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Data Analytics Course

Turn raw data into meaningful business insights. Our classroom Data Analytics training takes you from beginner-level concepts to practical analytics using Advanced Excel, SQL, Power BI, Statistics and Python. Students learn through instructor-led sessions, practical datasets, business case studies, dashboards and portfolio projects designed to build the skills required for entry-level analytics roles.

Beginner 6 Months
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AT A GLANCE

Data Analytics Course snapshot

Data Analyst Course with AI, Python, SQL & Power BI Become a job-ready Data Analyst with hands-on training in Excel, SQL, Python, Pandas, Statistics, Power BI, DAX, AI Tools, and real-world projects to build a strong professional portfolio.

AT A GLANCE

Course highlights

LevelBeginner
Duration6 Months
Learning hours480 hours
Total classes160
Learning formatClassroom
Batch typeWeekday
Course languageEnglish, Hindi

THE OVERVIEW

About this course

The Data Analytics Course is designed for students, freshers and working professionals who want to build a career in data analytics.

In this course, students will learn how to collect, clean, analyze and visualize data using industry-standard tools such as Microsoft Excel, SQL, Power BI and Python.

The course focuses on practical learning, real-world datasets, dashboards, business case studies and hands-on projects so students can develop job-ready data analytics skills.

BUILD YOUR SKILLS

What you will learn

  • Analyze raw business data and convert it into useful insights.
  • Create professional dashboards, reports and visualizations.
  • Query and analyze relational databases using SQL
  • Use Python libraries for data cleaning and exploratory analysis.

YOUR LEARNING PATH

Course curriculum

1 module · Explore the syllabus below

What is Data Analytics

Types of Data Analytics

Data Analyst roles and responsibilities

Data Analytics lifecycle

Understanding datasets

YOUR TOOLKIT

Tools & technologies

  • execl
  • python
  • Nampy
  • Pandas

BEFORE YOU BEGIN

Who should join?

Students looking for a career-oriented technology course

Fresh graduates preparing for their first analytics job

Working professionals who want to move into analytics

MIS and reporting professionals

Business professionals who work with data

Entrepreneurs who want to understand business data

Beginners interested in Excel, SQL, Power BI and Python

Candidates preparing for Data Analyst or Business Analyst roles

Prerequisites

  • College Students
  • Freshers
  • BCA / B.Tech / B.Sc students
  • Commerce students
  • Working Professionals
  • Beginners interested in Data Analytics
  • Anyone looking to switch into a Data Analyst career

No prior programming experience is required.


BEYOND THE CLASSROOM

Included with your course

Course certificate

Placement assistance

Demo class available

CAREER GUIDE

Career roadmap

What Does a Data Analyst Do?

A Data Analyst collects, cleans, organizes and analyzes data to help businesses understand performance, identify trends and make informed decisions. Data Analysts commonly work with Excel, SQL, Power BI, Python and visualization tools to transform raw information into reports, dashboards and actionable insights.

Daily Work of a Data Analyst

Collect data from different sources Clean incomplete or incorrect data Write SQL queries Analyze sales, marketing, finance or operational data Create Excel reports Build Power BI dashboards Track KPIs Identify trends and patterns Present insights to managers Automate repetitive reporting tasks

LEARNING PATH

Step-by-step roadmap

1

Data Analytics Fundamentals

Understand data, analytics types, business problems, datasets and the Data Analytics lifecycle.

2

Advanced Excel

Learn formulas, lookup functions, Pivot Tables, charts, cleaning and reporting.

SKILLS YOU BUILD

What you will learn

  • Analyze raw business data and convert it into useful insights.
  • Create professional dashboards, reports and visualizations.
  • Query and analyze relational databases using SQL
  • Use Python libraries for data cleaning and exploratory analysis.

CURRICULUM

Course curriculum

What is Data Analytics

Types of Data Analytics

Data Analyst roles and responsibilities

Data Analytics lifecycle

Understanding datasets

JOB OUTLOOK

Career opportunities

Data Analyst 3.5 LPA – 6 LPA
Junior Business Analyst 8 LPA – 15 LPA

GROWTH

Career progression

Advanced Excel

Learn data cleaning, formulas, Pivot Tables, dashboards, Power Query, charts, lookup functions, automation, and business reporting.

Statistics for Data Analytics

Understand mean, median, probability, variance, standard deviation, correlation, distributions, hypothesis testing, and business statistics.

INDUSTRY FIT

Where this skill fits

Information Technology E-commerce Finance

Related roles

Business Analyst Business Intelligence Analyst Power BI Developer Data Scientist Product Analyst Marketing Analyst Financial Analyst Operations Analyst MIS Executive Analytics Consultant Data Engineer

Why classroom training works

  • Face-to-face trainer interaction
  • Live practical demonstrations
  • Immediate doubt resolution
  • Structured learning schedule
  • Classroom assignments
  • Peer interaction
  • Practical lab sessions
  • Trainer-guided projects
  • Interview preparation
  • Portfolio guidance
  • Regular assessments
  • Career guidance

FAQ

Common questions

Data Analytics is the process of collecting, cleaning, transforming and analyzing data to identify useful information, trends and insights that support better decisions.

Yes. The course starts from fundamental concepts and gradually progresses to Excel, SQL, Power BI, Python, statistics and practical projects.

Common myths

  • Data Analytics requires advanced mathematics. Reality: Most entry-level Data Analytics work requires practical statistics, logical thinking and business understanding rather than advanced mathematics.
  • Only engineering students can become Data Analysts. Reality: Professionals from commerce, management, science and other backgrounds also work successfully in analytics.
  • Data Analytics and Data Science are the same. Reality: They overlap, but Data Science generally includes more advanced programming, machine learning and predictive modeling.

Beginner mistakes to avoid

  • Learning tools without practicing on real datasets
  • Memorizing SQL instead of solving queries
  • Creating dashboards without understanding business requirements
  • Ignoring statistics

Interview topics

Data Analytics fundamentals Excel formulas Types of analytics

TAKE THE NEXT STEP

Ready to explore Data Analytics Course ?

Speak with our counsellors and find the right learning path for you.

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