In this world data is increasing day by day. Due to this increasing data it’s important to manage, analyze, and maintain data effectively. That’s why data analytics comes into focus. With data analytics, companies can find useful information, make better decisions, and stay ahead of others. Every company wants a person who studies data and helps in making smart decisions. In this blog we talk about some important data analytics entry level jobs. Also by reading this blog we will get a strong understanding on data analytics.
If we are looking to make a career in this rapidly growing field. Technogeeks is a top institute for data analytics training in Pune (Aundh). Technogeeks data analytics course is made by industry experts who are working in this field with 7 – 8 years of experience. In this course we start from basic to advanced level. At Technogeeks we will get practical training, hands-on projects, and real case studies. Also help in mock interviews, resume building, interview calls. This course is beneficial for freshers as well as working professionals.
Enroll now and take the first step toward a successful career. Click here to join our courses today!
Table of Contents
ToggleWhat is Data Analytics in entry level?
In the data analytics process, we collect a large amount of data and clean it. Then, we study that data. By studying the data, we find useful patterns or important points. After that, we present the data using reports, charts, and dashboards. The important data that we discover is used by companies to make better decisions for their business. In today’s world, data analytics is very important because every company depends on data.
Steps to Begin Your Data Analyst entry level journey
1. Know Why Data Analytics is a Good Career
The main thing we should know is why data analytics is important. In today’s world, every company has a lot of data, and they want people to study that data. This career is in high demand and is expanding day by day. Many industries like IT, finance, healthcare, marketing, and e-commerce need data analysts. This is due to good job opportunities and a growing salary.
2. Learn the Main Skills
Once we know it’s a good career, we should start learning skills. First we understand how to collect data and analyse it. . Next, learn programming – Python and R are easy and great to start with. For databases, we have to learn SQL. Excel is also important to learn. For cleaning, studying, and presenting data, these skills are important.
3. Get the Right Course or Certification
Some people start learning on their own, but when we join a proper course, we learn things in a proper and step-by-step way. We can also do online courses and certifications, which look good on our resume. When we select any course, it should provide projects and real-life examples.
4. Practice and Do Projects
Theory is not enough for cracking a job interview. We need to practice correctly. We can get free datasets from Kaggle, GitHub, or UCI. We can do small projects like sales reports, customer behavior studies, or healthcare analysis. If we do more practice, we will become more confident.
5. Make a Portfolio and Apply for Jobs
After learning and practicing things from basic to advanced, we have to show our work. We can create our portfolio, in which we can show our projects, dashboards, and reports. If our portfolio looks good, then we will definitely get calls from companies for a job or internship.
Top 10 Data Analytics Entry Level Jobs in 2025
1. Data Analyst Intern
This role is the first step in a data analytics career. Basically, they do tasks like data cleaning, simple reports, and charts to learn real work as they are new.
2. Junior Data Analyst
These people work on small data queries, Excel sheets, dashboards. and giving reports to their team.
3. Entry-Level Operations Analyst
Entry-Level Operations Analysts study business operations and daily work data, find problems, and give suggestions so the company can improve their processes.
4. Junior Quantitative Analyst
This person uses math, statistics, and Excel or Python to study financial data, risk, and numbers. People who like numbers and finance can choose this.
5. Entry-Level Healthcare Data Analyst
In this role, we study hospital and patient data so healthcare services can get better. We work on reports, charts, and medical records.
6. Junior Financial Analyst
These work with money reports, budgets, and planning. We use Excel, SQL, and various tools to visualize financial data in charts.
7. Junior Business Intelligence Analyst
We use tools like Tableau, Power BI, or Looker. and make dashboards and charts. by which we can show company data in a way that helps managers take action.
8. Entry-Level Manufacturing Analyst
To increase speed, quality, and reduce costs. We study factory and production data. So we check machine data and reports.
9. Junior Marketing Analyst
customer behavior, online ads, and sales data these concepts are studied. by which we understand marketing campaigns work best. For this we use Google Analytics.
10. Junior Operations Analyst
Basically these people analyze internal processes and regular tasks. and give simple reports.
These are some data analytics entry level jobs.
Data Analytics Entry Level Jobs in 2025 Salary Reference (For reference only based on multiple sites review)
1. Data Analyst Intern
Fresher: ₹3.5 to 4.5 LPA
2. Junior Data Analyst
Fresher: ₹4.5 to 6 LPA
3. Entry-Level Operations Analyst
Fresher: ₹5 to 6.5 LPA
4. Junior Quantitative Analyst
Fresher: ₹6 to 8 LPA
5. Entry-Level Healthcare Data Analyst
Fresher: ₹5 to 7 LPA
6. Junior Financial Analyst
Fresher: ₹5.5 to 8 LPA
Data Analytics Syllabus & Tech Stack [2025 Updated]
1. Basics of Data Analytics
First, we should know what data analytics means and why it is important. It helps companies study information and make smart decisions. There are four main types:
- Descriptive Analytics – We see what happened in the past.
- Diagnostic Analytics – We find why it happened.
- Predictive Analytics – We try to predict future outcomes.
- Prescriptive Analytics – We give suggestions on what to do next.
2. Programming Languages for Data Analytics
Some of the learning most common languages are:
- Python – Easy to learn and very popular.
- SQL – Helps us work with databases and get data.
3. Data Cleaning and Preparation in Data Analyst Entry level skillset
Data is not always ready to use. We need to clean it. This means:
- Removing missing numbers, mistakes, and copies.
- Arranging and formatting the data properly.
Some of the tools we can use: Excel, Python (Pandas), Power BI
4. Data Visualization
We should show data in an easy way so others can understand. We make charts, graphs, and dashboards.
Tools include:
- Excel, Tableau, Power BI, Google Data Studio
- Python libraries: Matplotlib, Seaborn, Plotly, Bokeh
- Other tools: Looker, Qlik Sense, Domo
5. Statistics and Mathematics
We need some basic math and stats to study data:
- Probability (chance), distributions, hypothesis testing, regression (finding patterns), correlation (finding relationships).
- Some topics in algebra, calculus, and time series (data over time).
6. Databases and Data Management
Data is stored in databases. We must know how to work with them:
- SQL Databases: MySQL, PostgreSQL, Oracle, Microsoft SQL Server
- NoSQL Databases: MongoDB, Cassandra, Firebase
- Data Warehouses: Snowflake, Amazon Redshift, Google BigQuery
- ETL Tools: Talend, Apache Nifi, Informatica (used for transferring and cleaning data)
7. Basics of Machine Learning
Machine learning helps computers learn from data. We should know:
- Supervised Learning: Regression (predict numbers), Classification (predict groups).
- Unsupervised Learning: Clustering (group data), Dimensionality Reduction (make data simpler).
Libraries: Scikit-learn, TensorFlow, Keras, PyTorch
8. Big Data Tools
When data is very big, we use special tools:
- Hadoop, Spark, Hive, Pig for handling big data.
- Kafka for live streaming data.
- Databricks and Snowflake for big data analytics.
9. Cloud Platforms for Analytics
Many companies use cloud platforms. We can learn:
- AWS (Amazon Web Services): S3, Redshift, Glue
- Google Cloud Platform (GCP): BigQuery, Dataflow
- Microsoft Azure: Synapse, Azure Data Lake
10. Projects and Case Studies
We must practice on real problems to become better. We can:
- Use free datasets from Kaggle, GitHub, UCI Machine Learning Repository.
- Work on problems like sales analysis, customer trends, healthcare data.
11. Tech Stack Summary
- Programming: Python, R, SQL, SAS
- Visualization: Tableau, Power BI, Excel, Looker, Qlik Sense
- Databases: MySQL, PostgreSQL, Oracle, MongoDB, Cassandra
- Big Data: Hadoop, Spark, Kafka, Hive
- Cloud: AWS, GCP, Azure, Snowflake, Databricks
- Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, Matplotlib, Seaborn, Plotly
- Other Tools: Git/GitHub (to save code), Jupyter Notebook, Google Colab
Frequently Asked Questions (FAQs)
Question 1. What is the typical pay for a data analyst in India in 2025?
Answer :- A new data analyst in India can earn around ₹4–6 lakh per year. With experience, they can earn ₹8–15 lakh or even more.
Question 2. What skills do you need for a data analytics job?
Answer :- You should know Python, R, SQL, Excel, Tableau, and statistics. You also need to be good at problem-solving and thinking with data.
Question 3. Which tools are best for data analytics?
Answer :- The most common tools are Tableau, Power BI, Excel, and Python libraries like Pandas and NumPy. These help in data cleaning, analysis, and making charts.
Question 4. If someone is new so he can build a career in data analytics?
Answer :- Yes, freshers can start as interns or junior analysts. If you learn the basics and work on projects or courses, you can get a job.
Question 5. What is the best way to start a career in data analytics?
Answer :- First, learn the basics of analytics. Then do certifications, projects, and build a portfolio to show your work. Apply for internships or entry-level jobs to gain experience.
Question 6. How can I prepare well for a data analytics interview?
Answer :-
- Revise statistics, data cleaning, and visualization.
- Practice explaining ideas in a simple way.
- Work on real projects to show your skills.
- Be ready to share examples of how you used data to solve problems.
Final Words
Data Analytics Entry Level Jobs in 2025 increasing in multifold, and every company wants skilled people who can study data and help make smart business decisions. Starting early gives us a big advantage.
We need to learn Excel, SQL, Python, Power BI, statistics, and how to visualize data . In this blog we discussed lots about data analytics entry level jobs.
Many of the companies also looking for Data Analytics Entry Level Jobs in 2025 with AI Skilled professionals.
Joining a data analytics course, taking online certifications, or doing an internship will help us gain real experience. The most important thing is to keep learning and applying our skills. If we do this, we can easily get a good data analyst job and grow in this exciting career.
Want to start a career in Data Analytic call us at +91 8600998107 / +91 7028710777 For more details.