How To Get a Data Analyst Job As A Fresher In India

How To Get a Data Analyst Job As A Fresher In India

Are you wondering how to become a data analyst with no experience? You’re not alone. Securing a data analyst position as a fresher in India can be challenging, but with the right approach and strategies, you can increase your chances of success. 

According to recent statistics, the demand for data analysts in India is expected to grow by 35% in the next 5 years. This is more than the national average job growth and will bring numerous opportunities if you wish to build a career in data analytics. 

In this guide, we’ll explore actionable steps and valuable tips to help you get a data analyst job as a fresher in India.

What is a data analyst? 

A data analyst plays an important role in deciphering data to help businesses make informed decisions. 

They identify patterns, evaluate the data, and identify trends using statistical and analytical methods to inform corporate choices and suggest adjustments to operations. Their study assists the business in enhancing productivity, understanding customer needs, and boosting earnings. 

The reports provided by data analysts are distributed to the management and the leaders for planning and decision-making. Data analysts contribute to the organization’s overall success by enabling them to transform complications into well-grounded strategies and feasible solutions.

Key skills required for data analysts

I) Data Analytics Skills: Technical

1) Data Visualization

Data visualization is the ability to present data findings using graphics or illustrations. With data visualization, data analysts can assist decision-makers in spotting patterns and grasping complex ideas quickly, even if they lack advanced analytical skills. 

Additionally, getting familiar with tools like Tableau and Power BI is essential aa they are highly used tools for data visualization.

A recent LinkedIn Learning study found that new graduates prioritize hard skills upon entering the workforce, especially in data analysis and storytelling with data insights, with data visualization being the top skill they aim to acquire.

2) Data Cleaning

Data cleaning is an essential aspect of data analytics skills. It’s a crucial step in building effective machine learning models, often occupying a significant portion of a data analyst’s workload. 

Tech writer Ajay Sarangam highlights that despite the common perception of data scientists focusing on ML algorithms, most spend approximately 80% of their time cleaning data. Why? Because in the realm of machine learning, superior data quality trumps sophisticated algorithms every time. 

Possessing adept data cleaning skills is a prerequisite for data analyst roles, as a properly cleaned dataset empowers even basic algorithms to unveil significant insights, while uncleaned data may lead to erroneous patterns and misguided business decisions.


MATLAB is a powerful programming language and numerical computing environment used for algorithm implementation, data manipulation, and visualization. It’s essential for data analysts because it expedites data pre-processing, cleaning, and visualization, particularly for big data tasks. 

Proficiency with MATLAB is not compulsorily required, but it does improve a data analyst’s skill set and increases their marketability to companies.

4) R

R analytics or R programming language is widely used as it has features specifically designed for analytics. As per the poll conducted by IEEE Spectrum, R ranked 5th amongst the top ten most used programming languages in 2019. 

Businesses find it appealing because of its capacity to manage intricate datasets and substantial amounts of data. R is a popular, useful, and relevant language that every aspiring data analyst should learn.

5) Python

Python is important for data analysis, with libraries like Pandas and NumPy. It ranked first in IEEE’s 2019 survey for its many AI-related libraries. 

Python serves as the server-side programming language for 1.4% of all websites on the internet! Furthermore, given AI’s growth, mastering Python is crucial to making a successful career as a data analyst.

6) SQL and NoSQL

SQL, or Structured Query Language, is fundamental for aspiring data analysts. Companies across various industries continue to rely on SQL due to its effectiveness and widespread use.

While mastering general-purpose languages like Python or R is essential, neglecting SQL can pose challenges in securing data-related roles. Branded versions such as MySQL provide valuable insights into relational database management systems, enhancing job prospects for aspiring data analysts.

Additionally, proficiency in NoSQL databases is advantageous. Unlike SQL, NoSQL systems offer flexibility in structuring data sets without adhering to relational formats. Popular frameworks like MongoDB adopt hierarchical structures, providing hands-on experience in managing diverse data structures. 

7) Machine learning

As compared to data cleaning or programming, machine learning might not seem important but it’s knowledge can give you a competitive edge when applying for data analytics jobs.

Artificial intelligence (AI) and predictive analysis have become more important in recent years because they help increase productivity by automating important tasks, cutting costs, saving time, and eventually promoting overall growth. Infact Statista reports that 82% of organizations are looking for candidates with machine learning skills, but only 12% say there are enough people with these skills.

8) Statistical Analysis Skills

The foundation of data analytics is statistical analysis, which offers the procedures for concluding data. Knowing statistical techniques enables you to apply the following:

    • Descriptive data: Condense and analyze data to give a concise summary of what it reveals.
    • Statistical inference: Based on a sample, draw conclusions and forecasts about the population.
    • Testing of hypotheses: Assess theories or hypotheses to address real-world issues.

9) Linear Algebra and Calculus

Linear algebra and calculus are essential for data analysts as they underpin real-world analytical practices. Linear algebra supports operations in machine and deep learning, while calculus is crucial for constructing algorithms’ objective functions. 

Practical coding skills and hands-on experience with tools like the PyData stack are valuable, allowing individuals to understand algorithmic workings and the relevance of mathematical theory in practical applications. 

10) Microsoft Excel

Excel is crucial for data analysts because it is widely used in businesses worldwide and frequently appears as a required skill in job postings. 

Despite its perceived limitations, Excel offers automation features through its programming language, VBA, which helps save time on repetitive tasks like accounting and project management. Additionally, Excel’s Analysis ToolPak provides statistical modeling and data analysis capabilities, enhancing data analytics skills.

11) Big data technologies

Big data technologies are essential skills for aspiring data analysts due to their growing significance in the field. Here’s why:

  • Market growth: As per Statista, the big data market will reach $102 billion by 2027, leading to high demand for skills in big data technology. 
  • Handling large datasets: Big data technologies like Hadoop and Spark enable data analysts to manage and process vast amounts of data that traditional databases cannot handle.
  • Complex computations: These technologies empower data analysts to perform intricate computations and analyses on big data, extracting valuable insights.
  • Real-time analytics: With big data technologies, analysts can analyze data in real-time, allowing for immediate decision-making based on current information.

II) Data Analytics Skills: Soft Skills

1) Communication skills 

Data analysts are required to translate complex data into easily understandable insights for non-technical stakeholders. It involves: 

    • Data storytelling: Data analysts should be able to weave data into narratives that is more relatable and easy to understand for stakeholders. 
    • Presentation skills: You should master the ability to present data visually through clear yet appealing presentations that showcase key findings. 
    • Interpersonal skills: Data analysts need strong interpersonal skills to effectively communicate with colleagues, understand their perspectives, and collaborate effectively on projects.

2) Problem-Solving Skills

Problem-solving skills are paramount for aspiring data analysts, given the nature of the challenges they encounter in their roles. 

    • Critical thinking: Data analysts should be able to objectively analyze and evaluate complex data and draw meaningful insights for decision-making.
    • Analytical reasoning: Data analysts rely on analytical reasoning to interpret data, identify trends, and find effective solutions to business challenges.
    • Innovation: As a data analyst, you will come across situations that need innovative thinking and problem-solving skills. You need to be able to think outside of the box and find solutions that may be different from traditional methods.

How to make a resume for a data analyst fresher with no experience

I) What to include in a resume for a data analyst fresher in India?

    • Proficiency in data analysis tools: Recruiters seek familiarity with tools like SQL, Python, R, and Excel, crucial for cleaning, processing, and interpreting data in this role.
    • Statistical and mathematical skills: Knowledge of statistics and mathematics is vital for confidently interpreting and manipulating data, something recruiters prioritize.
    • Problem-solving skills: Analysts often face issues requiring innovative solutions; showcasing problem-solving skills convinces recruiters of your ability to handle similar situations.
    • Data visualization skills: The ability to translate complex results into understandable visual representations aids in better decision-making and is a sought-after skill.
    • Degree in relevant field: Having a degree in fields like statistics, mathematics, or computer science shows recruiters your formal education in dealing with complex data, offering a competitive advantage.

II) Hard Skills you should include your entry-level data analyst resume 

    • SQL (Structured Query Language)
    • Python
    • R Programming
    • SAS (Statistical Analysis System)
    • Tableau
    • Power BI
    • Java
    • Hadoop
    • Apache Spark
    • Data Visualization
    • GIS (Geographic Information Systems)
    • MATLAB
    • Data Cleaning
    • SPSS (Statistical Package for the Social Sciences)
    • Big Data Analysis
    • Database Management
    • Data Modeling
    • Machine Learning
    • Predictive Modeling
    • Excel
    • Advanced Mathematics

III) Soft skills to include in a resume for data analyst fresher

    • Analytical Thinking
    • Communication Skills
    • Problem-Solving Abilities
    • Critical Thinking
    • Attention to Detail
    • Organization Skills
    • Collaboration
    • Fast Learner
    • Flexibility
    • Time Management
    • Decision-Making
    • Creativity
    • Active Listening
    • Patience
    • Ethical Judgement
    • Positive Attitude
    • Perseverance
    • Team Leadership
    • Conflict Resolution

IV)Tips for crafting a good resume for a data analyst fresher

Below are a few tips you can follow to craft a resume for a data analyst fresher with no experience: 

1) Highlight relevant coursework and projects

Focus on academic projects or internships involving data analysis tasks. Provide specific examples of how you applied analytical techniques to solve problems or uncover insights.

2) Demonstrate proficiency in programming languages

List programming languages like Python, R, or SQL in a “Technical Skills” section. Include other relevant tools like Tableau or Power BI. Provide examples of projects where you utilized these languages to analyze and visualize data effectively.

3) University projects

Use data analyst projects to showcase skills like creating predictive models, especially if you lack extensive work experience.

4) Strong action verbs

Ensure to include action words such as ‘Analyzed’, ‘Researched’, and ‘Assessed’ to showcase your role in the project.

5) Highlight your impact with the help of results

When adding your experience and projects, try to quantify your individual impact with the help of results and metrics. This enables the Hiring Manager to understand your contribution to the particular project. 

For example, compare these two statements:

Eg. 1: Examined sales data to uncover trends and insights

Eg. 2: Utilized SQL and Python to analyze sales data, leading to a 10% increase in revenue and a 20% decrease in customer churn

6) Modify your resume according to the job description

Tailoring your resume to the job description is crucial to impress hiring managers. Here’s how:

    1. Carefully analyze the job posting to identify key skills and requirements.
    2. Highlight your relevant skills and experience on your resume that align with the job description.
    3. Incorporate language and keywords from the job posting into your resume where applicable.

For instance, if the job emphasizes SQL proficiency and Tableau experience, ensure your resume showcases specific SQL and Tableau projects you’ve worked on. This customization demonstrates your suitability for the role and increases your chances of landing the job.

V) How to list academic details on a resume for a data analyst fresher?

It’s crucial to highlight your academic accomplishments and certificates when mentioning your education on your CV to demonstrate your hard work and commitment to lifelong learning. This is how you do it: 

1) For each degree:

    • Make sure you share the name of the university, its location, the dates attended, and the title of your degree.
    • You may choose to include your GPA, but it’s optional unless the job description specifies a minimum GPA requirement.
    • Only include your major and minor if they are relevant to the open position.

2) Certifications

Certifications are also essential for data analysts, as they demonstrate your proficiency in specific tools and software. 

    • Include the name of the certification, the certifying institution, and the year obtained.
    • Make sure your certifications are current and unexpired.
    • Limit your certifications to those directly related to the required skills mentioned in the job description. 

For example, certifications in programming languages, database platforms, big data analysis, and data analysis tools are highly relevant.

VI) How to write a resume summary or objective for a data analyst fresher? 

When crafting a resume summary or objective for a data analyst fresher, keep these tips in mind:

    • Customize for the Job: Tailor your statement to the specific job you’re applying for, highlighting relevant skills and experiences.
    • State Your Goal: Clearly express the type of position you are seeking as a data analyst.
    • Highlight Transferable Skills: Emphasize any transferable or soft skills you possess that are applicable to database development.
    • Show Motivation: Showcase your eagerness to learn and gain experience in the field of data analysis.
    • Use Action Verbs: Start your sentences with action verbs to make your summary dynamic and impactful.
    • Incorporate Keywords: Include keywords directly from the job description to align with the employer’s requirements.
    • Keep it Concise: Keep your summary or objective brief, aiming for 3-5 sentences to maintain clarity and focus.

Remember, focus on the skills and knowledge you already possess and how you will utilize them to achieve your goals as a data analyst, rather than solely focusing on what you hope to gain from the experience. 

How to build a data analyst portfolio?

Building a portfolio as a data analyst doesn’t have to be complicated or expensive. Here’s how you can do it using free platforms:

  1. LinkedIn: Utilize LinkedIn to create an online portfolio by adding, updating, and removing projects from your profile. You can upload various types of content, including documents, presentations, and images. Projects can be showcased under different sections like Featured, Experience, or Education.
  2. GitHub: GitHub is a popular platform among developers where you can host your portfolio for free. Create an account and start adding your data projects to a public repository. This allows you to showcase your code and Jupyter Notebooks, making your work accessible to potential employers and collaborators.
  3. Kaggle: Kaggle offers a customizable Jupyter Notebooks cloud environment that can double as a portfolio for your data analysis work. You can display the results of data science competitions you participate in, as well as showcase any datasets you’ve created or code you’ve written. Kaggle provides a community of data enthusiasts and professionals, offering valuable networking opportunities.

How much do data analysts make? 

In India, the typical salary of a data analyst in India is from ₹3,00,000 to ₹6,00,000 annually. The salary range often varies based on the organization you choose to work with and your level of experience. 

On average, data analysts earn around ₹32,042 per month to ₹50,801 per month. Starting as data analytics interns, individuals typically earn an average of ₹14,661 monthly. With increasing experience, senior data analysts can earn up to an average of ₹8,23,097 annually.

Data Analyst’s Salary Based on Locations

The city in which you work will also have an impact on your data analyst pay in India. The demand for the position and the cost of living in the city will have an impact. Below is the table showing some of the top Indian cities with the highest average salaries for data analysts: 




INR 6,50,000


INR 6,60,000


INR 6,00,000


INR 5,00,000


INR 6,00,000


INR 5,00,000

Source: Coursera

Data Analyst Salary Based on Different Job Titles

Job Title

Average Salary

Business Analyst


Database Administrator


Business Intelligence Analyst




Data Engineer


Data Scientist


Data Architect


Analytics Manager


Machine Learning Engineer


Source: Coursera

Common Interview Questions for Data Analysts Fresher

  1. Highlight the distinctions between Data Mining and Data Profiling.
  2. Define ‘Data Wrangling’ in the context of Data Analytics.
  3. Outline the various stages involved in an analytics project.
  4. Identify common challenges encountered by data analysts during the analysis process.
  5. Enumerate the technical tools utilized for analysis and presentation in data analytics.
  6. Discuss effective methods for data cleaning.
  7. Elaborate on the importance of Exploratory Data Analysis (EDA).
  8. Define descriptive, predictive, and prescriptive analytics.
  9. Describe the different types of sampling techniques employed by data analysts.
  10. Clarify the concept of Normal Distribution.
  11. Explain Time Series analysis.
  12. Distinguish between Overfitting and Underfitting.
  13. Discuss strategies for handling outliers in a dataset.
  14. Explore various types of Hypothesis testing.
  15. Define Type I and Type II errors in Statistics.

How to stay updated on industry trends for data analysts?

Resources to stay updated with industry trends


    • Data Science Central: It provides complete resources and expert insights spanning data science, big data, analytics, technology, and job opportunities.
    • What’s The Big Data?: This blog features insightful commentary by Gil Press, a seasoned expert in data research. He delves into the intersection of big data with technology, business, government, and shares analysis in the data sphere.
    • No Free Hunch: “No Free Hunch” is Kaggle’s official blog, delving into the intriguing world of data science competitions hosted on the platform. They have over 200 challenges, including high-profile ones like refining Microsoft Kinect gesture recognition and predicting hospital visits. The blog offers insights from competition winners, along with news and tutorials catering to data science enthusiasts of all levels.

Best Online Course for Data Analytics

    • Excelr: Excelr is a renowned institute in Mumbai that provides training for data analytics. Their courses are available online as well as offline. 
    • UpGrad: UpGrad is recognized as India’s leading program offering mastery in 14+ tools and languages and a WES-recognized degree akin to Canada’s 1-year PG Diploma.
    • Intellipaat: Intellipaat offers a 7 months course where you can master essential tools like Python, SQL, Machine Learning, PowerBI, Generative AI, prompt engineering, and ChatGPT through real-world case studies and projects.


We hope our guide on ‘How to become a data analyst with no experience’ helped you to get a better understanding of how to break into the field of data analysis as a fresher in India. 

Individuals can enter the data analyst field without prior experience by utilizing educational resources, obtaining relevant certifications, and demonstrating transferable skills. Additionally, networking with industry professionals and gaining practical experience through internships or personal projects is also crucial. 

Dedication, persistence, and a commitment to continuous learning are key to building a successful career in this dynamic and expanding field.

Furthermore, we suggest you explore 10xhire to get your first data analyst job. The platform offers thousands of job listings for freshers with a focus on work-from-home jobs. 

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