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Data Analysis Course

Data Analysis Course - Build Practical Skills with Modern Data Tools

Build practical data analysis skills through an intensive 8-month Data Analysis Course covering R, Python, NumPy, MySQL, Power BI, Presto, MongoDB, KNIME, DSA, and Predictive Modeling. The program is designed for learners who want to understand how data is collected, processed, analysed, visualised, and used for meaningful insights across real-world business and technical environments.
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Course Overview

What Is a Data Analysis Course and Why Is It Important Today?

Data is at the centre of modern businesses, and organisations need professionals who can work with data, identify patterns, create meaningful reports, and turn information into useful insights. Learning the right combination of programming, databases, analytics, and visualisation tools can create a strong foundation for a data-focused career. This Data Analysis Course brings together multiple technologies and analytical concepts in one structured program. From Python, R, MySQL, and MongoDB to Power BI, Presto, KNIME, DSA, NumPy, and Predictive Modeling, learners get exposure to a broad data-analysis toolkit.

Programming for Data Analysis

Develop practical exposure to Python and R for working with data and analytical tasks.

Database & Data Handling Skills

Learn how data can be stored, queried, and managed using MySQL and MongoDB.

Data Visualisation

Develop practical understanding of Power BI for transforming data into meaningful visual reports and dashboards.

Analytical & Predictive Thinking

Explore DSA, KNIME, Presto, and Predictive Modeling to understand different approaches to working with and analysing data.

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Ideal For

Who Should Take This Data Analysis Course?

This program is suitable for learners who want to develop a broader understanding of data analysis tools and build practical skills across programming, databases, visualisation, and predictive analytics.

Students

Build practical retail and business skills before starting a career in the retail industry.

Freshers

Develop useful knowledge for entry-level roles in stores, sales, customer service, and retail operations.

Sales Professionals

Improve selling, customer handling, product knowledge, and retail communication skills.

Store Staff

Understand store operations, inventory, merchandising, customer service, and daily retail activities.

Business Owners

Learn how customer experience, product presentation, pricing, and operations affect retail performance.

Career Switchers

Move towards retail, sales, merchandising, customer service, or store management roles.

Entrepreneurs

Build a better understanding of the systems involved in starting and managing a retail business.

E-Commerce Learners

Understand retail concepts that can also be applied to online selling and modern multi-channel retail.

Retail Career Aspirants

Build foundational knowledge for exploring opportunities across retail stores, brands, sales, and customer-facing roles.

Course Curriculum

What You Will Learn

The 8-month curriculum covers the key technologies shown in the course program, giving learners exposure to programming, databases, data processing, visualisation, analytics, and predictive modelling.
01

Python for Data Analysis

Learn Python fundamentals and its practical role in working with data and analytical tasks.

02

R Programming

Understand R and its application in data analysis, statistical work, and analytical workflows.

03

NumPy

Learn the fundamentals of NumPy for numerical operations and working with structured numerical data in Python.

04

MySQL

Develop practical knowledge of relational databases, SQL queries, data retrieval, and database-based analysis.

05

MongoDB

Understand MongoDB and the fundamentals of working with document-oriented data.

06

Power BI

Learn how to work with data in Power BI and create meaningful visual reports and dashboards.

07

Presto

Understand Presto as a data-querying technology and explore its role in working with data across analytical environments.

08

KNIME

Learn the fundamentals of KNIME and understand how visual workflows can support data preparation and analysis.

09

Data Structures & Algorithms

Build an understanding of DSA concepts that strengthen logical thinking and problem-solving for technical data work.

10

Data Analysis Workflow

Understand how different data tools can fit together across data handling, analysis, visualisation, and interpretation.

11

Predictive Modeling

Explore the fundamentals of predictive modeling and understand how data can be used to identify patterns and make predictions.

12

Practical Data Analysis

Bring together the tools and concepts covered during the course to develop a practical understanding of data-analysis workflows.

What You Get

Everything Included in This Data Analysis Course

The program brings together programming, databases, visualisation, analytical tools, and predictive modelling to provide a broad foundation in modern data analysis.

Learn Data Analysis

Build Practical Data Analysis Skills

Develop hands-on skills in programming, databases, data processing, visualization, and predictive analysis to understand and work with real-world data confidently.

INT_Data_Analysis_Course_770x450

Practical Learning

Work with the concepts and technologies covered in the curriculum to develop a more application-oriented understanding of data analysis.

Python & R Training

Develop practical exposure to two important programming languages used in data-related work.

Database Skills

Build knowledge of MySQL and MongoDB for working with different types of data storage and retrieval systems.

Power BI Skills

Learn to transform data into visual reports and dashboards using Power BI.

Data Processing Tools

Gain exposure to NumPy, Presto, and KNIME as part of a broader data-analysis toolkit.

Predictive Modeling

Understand the basic concepts behind using data for predictive analysis and pattern-based insights.

DSA Knowledge

Strengthen logical and problem-solving skills through Data Structures and Algorithms concepts.

Career Path

Where This Data Analysis Course Can Take You

Data analysis skills can be applied across technology, business, finance, operations, marketing, reporting, and other data-driven environments. Your career opportunities will depend on your practical skills, experience, portfolio, and specialization.

Data Analyst

Work with data to identify patterns, prepare analysis, and communicate useful insights.

Business Intelligence Analyst

Use data and visualisation tools to support reporting and business decision-making.

Power BI Analyst

Create reports, dashboards, and visual data presentations using Power BI.

Data Reporting Analyst

Work with databases, spreadsheets, reports, and analytical information to support organisational requirements.

Junior Data Professional

Build an entry-level foundation through programming, database, visualisation, and analytical skills.

Data Analytics Professional

With additional experience and specialization, progress towards broader data analytics responsibilities across different industries.

Course Details

Duration, Mode & What to Expect

Complete the 8-month Data Analysis Course through structured online or offline learning designed around modern data-analysis technologies.The program covers Python, R, NumPy, MySQL, Power BI, Presto, MongoDB, KNIME, DSA, and Predictive Modeling through a focused data-analysis curriculum.Build practical exposure to multiple data tools and analytical concepts through structured learning, with the course fee currently listed at ₹40,000.

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New Batch Starting Soon

Limited Seats

Practical data analysis and technology-focused learning

Python, R, MySQL, Power BI and other modern data tools

Predictive Modeling and analytical concepts

Study material and learning resources

Doubt clearing and mentor guidance

Hands-on Data Processing and Visualization Practice

Real-World Data Analysis Projects

Reserve Your Seat
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Student Stories

What Students Say

Aditya Sharma

Data Analysis Learner

5.0

""The course gave me exposure to multiple data technologies instead of focusing on only one tool. The combination of Python, SQL and Power BI was particularly useful.""

Priya Verma

Data Analytics Learner

5.0

""Learning different tools in a structured sequence helped me understand how programming, databases and visualisation connect in data analysis.""

Rohit Kumar

Data Analysis Student

5.0

""The curriculum gave me a broader understanding of data analysis and helped me explore areas like Power BI, Python and Predictive Modeling.""

Have Questions?

Common Questions FAQs

Find answers to the most common questions about our course.

The course duration is 8 months, with online and offline learning options available.

The course covers R, MySQL, Power BI, Presto, Python, NumPy, DSA, MongoDB, KNIME, and Predictive Modeling.

Yes. Python is one of the key technologies included in the course curriculum.

Yes. Power BI is included to help learners develop data visualisation and reporting skills.

The course can be suitable for learners who want to build data-analysis skills from a structured foundation. A willingness to learn programming and work with technical concepts will be helpful.

Yes. The course is available through online and offline learning, making it accessible to learners in Sonipat as well as those joining remotely.

The curriculum includes MySQL and MongoDB, giving learners exposure to both relational and document-oriented database technologies.

Yes. Predictive Modeling is included as part of the course curriculum, providing learners with an introduction to predictive analytical concepts.
Start Your Journey

Start Your Data Analysis Journey

Data analysis is not about learning a single software or programming language. It involves understanding data, working with different tools, finding meaningful patterns, creating useful visualisations, and communicating insights clearly. Build a broader foundation in modern data analysis with the Data Analysis Course at INT Institute and gain practical exposure to Python, R, databases, Power BI, analytical tools, and Predictive Modeling.

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