Step By Step Plan To Help You Get You Ready For A Data Analyst Job

Before delving into the blog let’s understand it through an analogy. Imagine sitting on your study table and finding all your stuff kept all over the place in an unorganized manner.

Getting productive work done in such an environment can be very difficult. What is even more difficult is finding something from this mess in an urgent situation. Data is very similar to the above given example. 

Getting productive information from data can be equally cumbersome if it is not organized to make sense. Data analysts are the people who are responsible for arranging this data so that it becomes easier for us to understand and comprehend. 

Becoming a data analyst is not a piece of cake. There is a particular skill set that one must possess to become a data analyst. If you plan on becoming one, then here is a step-by-step plan that will help you get ready for the job. 

Step By Step Plan To Help You Get You Ready For A Data Analyst Job

Know the Precursors 

  • What does a Data Analyst exactly do?
  • What is the Process in Data Analysis?
  • What are the responsibilities that fall on a Data Analyst?
  • What is the difference between a Data Analyst and a Data Scientist?


If you can answer the above-listed questions without a hint of a doubt, then you already know what you are heading into. But if you are not, then you might want to do more research on a data analyst

Also Read: Understanding Data And Why It Is Important If You Wish To Become A Product Manager

Familiarise Yourself with the Basics 

One thing that is common for anyone who is applying for a job, or even for those who already have a job is knowing your basics. This is like the foundation of the platform that you are building for yourself.  

Suppose solving a problem using high technology gadgets that also require more cost can be done using more straightforward techniques that you learned as basics. In that case, the latter is always preferred and valued by the higher authorities. 

Improve Your Skills 

As mentioned above, a particular skill set is required for a data analyst to possess. A few of those skills are listed below:


As a data analyst, you will more often than not face data that mainly consists of numbers and figures in front of you. You might have to solve equations, plot graphs, and much more. That is why your Math has to be very strong. 


This is the most important skill required. It is an integral skill that sets you apart from business analysts. The more programming languages you know, the better, but make sure you know at least one of them. Python, MATLAB, C++ are some of the common programming languages used.  


You must also have sound knowledge in spreadsheets like Microsoft Excel, through which you can enter your data and form graphs to get meaningful information and results from it.  


‘Probability testing’ ‘Regression Analysis’ are some of the concepts based on which you will encounter tasks in your job. This comes under the ‘Statistics’ category. This skill will help you to derive accurate conclusions by efficiently interpreting the data. 

Machine Learning (ML)

Concepts like ‘Multivariable Calculus’ and ‘Linear Algebra’ that is part of Math, as well as concepts from Statistics, make the foundation for machine learning.

This will help you make predictions and suggestions based on a huge amount of data given to you. 

Enroll in a Certification Course 

As a data analyst, you must always learn more stuff and skills related to your field to continue to have the edge over the rest of the people.

With the number of people out there considering to apply for a job as a data analyst, being one step higher than them becomes of utmost importance. 

This is where a data analyst certification can be beneficial for you. Many certified courses are offered by top companies and firms that you can consider taking up. 

Gain Experience 

This brings us to the famous paradox – Get a job to gain experience and gain experience to get a job. One thing that must be understood over here is that experience is not gained through a job alone. You can even gain experience through the skills that you possess. 

  • Do projects using your skills that are related to the data analytics field
  • Apply for internships with companies to get some hands-on experience

These are just a few ways to gain experience and get the edge over your competitors. 


Getting to know people who share a similar mindset and surrounding yourself with them can be very helpful.

 When you do your projects or internships with companies, talk to the people who work there and make sure to stay in touch with them even after you are done with your internship. 

Ask them questions and pick on their brain. This will help you learn more from them and might even open up a possibility for a job prospect in the future.  

A LinkedIn profile can also help you in contacting like-minded people. This can be useful for people who haven’t done any internships or projects with any company. 

To all the introverts reading this, we understand that this step might even sound harder than the entire process of becoming a data analyst in itself.

Still, networking is one step you must not omit as it will help you throughout your career. 

Prepare Your Resume 

Your resume is one of the essential documents that you must carry with you when you apply for a data analyst job. Make sure your resume is up to date and contains all the necessary information like:

  • Educational Qualifications
  • Job Experience
  • Internships
  • Projects
  • Skills

Also Read: How Big Data Plays a Vital Role in Major Business Sectors?


Recent market research by Market Sports World has shown that the Data Analytics Market will grow at a CAGR (Compound Annual Growth Rate) of 30.08% by reaching USD 77.64 billion by the year 2023. This would mean better, rewarding job prospects for you, an aspiring data analyst.  

Now that you have done all your research in the ‘How’ part, it is time to follow the steps mentioned above so that you can become a great data analyst and make significant contributions to the field of data analytics.    

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