How To Become A Data Analyst – Complete Roadmap

Q: How much does a Data Analyst earn?

Entry-level Data Analysts can earn an average of u20b92.5 Lakhs - u20b95 Lakhs annually, depending on location and skills.

8 min read 2,351 views 2 likes

Posted by Aarna Tiwari Dec 11, 2024

Data analytics is one of the fastest-growing career fields in today’s digital economy. With businesses across sectors relying on data-driven decisions, the demand for skilled data analysts has surged significantly.

If you are a college student or a fresher in India eager to embark on this career path, this guide will provide you with all the insights you need.

Table of Contents

Who is a Data Analyst?

A Data Analyst is a professional who collects, processes and analyzes data to extract meaningful insights that help organizations make informed decisions. They use statistical tools, data visualization techniques, and domain knowledge to interpret data trends, solve problems, and suggest actionable strategies.

The key responsibilities of a data analyst are:

Gathering and cleaning data from various sources.
Performing statistical analysis to identify trends.
Creating dashboards and visual reports using tools like Tableau or Power BI.
Collaborating with teams to implement data-driven solutions.

How To Become A Data Analyst: Step-by-Step Guide

Becoming a Data Analyst involves acquiring technical skills, domain knowledge, and practical experience. Here’s a step-by-step roadmap:

Understand the Job Role: Research and familiarize yourself with the expectations of a data analyst’s role. Understand the tools, techniques, and skills commonly required.
Educational Background: While a degree in fields like Computer Science, Statistics, Mathematics, or Engineering is often preferred, it is not mandatory. For those wondering how to become a data analyst without a degree, online certifications and boot camps can provide the necessary foundation.
Learn the Required Skills: To become a proficient Data Analyst, focus on these core skills such as learning programming languages such as Python and R for data manipulation and analysis, SQL to extract and manage data from databases, and data visualization. Building a solid understanding of probability, statistics, and linear algebra will also help in the long run.
Internship Experience: Practical experience is crucial for this role. Opt for a data analyst internship for freshers to work on real-world datasets. Internships also enhance your data analyst resume and build your network within the industry.
Build a Portfolio: Showcase your skills and projects in a portfolio. Include personal projects (e.g., analyzing public datasets like COVID-19 trends), internship experiences, and visualization dashboards created using tools.
Prepare for Interviews: When applying for jobs, prepare for Data Analyst interview questions. Mock interviews and problem-solving sessions can significantly boost your confidence.

Consider using Naukri Campus’s Pathfinder, a role-based skill readiness assessment, to evaluate your preparation and role readiness for becoming a Data Analyst.

Data Analyst Skills – Technical and Soft Skills

India’s rapidly evolving digital economy has created an unprecedented demand for data professionals. From startups to multinational corporations, organizations are increasingly relying on data-driven decision-making.

The essential technical skills required from a professional data analyst are:

Data Analyst Technical Skills

Python

Primary language for data analysis
Libraries: Pandas, NumPy, Matplotlib
Essential for data manipulation and visualization

R

Statistical computing and graphics
Strong in statistical modeling
Popular in research and academic settings

Data Visualization Tools

Tableau
Power BI
Matplotlib
Seaborn
Plotly

Database Management

SQL
MySQL
PostgreSQL
MongoDB

Soft Skills For Data Analysts

While technical skills form the backbone of a data analyst’s capabilities, soft skills transform good analysts into exceptional professionals.

Critical Thinking

Questioning assumptions
Identifying potential biases in data
Developing innovative approaches to problem-solving
Connecting seemingly unrelated data points

Communication Skills

Simplifying complex statistical findings
Creating compelling data visualizations
Presenting insights to non-technical stakeholders
Adapting communication style to different audiences

Emotional Intelligence

Read room dynamics during presentations
Understand stakeholder expectations
Manage potential resistance to data-driven changes
Build collaborative relationships across departments

Problem-Solving

Identifying the root cause of complex issues
Developing creative analytical approaches
Thinking holistically about business challenges
Proposing practical, implementable recommendations

Professional Certifications Necessary For Data Analysts

Having the below-mentioned professional data analyst certificates can help freshers in the long run.

Google Data Analytics Professional Certificate
IBM Data Analyst Professional Certificate
Microsoft Certified: Data Analyst Associate
SAS Certified Data Scientist

Data Analyst Salary

Data Analysts are well-compensated for their expertise. The average Data Analyst salary in India ranges from ₹3-6 LPA for freshers. With experience and specialization, mid-level professionals can earn up to ₹15 LPA, while senior analysts can command salaries exceeding ₹20 LPA.

Data Analyst Salary In India

Data analysis has emerged as one of the most lucrative and promising career paths in India’s digital economy. Understanding salary dynamics requires a multifaceted approach that considers various factors influencing compensation.

The data analyst salary in India is as:

Entry-Level Data Analyst (0-2 Years): ₹2.5 Lakhs – ₹5 Lakhs per annum
Mid-Level Data Analyst (2-4 Years): ₹5 Lakhs – ₹10 Lakhs per annum
Senior Data Analyst (4-7 Years): ₹10 Lakhs – ₹18 Lakhs per annum
Expert/Lead Data Analyst (7+ Years): ₹18 Lakhs – ₹30 Lakhs per annum

How to Land a Data Analyst Internship as a Fresher

Securing a data analyst internship as a fresher can be your first step into the world of data analytics. Here’s an overview guide:

Understand the Role: Assist in data cleaning, analysis, and creating reports/dashboards.
Develop Skills: Learn Excel, SQL, Python/R, and visualization tools like Tableau/Power BI.
Create a Strong Resume: Highlight coursework, projects, certifications, and technical skills.
Work on Projects: Use datasets from Kaggle or similar platforms to build practical experience.
Leverage Networking: Connect on LinkedIn, join data science communities, and seek referrals.
Apply Strategically: Use portals like Naukri and LinkedIn, for internships.
Prepare for Interviews: Focus on SQL, EDA, visualization, and statistics basics.
Upskill Constantly: Enroll in online courses like Google Data Analytics or Tableau training.
Highlight Soft Skills: Showcase communication skills, problem-solving, and teamwork abilities.
Stay Consistent: Be persistent, tailor applications, and learn from each attempt.

Use tools like the Naukri Campus Free Resume Maker to craft an ATS-friendly resume for your internship applications.

How to Become a Data Analyst Without a Degree

Even without a formal degree, you can break into this field by:

Enrolling in Certification Courses: Online platforms offer affordable programs in data analysis.
Building Projects: Gain hands-on experience by working on publicly available datasets.
Networking: Join data analyst communities on LinkedIn or attend webinars to connect with professionals.

How to Become a Data Analyst Without Experience

Breaking into data analysis without prior professional experience might seem daunting, but it’s entirely achievable with the right strategy, dedication, and approach.

Start Small: Take on freelance projects or contribute to open-source initiatives.
Showcase Transferable Skills: Highlight transferable skills like problem-solving and critical thinking from previous roles.
Leverage Internships: A data analyst internship for freshers is the best way to gain entry-level experience.

Data Analyst Interview Questions

Here are 10 common Data Analyst interview questions with answers to help you prepare effectively:

What are the key responsibilities of a Data Analyst?

A Data Analyst collects, processes, and performs statistical analyses on data to generate actionable insights. Key responsibilities include:

Extracting data using SQL or other tools.
Cleaning and preparing data for analysis.
Creating dashboards and reports.
Identifying trends and patterns.
Presenting insights to stakeholders for decision-making.

Can you explain the difference between Data Analysis and Data Analytics?

Data Analysis focuses on examining data to find insights or trends and is more about interpretation. On the other hand, data Analytics involves using tools, techniques, and algorithms to forecast future outcomes or support decision-making processes.

How do you handle missing data in a dataset?

Handling missing data depends on the context and the dataset:

Deletion: Remove rows or columns with missing values if the dataset is large and the missing data is negligible.
Imputation: Replace missing values using methods like mean, median, mode, or predictive models.
Flagging: Create a separate column to indicate missingness for analysis.

What is the role of SQL in Data Analysis?

SQL is critical for:

Querying large databases to extract relevant data.
Performing aggregations, joins, and filtering operations.
Data cleaning and transformation for further analysis.

What are some common data visualization tools you have used?

Common tools include:

Tableau and Power BI for interactive dashboards.
Excel for quick and simple visualizations.
Matplotlib, Seaborn, or Plotly for Python-based custom visualizations.

What is normalization in a database, and why is it important?

Normalization is the process of organizing a database to minimize redundancy and dependency.

Benefits: Reduces data duplication, ensures data integrity, and optimizes query performance.
Example: Dividing a single table with redundant data into multiple related tables.

Explain a time you encountered outliers in your dataset and how you handled them.

Outliers were identified using visualization tools like boxplots or statistical methods such as Z-scores. I handled them by:

Removing them if they were errors or irrelevant.
Transforming them using methods like log transformation.
Retaining them if they carried meaningful information.

What is the difference between supervised and unsupervised learning?

Supervised Learning: Works with labeled data to predict outcomes (e.g., regression, classification).
Unsupervised Learning: Works with unlabeled data to find hidden patterns or groupings (e.g., clustering, dimensionality reduction).

What is the significance of A/B testing in Data Analysis?

A/B testing helps compare two variants to determine which performs better.

Use case: Measuring the impact of a new website design on user engagement.
Steps: Split traffic into groups (control and test), apply changes to the test group, and analyze metrics for significant differences.

Can you explain your approach to creating a predictive model?

Understand the problem and define the objective.
Collect and clean data.
Feature selection to identify key variables.
Choose a model (e.g., regression, decision tree).
Train and test the model using techniques like cross-validation.
Evaluate performance using metrics such as RMSE, precision, or recall.

Pursuing a career as a Data Analyst is both challenging and rewarding. With the right skills, a strategic approach, and the guidance of tools like Naukri Campus Pathfinder, you can excel in this domain.