Last Updated: 01/2026
Start Date Dec 15, 2025
Duration 130 Days
EMI Starting At ₹10000
Language Availability Both English & Hindi

Course Key Highlights:

Hours of Instructor-Led Training
Flexible Schedule
22 Hours of Self-Paced Videos
Certification
Job Assistance
Lifetime Free Upgrade
56 Hours of Projects Exercises
Hours of Instructor-Led Training
Why should you opt for this course?
  • Live interactive sessions with lifetime recorded access
  • Industry-standard tools: Excel, SQL, Python, Power BI, Tableau
  • Real-world datasets and case studies from top companies
  • Hands-on projects building end-to-end dashboards
  • Placement readiness with mock interviews and portfolio building

What will you learn?

  • Master Excel with pivot tables, data cleaning, visualization, and dashboards.
  • SQL for data extraction, database design, and complex queries.
  • Python programming with Pandas, NumPy, Matplotlib for data manipulation and analysis.
  • Statistical analysis, probability theory, hypothesis testing, and correlation analysis.
  • Power BI and Tableau for creating interactive dashboards and visualizations.
  • Advanced techniques including ETL processes, API integration, and predictive modeling.

Who should enroll?

Freshers
Freshers

Freshers and recent graduates aspiring to start a career in data analytics

Working Professionals Seeking Career Growth
Working Professionals Seeking Career Growth

HR, Finance, and Operations professionals seeking to enhance data analytics skills

Carrer Switchers
Carrer Switchers

Carrer Switchers

What are the prerequisites?

  • Basic computer knowledge and familiarity with Windows or Mac operating system. No prior programming or data analytics experience required. Beginner-friendly course for anyone with basic computer skills.

What is the price?

₹25000 ₹20000
₹20000 Buy Now

Course Curriculum

📌 Module 1: Fundamentals of Data Analytics 4 lectures

📋 Learning Objectives

  • 🧠 Understand the role of a Data Analyst
  • 🔄 Learn the data analytics workflow
  • 📊 Explore different types of analytics
  • 📈 Grasp business metrics and KPIs

📚 Topics Covered

  • 🟡 1.1 Introduction to Data Analytics
  • 🔍 1.2 Types of Data Analytics
  • 📁 1.3 Data and Information
  • 🛠️ 1.4 Analytics Workflow (OSEMN Framework)
  • 💼 1.5 Business Context
  • 🧰 1.6 Tools Overview

🧪 Hands-on Activities

  • 🧪 Lab: Understanding Data Sources
  • 📊 Lab: Identifying KPIs

📌 Project: Business Problem Analysis

📊 Module 2: Excel for Data Analytics 6 lectures

📋 Learning Objectives

  • 📈 Master analytics in Excel
  • 📊 Build dashboards & reports
  • 🛠️ Use advanced functions
  • 🤖 Automate tasks

📚 Topics Covered

  • 📘 Excel Fundamentals
  • ✨ Advanced Excel Functions
  • 🧹 Data Cleaning & Preparation
  • 📑 Pivot Tables & Analysis
  • 📊 Statistical Functions
  • 🎨 Visualization & Dashboards
  • 🔍 What-If & Advanced Analytics
  • 🤖 Macros & Automation

🧪 Hands-on Activities & Projects

  • 📉 Pivot Table Creation
  • 📊 Dashboard Design
  • 📈 Sales Performance Analysis
📈 Module 3: Statistics & Probability for Data Analysis 5 lectures

📋 Learning Objectives

  • 📊 Learn core statistical methods
  • 🎲 Understand probability
  • 📉 Perform hypothesis testing
  • 📈 Apply statistical thinking

📚 Topics Covered

  • 📌 Descriptive Statistics
  • 🧮 Probability Theory
  • 📊 Regression & Correlation
  • 🔄 Time Series Analysis
  • 🧠 Statistical Thinking

🧪 Hands-on Activities

  • 📈 Statistical Analysis Lab
  • 📊 Hypothesis Testing Lab
  • 📉 Regression & Forecasting
🗃️ Module 4: SQL for Data Retrieval & Analysis 7 lectures

📋 Learning Objectives

  • 🗄️ Master database querying
  • 🧠 Build complex SQL queries
  • 🧩 Learn database design
  • 🚀 Optimize performance

📚 Topics Covered

  • 💽 Database Fundamentals
  • 🔍 SQL Basics & SELECT
  • 📊 Advanced Queries
  • 🔗 Joins & Subqueries
  • 📈 Window Functions
  • ⚙️ Performance Optimization

🧪 Hands-on Activities

  • 🧩 SQL Labs
  • 📌 Database Analysis Projects
🐍 Module 5: Python for Data Analysis 6 lectures

📋 Learning Objectives

  • 🐍 Use Python for analytics
  • 🧰 Work with Pandas & NumPy
  • 📊 Build data workflows
  • 📈 Visualize data

📚 Topics Covered

  • 🖥️ Python Basics
  • 🔁 Control Flow
  • 📊 Data Structures
  • 📥 File Handling
  • 🧮 EDA & Visualizations
  • 🧰 Regex for Data Cleaning

🧪 Hands-on Activities

  • 🧠 Data Manipulation
  • 📉 Visualization Projects
📊 Module 6: Data Visualization with Tableau 6 lectures

📋 Learning Objectives

  • 🔍 Master Tableau dashboards
  • 📈 Create interactive charts
  • 🧠 Tell data stories visually

📚 Topics Covered

  • 📊 Tableau Fundamentals
  • 🖼️ Building Visuals
  • 🎯 Dashboard Design
  • 🔄 Interactivity & Filters
  • 🗺️ Maps & Storytelling

🧪 Hands-on Activities

  • 📊 Build Business Dashboards
  • 📈 Data Storytelling Projects
📊 Module 7: Power BI for Data Analytics 6 lectures

📋 Learning Objectives

  • 📈 Build interactive dashboards using Power BI
  • 🔗 Connect multiple data sources
  • 🧠 Apply data modeling concepts
  • 📊 Share insights with stakeholders

📚 Topics Covered

  • ⚡ Introduction to Power BI & Architecture
  • 🔌 Data Connections & Importing
  • 🧹 Data Cleaning using Power Query
  • 🧩 Data Modeling & Relationships
  • 🧮 DAX Basics & Calculated Measures
  • 📊 Visuals, Charts & KPIs
  • 🎯 Interactive Dashboards & Reports
  • 🔐 Publishing & Sharing Reports

🧪 Hands-on Activities

  • 📊 Sales & Finance Dashboard
  • 📈 Business Performance Report
  • 🧠 Real-world BI Use Case Project
🤖 Module 8: Data Analytics with AI Tools 4 lectures

📋 Learning Objectives

  • 🤖 Use AI to speed up analysis
  • 🧠 Improve decision-making with AI insights
  • 📊 Automate reporting & analysis tasks
  • 🚀 Increase productivity as a Data Analyst

📚 Topics Covered

  • 🤖 Introduction to AI in Data Analytics
  • 🧠 Prompt Engineering for Data Analysis
  • 📊 AI-assisted Data Cleaning
  • 📈 AI-based Insights & Forecasting
  • 📝 Automated Reports & Summaries
  • 🔍 AI Tools for SQL, Excel & Python
  • ⚙️ Ethics & Responsible AI Use

🧪 Hands-on Activities

  • 🤖 AI-assisted Excel & SQL Analysis
  • 📊 Automated Dashboard Insights
  • 🚀 Productivity Booster Mini Projects
💼 Module 9: Real-World Projects, Interview & Career Preparation 1 lectures

📋 Learning Objectives

  • 💼 Gain real-world project experience
  • 📄 Build an industry-ready resume
  • 🎤 Crack Data Analyst interviews
  • 🚀 Become job-ready

📚 Topics Covered

  • 🏢 Industry-Based Capstone Projects
  • 📊 End-to-End Data Analysis Lifecycle
  • 📄 Resume & Portfolio Building
  • 🧠 Business Case Studies
  • 🎤 Technical & HR Interview Questions
  • 💬 Mock Interviews & Feedback
  • 🔍 Job Search & Career Guidance

🧪 Hands-on Activities

  • 📊 Capstone Project (Domain-Based)
  • 📁 Portfolio & GitHub Setup
  • 🎯 Interview Simulation Sessions

Course Projects

Project 1: Sales Analysis and Forecasting

Difficulty: Intermediate | Duration: 2-3 days Create comprehensive sales analytics solution: • Clean and consolidate sales data • Perform regional and product analysis • Create sales forecasting model • Build interactive sales dashboard • Present insights and recommendations Technologies: Excel, SQL, Python, Tableau/Power BI

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Project 2: Customer Analytics and Segmentation

Difficulty: Intermediate | Duration: 3-4 days Develop customer analytics platform: • Clean customer and transaction data • Perform RFM analysis • Create customer segments • Build predictive churn model • Design customer analytics dashboard • Recommend retention strategies Technologies: Excel, SQL, Python, Tableau/Power BI

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Project 3: Marketing Campaign Performance Dashboard

Difficulty: Intermediate-Advanced | Duration: 3-4 days Build marketing analytics solution: • Integrate campaign and conversion data • Calculate ROI and performance metrics • Perform cohort analysis • Create attribution model • Build interactive marketing dashboard • Present optimization recommendations Technologies: SQL, Python, Tableau/Power BI, Excel

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Project 4: Financial Analytics and Reporting

Difficulty: Advanced | Duration: 4-5 days Create financial analysis system: • Import and reconcile financial data • Calculate financial ratios and metrics • Perform variance and trend analysis • Build forecasting model • Create financial executive dashboard • Present financial insights and recommendations Technologies: Excel, SQL, Python, Power BI

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Tools Covered

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Frequently Asked Question

A Data Analyst collects, processes, and analyzes data to help organizations make informed business decisions. They use tools like Excel, SQL, Python, and BI platforms to uncover insights, trends, and patterns in data that drive strategic business decisions.

No! This course is beginner-friendly. We start from basics and gradually progress to advanced concepts. Basic computer knowledge is sufficient. No prior programming or data analytics experience is required to enroll.

Yes! The course includes career guidance, resume optimization, mock interviews, portfolio review, and job placement support to help you secure positions with leading IT companies and startups.

You will learn industry-standard tools including Microsoft Excel (advanced formulas, pivot tables, data visualization), SQL (MySQL, PostgreSQL, database design), Python (Pandas, NumPy, Matplotlib), Power BI, Tableau, and statistical analysis tools. All tools used are widely used in the industry.

The course is 5 months long with 130 days total duration. It includes 70 live classes and 370 total hours of learning content. You can access recorded sessions anytime after live class completion, making it flexible for working professionals.

No, this course is designed for beginners. No prior programming or Excel experience required.

Yes, you get 1 year access to all video lectures, labs, resources, and updates

We provide job placement assistance and connect you with hiring partners, but placement depends on performance and market conditions.

Yes, top performers get internship opportunities with partner companies.

Yes, our flexible evening and weekend batches are designed for working professionals.

Both are taught in the course. Your employer's preference may guide focus, but learning both increases opportunities.

Yes, we cover basics of cloud databases with AWS and Azure SQL examples.

Career Scope?

    • Data Analyst - 3-6 LPA entry level to 8-12 LPA senior roles in IT companies, startups, and corporations
    • Business Analyst - 4-7 LPA entry level to 10-15 LPA senior roles in consulting firms and enterprises
    • BI Developer - 5-8 LPA entry level to 12-18 LPA senior roles in tech companies and MNCs
    • Reporting Analyst - 3.5-6 LPA entry level to 8-11 LPA senior roles in financial and banking sectors

Frequently bought together

Your Instructors

Person

Abhinav Thakur

Senior Full Stack Developer with 10+ years of extensive experience in designing, developing, and deploying scalable web applications across modern tech stacks. Skilled in leading projects, mentoring teams, and delivering high-quality solutions.