10 Essential Skills You Must Have to Become a Data Analyst


What if we tell you that modern society purely runs on data? Would you simply nod in agreement or would you think this claim is outlandish? Just to back our claim about data, data suggests that humanity generates a staggering two-and-a-half quintillion bytes of data on a daily basis. And by the looks of it, this metric is only going to ascend with time. If you read any report from the International Data Corporation (IDC), you will learn that the global Big Data and business analytics market has been aggressively expanding over the past few years, and it shows no signs of stopping. With this fast-paced expansion, there only comes a significant opportunity to hone your abilities in data analytics. The modern business hopes to take itself to newer heights by adopting the concept of digital transformation to its fullest, and the need for data analyst skills has skyrocketed. Job offers are coming from almost every industry, be it healthcare, banking, retail, fitness, telecommunication, and manufacturing.


Now data analyst job openings are there, but like every career, it is always the test of the fittest. Job offers won’t be knocking on your doorstep without you having data analyst qualifications. Data analysts must have specific skills to come out as a winner in their field, and the qualifications needed are mostly tech-centric, however, like in any profession, one also needs a handful of soft skills. There are multiple ways to go about attaining skills. One can either take up a master’s program or enroll in boot camps. Irrespective of the path you choose, you will need to imbibe a specific set of skills in order to earn that high data analyst salary.

Let’s discuss some of the technical and soft skills that will surely come in handy.

If there is a place to begin your journey, it can be by figuring out what a data analyst does. Well, no marks for guessing that your primary concern will obviously be data. As you will be solely responsible for getting rid of corrupted data, determining data quality, and submitting reports to your employer, you will need to get accustomed to a wide variety of technical tools. Let’s focus on some of them.

  • Data Visualisation

Data visualization is exactly what it sounds like. It’s a person’s ability to explain data findings with the help of illustrations or graphics. The idea of this practice is very simple. It makes understanding data-driven insights very layman-friendly.  Thanks to data visualization, stakeholders can easily identify patterns and figure out complex ideas with the help of a glance. Through data visualization, an analyst can easily figure out a company’s current situation, offer effective insights to team members, and improve the overall decision-making ability of the company.

  • Data Cleaning

They say cleanliness is next to godliness, and even in the case of data, this proverb is the absolute truth. It is one of the most crucial steps in putting together a functional machine-learning model and usually takes up most of the analyst’s day.

Simple algorithms can do absolute wonders with a properly cleaned dataset. On the other hand, uncleaned data can give birth to deceptive patterns which can lead to the company drawing wrong conclusions. So long and short, an analyst cannot produce their best work without effective data cleaning.


MATLAB is the name of a programming language and also a multi-paradigm numerical computing environment that affords data plotting, matrix manipulations, and algorithm implementation, along with a wide variety of different functions. Businesses tackling big data have turned their attention towards MATLAB because of its ability to substantially reduce the time that is spent on pre-processing data. It also expedites data cleaning, organization, and visualization. The most commendable attribute of MATLAB is that it can carry out any machine-learning models built in its environment over multiple platforms.

Having a basic working understanding of the environment can help you grab the attention of employers.

  • R

R is one of the most highly renowned and celebrated languages in the world of data analytics. It came in the top 5 list of top 10 programming languages used in 2019.  The syntax and structure of R is well-suited to support analytical work. It comes with a variety of built-in, easy-to-use data organization commands naturally. This specific programming language is a big hit among businesses as it can seamlessly handle complex or huge quantities of data.

  • Python

It is at the top of the list for every aspiring and would-be analyst. This top-level, general-purpose programming language took the top spot in the IEEE’s Spectrum 2109 survey. Python is loaded with a significant number of specialized libraries, a lot of which cater specifically to AI.

In an AI-driven future, understanding the fundamentals of Python is going to be imperative for data analysts. Analysts keen on further understanding Python should show interest in its ancillary programs like Panda and NumPy.

  • Machine Learning

Although the importance of machine learning is not up there with the likes of data cleaning or learning a program, understanding it can give you a competitive edge in the data analytics hiring world.

Research by Statista indicates that artificial intelligence and predictive analytics are. taking the industry by storm now. Even though a lot of analysts might not get a chance to work on machine learning initiatives, having a basic understanding of related tools and concepts puts a beacon over your head and makes you visible to employers.

  • SQL and NoSQL

If you want to declare your arrival as a database analyst with the utmost aplomb, learning SQL (Structured Query Language) will be the way to go. Created in the year 1970, the importance of SQL is unquestionable to this day. In the world of modern-day analytics, SQL stands tall as the standard for querying and handling data when it comes to relational databases.

SQL is a part of companies across the globe because of its functionality and proven effectiveness. Learning SQL is extremely beneficial for improving your job-finding probability. Branded versions of SQL like MySQL help one drastically with gaining a better understanding of relational database management systems.

On the other hand, you also must rededicate your time and effort toward understanding the NoSQL databases. Being true to its name, NoSQL systems are the absolute opposite of SQL’s relational lines when it comes to organizing their data sets. According to the textbook definition, NoSQL frameworks can successfully structure their information in any way, given that the method isn’t relational.

  • Microsoft Excel

Before you start giggling and questioning the existence of Microsoft Excel in this article, hear us out. We totally agree that Excel looks a little clunky when compared to other platforms. However, Microsoft’s beloved worksheet is used by a staggering 750 million people worldwide. It is one of the most sought skills if you are looking for job opportunities on websites like jobsbuster.

The automating features and commands introduced by Excel have substantially led to improved data analysis. Its programming language, VBA is used to create macros or pre-recorded commands. When used efficiently, VBA can act as a savior for human analysts by saving their time when it comes to doing repetitive jobs like project management, payroll, and accounting.

  • Linear Algebra and Calculus

One can’t master the world of data analytics without having advanced mathematical skills. A lot of data analysts decided to opt for a major in mathematics and statistics during their undergraduate years to get a strong grasp of the theory that dictates real-world analytical practice.

Two mathematical studies dominate the world of analytics, namely calculus and algebra. Linear algebra has its importance in the world of machine and deep learning, where it facilitates matrix, vector, and tensor operations. Calculus is likewise put to use to build the objective/cost/loss functions that educate algorithms and make them capable of achieving their objectives.

This was our walk in the park of technical skills needed to become a data analyst. Now in the last part of the article, we will discuss some soft skills that one needs to shine bright in the world of Data Analytics.

  • Critical Thinking

Looking at data will do no one any good, one needs to be able to decipher it and expand its usage beyond its sheer volume. With the help of critical thinking, you can decipher the data analytically, figuring out patterns and culling out actionable insights. Data analytics is best done when one is thinking along with processing.

  • Communication

What is the use of all the findings if one is incapable of communicating them to others? You can be the best analyst this country has seen, but if you can’t break down the patterns that you witness to those without technical expertise, it is safe to say that you have failed at the task at hand.


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The mark of a good analyst is the fact that they are bilingual. They must have the ability to address extremely technical points with people of their qualifications and, at the same time offer their findings in a simplified manner to all the business-centered decision-makers.

Data analytics is absolutely a stellar career for anyone who wishes to pursue it. However, to be good at what you do, you need to be able to put together the required skillset. In this ever-evolving landscape, you must commit yourself to continuous learning and staying abreast with the latest tools and technologies. By gathering both technical and soft skills, you can be a force to reckon with in this highly competitive market.

Comments (1)

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