opportunities.

About the Author

Senior Content Contributor, Emeritus Blog
Varun, a seasoned content creator with over 8 years of diverse experience, excels in crafting engaging content for various geographies and categories. Leveraging this expertise, he seamlessly translates complex concepts into enriching educational content for the EdTech domain. His keen understanding of research and life experiences helps him resonate with students and create fact-based content. He finds solace and inspiration in music, nurturing his creativity for content creation.
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