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16 Data Analyst Skills You Need to Get Hired

Incredibly, two and a half quintillion bytes of data are produced by humans every day. International Data Corporation (IDC) recently released a report stating that the market for big data and business analytics has been growing rapidly over the past few years, increasing from $122 billion in global revenue in 2015 to $189 billion in 2019, and on its way to a projected $274 billion for 2022. This quick expansion creates a sizable potential for you to advance your data analytics expertise. Talented data analysts are more in demand in nearly every sector of the economy, including telecommunications, retail, manufacturing, banking, healthcare, and even fitness, thanks to digital marketing. However, there are a few necessary data analyst skill sets that must you must have to become a data analyst. Several Data Analytics courses are available to offer the required training. This article will shed light on the required data analyst skills and qualifications to become a competent data analyst and will help you realize your dream of working in this field by giving you a thorough understanding of what you need to do and the skills you’ll need to succeed.

Top Data Analyst Skills To Master To Get Hired

What is Data Analytics?

Before delving into Data Analyst skills, qualifications, and other attributes let’s look at what data analytics is. Data analytics is the practice of carefully examining raw data to derive relevant insights from it. These insights could include knowledge about your company, new products, market trends, etc.

The type of data gathered may be structured, semi-structured, or unstructured. Visual representations of the result, such as graphs and charts that show the research’s precise findings, are available. The analytical process makes use of several tools and frameworks.

Professionals who can assist businesses in transforming raw data into knowledge that is useful for corporate success are in great demand. Data analysts have some of the most exciting career options among the various employment types in the data analytics industry.

Who is a Data Analyst?

Large datasets are collected, prepared, and analyzed by data analysts. This information may take the shape of client reviews, logistics, financial records, market research, and so forth.

This information is gathered to determine a range of actions, including how to improve customer satisfaction, lower transportation costs, and set prices for new products, to name a few. Data handling, data modeling, and reporting are the domains of data analysts.

As businesses have gotten more data-driven, the function of the data analyst has become more crucial. They aid companies in making sense of the massive amounts of data they get. They clean and analyze data using their knowledge of mathematics and computer science, then they convey their findings.

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Skills Required by Data Analysts

However, a career in data analytics won’t be rewarding without a lot of training and effort. Data analysts need a special set of skills to succeed in their line of work, and their education is mostly technological; nevertheless, they also need a few soft skills.

You can acquire these skills by applying to a Data Analytics course or you can also self-train yourself through books and videos. But whichever path you choose, you’ll need to build a solid foundation of skills if you want to succeed as a data professional.

Here we have listed the top data analyst skills – both technical skills and soft skills that one must possess for a rewarding career as a data analyst.

Technical Skills

  1. Data Visualization
  2. Data Cleaning
  3. Data Analysis and Exploration
  4. Creating Dashboards and Reports
  6. R
  7. Python
  8. SQL and NoSQL
  9. Machine Learning
  10. Linear Algebra and Calculus
  11. Microsoft Excel
  12. Domain Knowledge
  13. Econometrics

Soft Skills

14. Critical Thinking

15. Problem-solving

16. Communication

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 Data Analyst Skills – The Technical Aspect 

Data analysts are solely interested in data. They sort through a lot of raw data using technical tools, which helps them come up with insightful conclusions. In addition to cleaning up corrupted data, assessing data quality, and creating reports for their employers, – also form the duty of data analysts.

As you might have imagined, each of these duties calls for data analysts to have a well-rounded set of technical capabilities. The Data Analytics course provides a solid foundation to acquire these technical skills. Here are a few:

1. Data Visualization 

The ability to present data findings using graphics or other visuals is known as data visualization. Its main goal is to make it easier for people who aren’t skilled in data analysis to comprehend data-driven insights. Data analysts can use data visualization to assist decision-makers in a firm in finding patterns and quickly comprehending intricate concepts.

With this capacity, you, the data analyst, are more equipped to comprehend a company’s condition, share insightful information with team leaders, and even influence corporate decision-making for the better. Recent grads are considerably more likely to master hard skills when they first enter the workforce, according to a recent study by LinkedIn Learning. Learning visualization tools like Tableau is often necessary to advance your data visualization skills. You may turn your analysis into data models, dashboards, business intelligence reports, and visualizations by using this widely used piece of software.

2. Data Cleaning

Data cleaning is no exception to the rule that cleansing is essential to success. It’s one of the key steps in developing a valuable machine learning model, and it usually consumes a significant chunk of any data analyst’s day. Data cleansing takes up about 80% of the time that data scientists are working.

Why? Better data consistently outperforms more complex machine learning algorithms, which is the cause. Given a clean dataset, even basic algorithms can generate astounding insights. On the other side, incomplete data could lead to misleading trends and mislead a business. Effective data cleaning skills are a must for data analyst certification, there is no way around that.

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3. Data Analysis and Exploration

Although including “data analysis” in a list of critical data analyst skills may seem odd, analysis as a specific skill is essential. In its most basic form, data analysis includes taking a business query or a requirement and researching pertinent data to create an answer.

The exploration of data is another type of analysis. Data exploration is the process of looking for intriguing patterns or connections in the data that may have commercial worth. Exploration may be somewhat unguided or directed by a business question. You can discover a way for the company to cut costs or boost revenue by searching for trends in the data. While pursuing a Data Analytics course, you will get a good hang of Data Analysis and exploration.

4. Creating Dashboards and Reports

Data visualizations make trends and patterns in data clearer. Humans are visual beings, thus most people will comprehend a chart or graph more quickly than they will a spreadsheet because of this. To do this, make clear, eye-catching charts that will aid others in comprehending your findings.

Additionally, it involves staying away from anything that can be deceptive or challenging to understand. You should consider including visualizations in your data investigation. There are occasionally items in the data that are visible visually but may be hidden if you merely look at the numbers. Data visualization is a crucial data analyst ability because it is quite difficult to find a data science job that doesn’t involve it.


MATLAB is a programming language and multi-paradigm numerical computing environment that, among other things, facilitates the development of algorithms, the manipulation of matrices, and the charting of data. Because MATLAB enables analysts to significantly reduce the amount of time they typically spend pre-processing data and makes it simple to quickly clean, organize, and visualize data, businesses interested in big data have started to turn to it.

The ability of MATLAB to run any machine learning models created in its environment on several platforms is its most notable feature. Although knowing MATLAB isn’t a prerequisite for data analysts, having at least a working knowledge of the environment might increase your marketability to companies given its extensive uses and utility.

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6. R

One of the most popular and widely used languages for data analysis is R. R placed fifth in a survey of the top ten programming languages used in 2019 by the professional journal Spectrum of the Institute of Electrical and Electronics Engineers (IEEE).

R’s syntax and organization were developed to enable analytical work, and by default, it has several built-in, simple-to-use data organizing functions. The programming language is popular among businesses because it can handle complicated or vast amounts of data. Any aspiring data analyst should make learning R their top priority given its popularity and functionality. This is a compulsory topic, covered in the Data Analytics course.

7. Python

For aspiring analysts, mastering Python should come first. For good reason, this high-level, general-purpose programming language took first place in IEEE’s Spectrum 2019 poll, as it provides an impressive amount of specialized libraries, many of which are relevant to artificial intelligence in particular (AI). It’s crucial that Python can be used for AI development.

The AI software market is expected to grow 154 percent annually and reach a predicted height of $22.6 billion by the end of 2020, according to statistics released by Statista. Data analysts need to be proficient in Python if they want to stay relevant in the increasingly AI-focused professional environment. Those who are interested in expanding their knowledge of Python should also look into its supporting software, such as NumPy, a package that helps Python users with scientific computing jobs, or Pandas, an open-source data analysis tool that works together with Python’s programming language.

8. SQL and NoSQL

There are several database languages that you should be familiar with if you want to go into data analytics. The most important of these is Structured Query Language, also referred to as SQL. Even though SQL was developed in 1970, its value has not diminished. SQL continues to be the de facto method for processing and accessing data in relational databases in modern analytics.

Every company uses SQL to some extent. The demand for it has remained high among businesses due to its functionality and continued efficacy, and there are no signs that this trend will soon change. If not for its purpose, then at least for your career chances, learn SQL. Branded SQL implementations like MySQL present chances to learn more about relational database management systems. 

You should also concentrate on improving your skills with NoSQL databases. NoSQL systems don’t structure their data sets along the same relational axes as SQL, as the name implies. By this definition, NoSQL frameworks are capable of effectively structuring their data in any fashion, as long as it’s not a relational approach.

As a result, identifying a single framework as the “standard” NoSQL framework is all but impossible. However, it might be beneficial to try a framework like MongoDB, which organizes its database around flexible hierarchies rather than tabular relations, if you wish to get familiarity with NoSQL structures. Both SQL and NoSQL are covered in the Data Analytics course.

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9. Machine Learning

Understanding machine learning can make you more competitive in the recruiting market for data analytics even though it isn’t a skill in the same way that data cleansing or learning a programming language may be. Predictive analytics and artificial intelligence are currently receiving a lot of investment, according to research from Statista. Even while not all analysts work on machine learning projects, being at least somewhat familiar with the related tools and concepts could provide you an edge over competitors in the job market.

10. Linear Algebra and Calculus

It is essential to have advanced mathematical abilities when it comes to data analytics. To better understand the theory that supports in-depth analytical practice, some data analysts even decide to major in mathematics or statistics during their college years. In analytics, two particular branches of mathematics—linear algebra and calculus—take center stage. Because it enables vector, matrix, and tensor operations, linear algebra has applications in deep learning and machine learning. Calculus is also used to develop the objective, cost, and loss functions that show how to teach algorithms how to accomplish their goals.

11. Microsoft Excel

Excel does a great job at automating some functions and commands for improved data analysis. VBA, an Excel-specific programming language, is used to develop macros and pre-recorded commands. When used properly, VBA can significantly reduce the amount of time needed by human analysts to do routine tasks like accounting, payroll, or project management. Statistical modeling and data analysis were also taken into consideration when Microsoft created its Analysis ToolPak. Excel, in its Advanced form, is explained in great detail in Data Analytics Course.

12. Domain Knowledge

Understanding subjects that are particular to the business you work for and its industry is known as domain knowledge. For instance, if you work for a business that has an online store, you may need to be familiar with the complexities of e-commerce.

In contrast, you might need to be familiar with the operation of those specific mechanical systems if you’re evaluating data about them. No matter where you work, it will be harder to complete your tasks if you don’t grasp the data you’re examining. You can most definitely pick this up on the job. Building as much knowledge as you can in advance about a certain business or area will, however, make you a more desirable job applicant—and a more productive employee.

13. Econometrics

Economists are used to estimating future trends based on previous data by using mathematical and statistical data models in the study of economics. Data analysts seeking employment in the financial sector, notably at investment banks and hedge funds, must have a solid understanding of econometrics.

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Soft Skills Required by Data Analysts

Technical skills are not the only thing that matters for aspiring data analysts. They also need several softer, non-industry-specific abilities to be successful. While pursuing the Data Analytics course, you will get to explore and develop these much-required soft skills. We have listed the 3 most important soft skills, required by data analysts:

14. Critical Thinking

When you can think critically, you can analyze data, identify trends, and extracting out knowledge and ideas that can be put to use. You have to put in extra effort and commit yourself to thinking rather than just processing. Looking at data isn’t enough; you also need to comprehend it and consider its ramifications in light of more than just the statistics. Though developing critical thinking abilities can be tricky, you can do so by pushing yourself.

15. Problem Solving

Every day as a data analyst, you will encounter issues, defects, and barriers. Another essential talent for a data analyst is the ability to work your way out of problems. Here are a few possible outcomes:

  • You might need to do some study on a bug in the program or coding language you’re using.
  • Your business’s resource limitations may compel you to think creatively about how you solve problems.
  • You could be using insufficient data.
  • You might need to conduct some “good enough” analysis to achieve a pressing deadline.
  • Strong problem-solving abilities will always be a huge asset for any data analyst, regardless of the situation.

16. Writing and Communication

Another essential data analyst skill is the capacity to communicate in many formats. You can excel in any data analytics profession by developing your communication skills in writing, speaking, explaining, and listening. Collaboration with coworkers requires effective communication.

For instance, having attentive listening skills will help you comprehend the analyses needed at a kickoff meeting with business stakeholders. Similar to this, you might have to impart sophisticated information to non-technical teammates during a project.

Additionally, written communication is crucial, particularly when you’re summarising your study or describing a finding from your data investigation. Your career in data will advance if you can communicate effectively and directly. Even if it’s a “soft” skill, don’t undervalue it.

Qualifications and Prerequisites for Data Analytics course:

Data analysts need to have certain skills and certifications to succeed in their position. Data analysts who meet these requirements and prerequisites will be well-positioned to succeed in their positions and offer insightful information that can aid firms in making better decisions. Before you apply to a Data Analytics course, you must check the following requirements:

  • You must have domain expertise in the field you are working in and possess a degree in any relevant field.
  • First and foremost, data analysts should be proficient in mathematics and be able to examine large data sets.
  • Second, they should have experience working with statistical software programs like R, SAS, or SPSS.
  • Finally, successful presentation of their findings to clients or other stakeholders requires that data analysts possess great communication skills.

FAQs related to Data Analyst skills 

1. How do you list skills as a data analyst on a resume?

Remember to update your resume as you add new skills to your toolbox as a data analyst. Include a “skills” section where you identify your top five data skills in a list of bullet points. Be ready to talk about any skills you listed on your resume throughout your interview.

Additionally, it makes sense to apply your skills in the right context. Try to include a phrase describing how you used a certain ability to fulfill a task when you include data analysis projects or past responsibilities (for example, “Wrote a Python script to scrape data using the official Twitter API” or “used Tableau to show product sales over time”).

2. How can we acquire these Data Analyst skills?

You may have a fruitful career as a data analyst by investing the required time and effort to develop these skill sets. Enrolling in a Data Analytics course will help you in attaining these skill sets. Here are a few simple pointers to get you going:

  • Make time to practice your skills regularly.
  • Take note of your errors.
  • Use actual data projects as the practice
  • Join a data community online.
  • Develop your abilities gradually.

3. Can a non-technical candidate apply to the Data Analytics course?

There is a widespread misconception that you need a degree in mathematics or statistics to work as a data analyst. But this isn’t the case at all. While knowing these topics is advantageous, it is not required. In reality, many data analysts have backgrounds outside of IT. However, they share a knack for problem-solving and a keen interest in dealing with data. Regardless of your educational background, if you possess these traits, a career in data analysis may be a good fit for you.

Of course, having a degree in statistics or mathematics will greatly help you when it comes to comprehending complex data sets. It’s crucial to keep in mind that data analysis involves more than merely crunching numbers. It also involves being able to express your conclusions understandably and succinctly. Therefore, even if you lack a background in math or statistics, you can still be a strong contender for a position as a data analyst if you are effective at presenting your thoughts. While a degree isn’t necessarily necessary for a data analyst position, getting the necessary Data Analyst course from a reputable institution is essential.

Conclusion on data analyst skills and qualifications 

You may be interested in a job in data analytics, but to be successful, you must acquire the necessary data analyst skills. Look into local academic alternatives; compare master’s and undergraduate degree options. Consider taking a data analytics course if you’re seeking a more expedient and affordable answer. The above-mentioned 16 Data analyst skills are absolutely essential to excel in this field.

These multi-week educational choices give students the chance to affordably get a solid foundation in the tech field of their choice. The Data Analytics course will help you to gain the skills you need to thrive in this booming business in a fraction of the time and for a fraction of the expense because it only concentrates on all elements of data analysis. Explore your educational options and start building a strong base of Data Analyst skills.

Having done my Masters in English Literature, from the University of Calcutta, I always have had a love for reading and a flair for writing. My strong academic results made me inclined toward academic jobs, but my love for writing lingered. I have worked as an Assistant Profesor in Business Communications in different colleges over the years. Now I am pursuing my passion for writing. Articles, Blogs, and Content creation make me explore this passion to the fullest. I have successfully completed the Content Writing Master Course Certification from IIM Skills. I am trying to gain a strong foothold in the world of Digital Content Creation.

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