These Data Analyst Skills will Get You Hired in 2023

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If you are curious about seeking a successful career in data analytics, you must gain the necessary skills or abilities essential for this field. In this article, you will gain a detailed understanding of eight skills needed for data analytics to get hired in 2023 for a stable and high-paying role. Learning these skills will unlock the path to becoming a Data Analyst for different sectors.

What skills are needed for data analyst?

The best news is to become a data analyst; you don’t need a technical or coding background. It’s advantageous to have a technical background and a sharp mathematical brain. Data analytics involves investigating a business problem, data collection, cleaning, analyzing, and interpreting.

Different skill sets are used at every step, combining different technical and soft skills which anyone can learn if they are serious about making a long-term career in data analytics.

Knowledge of statistical analysis and programming languages such as Python, R, and SQL.

SQL ( structured query language ) is a programming language to pull or extract data from regional databases to collect, store, and retrieve raw data or information. Every data analyst will use SQL, making it a crucial skill to acquire in the step of Data collection.

Beginners can learn it quickly from different platforms and start with understanding basic queries. Not learning SQL can make it challenging to land a high-paying job in data analytics.

Python and R are open-source statistical programming languages that are a must to learn for beginners in Data analytics to get an edge over other competitors in landing high-paying jobs. Looking at the popularity and usage of R and Python, every aspiring data analyst should learn them as a top priority.

Not all businesses need to use Python and R, but most do. It’s optional to learn Python and R initially but get the basic knowledge because many interviewers may ask questions.

Ability to interpret data and draw meaningful conclusions from it.

Once data analysts collect, clean, and arrange their data using different programming languages, the next step is analyzing it. You will only succeed as a data analyst if you can describe the information you analyzed to people with non-technical knowledge. It would be best to have analytical skills/minds, or especially a willingness to develop once.

Knowing programming languages and tools would be helpful; you can successfully explain your findings to your company stakeholders. As you start to work on practical projects, you will develop analytical skills eventually.

Excellent problem-solving skills.

Every time a data analyst gets different problems and bugs to solve at different stages. Approximately 90% of data analytics job tasks involve critical thinking, assisting you in formulating the right questions and comprehending the best solutions for your organization.

Developing critical thinking skills may initially seem challenging, but you can with regular practice. 

Start thinking about the meaning in the data pattern you notice when given any analytical task. When training for a career in data analytics, pay attention to sharpening your critical thinking abilities.

Familiarity with machine learning algorithms and techniques.

Knowledge of machine learning algorithms and techniques can assist you in evolving competitively in the data analytics field and increase your likelihood of getting selected for interviews.  Data Analysts use Machine Learning techniques and algorithms for data collection, preparation, and processing.

As an aspiring data analyst, staying updated with the latest Machine learning tools and techniques is crucial for the best growth in this competitive world.

Proficiency in data visualization tools such as Tableau and PowerBI.

Another crucial step in data analysis involves presenting the analyzed information visually appealingly through maps, charts, graphs, etc., because people understand them more quickly than spreadsheets with large datasets.

Different organizations require data visualization software tools like Tableau and PowerBI to assist data analysts in presenting their complex data-driven results through dashboards, mathematical models, and business reports. Tableau and PowerBI (an advanced version of MS Excel ) provide various options to present analyzed data via graphics or visuals. You don’t need coding knowledge to learn PowerBI and Tableau.

Many companies solely employ PowerBI for Data visualization. Beginners can enhance their probability of getting selected for interviews if they know PowerBI or Tableau.

Good understanding of core database concepts.

Every programming language, tool, and statistical and mathematical model used in Data Analysts involves a core database concept that every sector commonly uses. For example – Some common SQL database concepts to learn for every data analyst to get entry-level jobs – Select statement, where clause, COUNT(), AVG(), and SUM() Functions, order by, AND/OR operator, and between operators. Similarly, research to know more fundamental concepts common to technical skills.

Ability to work with large datasets.

With time, a data analyst can get large datasets depending on the type of business or sector they’re working in. Data analysis requires various statistical tools to discover, explore, and crack patterns in large data sets for problem diagnosis and solutions prediction.

After learning technical tools and programming languages, every data analyst must know how to apply them to different datasets. Implement your learning on live data projects to get hands-on experience.

Excellent communication and presentation skills.

For all data analysts, the above-described technical skills are essential, but individually they will not direct to a booming career. Think about why companies hire data analysts. From the raw data, it’s the responsibility of data analysts to explain their insights to non-technical people like your colleagues, seniors, stakeholders, etc., through presentations by communicating in an understandable language.

Not everyone knows the analytical methods data analysts use to process information. Your hard work would reflect directly in your findings that assist businesses in making data-driven smart decisions for exponential growth. Communication is vital when cooperating with your office colleagues.

How to include data analyst skills on your resume

By including all the above technical and soft skills in your resume, you can stand out among other job-seekers. Make a separate “Skills” column in your resume. You can write a maximum of five skills on more but be prepared to explain when the interviewer asks.

If you have done a project using technical skills, write one or two sentences on how and what you implemented. For example- utilized the Tableau tool to visualize the company’s healthcare products.


Q1: What is the most important skill for a Data Analyst?

Critical thinking is most important for Data analysts because you have to make logical decisions at every step of the data analysis process, like using which tool to solve which type of problem or discussing your discoveries with other team members. You have to think every time.

Q2: What type of problem-solving skills are needed for a Data Analyst?

Every data analyst applies problem-solving skills mostly in driving predictions, organizing data, spotting unusual trends, uncovering connections, and locating patterns. You have to act as a critical analytical thinker at every stage. Data analysts follow organized and logical procedures to problem-solve. A creative mind also allows one to recognize hidden relations & problematic inconsistencies to pull significant insight.

Q3: What type of software tools should a Data Analyst be familiar with?

Every data Analyst should be familiar with the software tools below to implement the above skills. 

1. MS Excel

Microsoft Excel is the most widely used software tool for data analysis. Its versatility lets you manage, filter, sort, diagnose, and visualize collected data via spreadsheets. It’s a must for every Data Analyst to learn the basic concepts of MS Excel.

2. Python and R

Both programming languages are widely popular in Data analytics. Python is easy to understand and utilized to streamline, visualize, and interpret data via its Data analytics tools. Data Analysts use R mainly for statistical calculations, data visualization (create captivating graphics), & manipulation.

3. PowerBI

PowerBI ( Business Intelligence tool ) is extensively used for data visualization. It provides a drag-and-drop feature to create customized dashboards, graphics, and reports.

4. Tableau

Like PowerBI, Tableau is also a data visualization tool predominantly employed for business data analytics. It provides a user-friendly interface expressing massive data sets in understandable graphics. 

5. SAS

Business data analysts broadly utilize SAS because it combines robust analytic tools with an appealing graphical user interface (GUI) for data visualization and statistical analysis.

Q4: What communication skills are necessary for a Data Analyst?

Speaking your insights, explaining your visuals/graphics, and active listening are crucial communication skills necessary to succeed in all data analytics profiles. Written Communication is extremely vital — specifically when writing reports/summaries of your data analysis. You can attend Data Analytics Conferences.

Q5: Is it important for Data Analysts to have a strong understanding of mathematics?

Every Data analyst would have to deal with varying amounts of quantitative data to drive valuable insights for their company. It’s not necessary that a data analyst must know advanced mathematics to get into this field, but basic knowledge of commonly used mathematical concepts, including statistics like percentile, arithmetic, mean, standard deviation, etc. 

With time, you can grasp command over more mathematical concepts to advance in your career.

Q6: Do Data Analysts need to be familiar with big data?

Big Data is a compilation of data massive in volume and increasing exponentially. Yes, Data analysts can receive Big Data to work on. All types of Data Analytics – diagnostic, descriptive, prescriptive, and predictive- employ big data to solve different business problems. Data Analysts require Tableau for the visualization of Big Data.

Q7: Are team collaboration skills needed for Data Analysts?

Definitely, Communication is a fundamental soft skill for a data analyst when collaborating with your team. Suppose you are meeting with your business stakeholders; being an active listener would assist you in understanding their expectations. You can get different projects to work with a team that can require you to present a complicated technical matter to non-technical colleagues. Collaboration skills are an essential part of soft skills.

Q8: What type of qualifications or certifications do Data Analysts need?

A technical or coding background is not essential to becoming a Data Analyst. You don’t require a particular qualification or certification to work as a data analyst; it’s purely a skill-based job profile. You can become a Data Analyst if you have the above practical skills. 

Many organizations or educational institutions offer data Analysts online/offline courses with live projects to help you become a certified data Analyst. You can participate in boot camps to gain practical experience. 

A bachelor’s degree is the lowest educational eligibility to become a data analyst. It’s advantageous if you hold a bachelor’s/Master’s degree in finance, computer science, economics, statistics, and mathematics, which develops analytical skills. Different sectors can set their eligibility criteria depending on the requirement.

Mark Roberts is a freelance writer and tech enthusiast based in San Diego, specializing in internet security and Ai tools.

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