DATA ANALYST

About This Course

This comprehensive Data Analyst course takes you from data fundamentals to advanced analytics. You'll master Excel, SQL, Python, Power BI, Tableau, and statistical analysis. Through 15+ real-world projects, you'll learn to collect, clean, analyze, and visualize data to deliver actionable business insights. By the end, you'll have a professional portfolio and the skills to land top data analyst roles.

Syllabus Overview

• Module 1: Introduction to Data Analytics
  • What is Data Analytics?
  • Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
  • Data Analytics Lifecycle
  • Roles & Responsibilities of a Data Analyst
  • Industry Applications & Use Cases
  • Data-Driven Decision Making
  • Analytics Tools Overview
• Module 2: Advanced Excel for Analytics
  • Excel Fundamentals & Shortcuts
  • Data Cleaning & Formatting
  • Conditional Formatting
  • VLOOKUP, HLOOKUP, XLOOKUP
  • INDEX & MATCH
  • Pivot Tables & Pivot Charts
  • Power Query for Data Transformation
  • Power Pivot & Data Modeling
  • Advanced Formulas (IF, SUMIFS, COUNTIFS)
  • What-If Analysis & Goal Seek
  • Macros & VBA Basics
  • Excel Dashboards
• Module 3: SQL for Data Analysis
  • Introduction to Databases & SQL
  • SELECT Statements & Filtering (WHERE)
  • Sorting & Grouping Data (ORDER BY, GROUP BY)
  • Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
  • Joins (INNER, LEFT, RIGHT, FULL, CROSS)
  • Subqueries & Nested Queries
  • Window Functions (ROW_NUMBER, RANK, DENSE_RANK)
  • Common Table Expressions (CTEs)
  • String Functions & Date Functions
  • Views & Stored Procedures
  • Database Design & Normalization
  • Performance Optimization & Indexing
  • Working with MySQL & PostgreSQL
• Module 4: Python for Data Analytics
  • Python Basics for Data Analysis
  • NumPy for Numerical Computing
  • NumPy Arrays & Vectorized Operations
  • Pandas DataFrames & Series
  • Data Cleaning with Pandas
  • Handling Missing Data & Outliers
  • Data Transformation & Merging
  • GroupBy & Aggregation
  • Working with Dates & Time Series
  • Reading/Writing CSV, Excel, JSON, SQL
  • Matplotlib for Data Visualization
  • Seaborn for Statistical Visualizations
  • Plotly for Interactive Charts
• Module 5: Statistics for Data Analysis
  • Descriptive Statistics (Mean, Median, Mode, Std Dev)
  • Probability Distributions (Normal, Binomial, Poisson)
  • Central Limit Theorem
  • Hypothesis Testing (t-test, z-test, chi-square)
  • ANOVA (Analysis of Variance)
  • Correlation & Covariance
  • Regression Analysis (Linear & Multiple)
  • Confidence Intervals
  • P-values & Statistical Significance
  • A/B Testing Fundamentals
  • Sampling Techniques
• Module 6: Power BI
  • Introduction to Power BI
  • Power BI Desktop & Service
  • Connecting to Data Sources
  • Power Query for Data Transformation
  • Data Modeling & Relationships
  • DAX Functions (CALCULATE, FILTER, SUMX)
  • Creating Visualizations & Charts
  • Interactive Dashboards & Reports
  • Slicers, Filters & Drill-through
  • Power BI Service & Sharing
  • Row-Level Security (RLS)
  • Real-time Dashboards
• Module 7: Tableau
  • Introduction to Tableau
  • Connecting to Data Sources
  • Data Preparation & Blending
  • Dimensions & Measures
  • Creating Charts & Graphs
  • Calculated Fields & Table Calculations
  • Parameters & Filters
  • LOD Expressions (Level of Detail)
  • Interactive Dashboards
  • Story Points & Storytelling
  • Tableau Public & Publishing
  • Best Practices for Visualization
• Module 8: Exploratory Data Analysis (EDA)
  • Understanding Data Structure
  • Data Profiling & Summary Statistics
  • Identifying Patterns & Trends
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariate Analysis
  • Outlier Detection & Treatment
  • Feature Engineering Basics
  • Correlation Matrix & Heatmaps
  • Data Distributions & Transformations
  • Automated EDA with Pandas Profiling
• Module 9: Data Storytelling & Communication
  • Principles of Data Visualization
  • Choosing the Right Chart Type
  • Design Principles for Dashboards
  • Storytelling with Data Framework
  • Creating Executive Summaries
  • Presentation Skills for Analysts
  • Business Communication & Reporting
  • Making Data-Driven Recommendations
  • Common Visualization Mistakes
  • Building a Data Portfolio
• Module 10: Capstone Projects
  • Sales Analytics Dashboard
  • Customer Segmentation Analysis
  • Financial Performance Report
  • Marketing Campaign Analysis
  • HR Analytics & Attrition Analysis
  • E-commerce Data Analysis
  • Healthcare Data Analysis
  • Supply Chain Analytics
  • Social Media Analytics
  • End-to-End Business Case Study

Tools & Technologies Covered

Hands-On Projects

• Sales Performance Dashboard with Power BI & SQL
• Customer Churn Analysis using Python & Statistical Methods
• Financial Statement Analysis with Excel & Pivot Tables
• E-commerce Sales Analysis with Tableau Dashboard
• Marketing Campaign ROI Analysis using SQL & Python
• HR Attrition Analytics & Predictive Insights
• Healthcare Data Analysis & Patient Outcome Study
• Supply Chain Optimization Analysis
• Social Media Sentiment Analysis Dashboard
• Real Estate Market Analysis & Price Prediction
• A/B Test Analysis for Website Optimization
• Retail Inventory Analytics Dashboard
• Customer Lifetime Value (CLV) Analysis
• End-to-End Business Intelligence Project
• Data Storytelling Portfolio Presentation

Career Outcomes

• Data Analyst₹4 – ₹10 LPA
• Business Intelligence Analyst₹5 – ₹12 LPA
• Data Analytics Consultant₹6 – ₹15 LPA
• Business Analyst₹5 – ₹12 LPA

Frequently Asked Questions

• Do I need a technical background for this course?

  No technical background is required. The course starts from basics and builds up gradually. Basic computer knowledge and analytical thinking are sufficient to get started.

• Which tools are most important for a Data Analyst?

  Excel, SQL, and a visualization tool (Power BI or Tableau) form the core toolkit. Python is increasingly important for advanced analytics. Our course covers all these comprehensively.

• How is this different from a Data Science course?

  Data Analytics focuses on analyzing existing data to derive insights and create reports. Data Science goes deeper into machine learning and predictive modeling. This course prepares you specifically for Data Analyst and BI roles.

• Will I work on real datasets during the course?

  Yes! All 15+ projects use real-world datasets from domains like e-commerce, healthcare, finance, HR, and marketing. You'll build a professional portfolio that showcases your analytical skills to employers.