Analytics

Data Analyst

Turn raw data into actionable business insights using Excel, SQL, Python, and Power BI.

Course Fee ₹ ---

Course Overview

The Data Analyst program is designed to transform you into a job-ready analytics professional. You will start with the foundational tools every analyst needs — Excel and SQL — then progress to Python for data manipulation and Power BI for building interactive dashboards. The course places equal emphasis on technical skills and analytical thinking, covering statistics, hypothesis testing, and real-world case studies. Every module includes hands-on projects using industry datasets, and the program concludes with a portfolio of dashboards and analyses that demonstrate your capabilities to hiring managers. Data analytics is one of the fastest-growing career fields, and this course gives you a direct path into it.

What You'll Learn

Excel & SQL Mastery

Analyze and query data confidently using advanced Excel functions, pivot tables, and complex SQL queries with joins and window functions.

Python Analytics

Wrangle, clean, and analyze large datasets using Pandas and NumPy, and create compelling visualizations with Matplotlib and Seaborn.

Power BI Dashboards

Build interactive, publish-ready business dashboards using DAX formulas, data modeling, and Power BI visualization best practices.

Statistical Thinking

Apply descriptive statistics, probability, hypothesis testing, and regression analysis to make data-driven business decisions.

Detailed Syllabus

Module 1: Excel for Data Analysis

  • Pivot Tables for summarizing and cross-tabulating data
  • VLOOKUP, XLOOKUP, INDEX-MATCH for data retrieval
  • Conditional formatting and data validation rules
  • Data cleaning: removing duplicates, text-to-columns, handling blanks
  • Charts, sparklines, and dashboard creation in Excel
  • What-if analysis: Goal Seek, Scenario Manager, Data Tables

Module 2: SQL & Databases

  • SELECT statements, filtering with WHERE, and sorting with ORDER BY
  • INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN
  • Subqueries, correlated subqueries, and derived tables
  • Window functions: ROW_NUMBER, RANK, LEAD, LAG, running totals
  • Common Table Expressions (CTEs) for readable complex queries
  • Working with PostgreSQL and MySQL environments

Module 3: Python for Data

  • Python fundamentals: variables, loops, functions, and data structures
  • Pandas: DataFrames, filtering, groupby, merge, and pivot tables
  • NumPy: arrays, broadcasting, and vectorized operations
  • Data wrangling: handling missing values, type conversion, string operations
  • Matplotlib: line charts, bar charts, histograms, and subplots
  • Seaborn: heatmaps, pair plots, distribution plots, and styling

Module 4: Power BI & Visualization

  • Connecting to data sources: Excel, SQL, CSV, web APIs
  • Data modeling: relationships, star schema, and calculated columns
  • DAX formulas: CALCULATE, FILTER, ALL, time intelligence functions
  • Building interactive dashboards with slicers, drill-throughs, and bookmarks
  • Custom visuals, conditional formatting, and KPI indicators
  • Publishing reports to Power BI Service and scheduled refresh

Module 5: Statistics & Probability

  • Descriptive statistics: mean, median, mode, standard deviation, percentiles
  • Probability distributions: normal, binomial, Poisson
  • Hypothesis testing: t-tests, chi-square tests, p-values, confidence intervals
  • Correlation analysis and interpreting correlation coefficients
  • Linear and multiple regression analysis
  • A/B testing methodology and business applications

Module 6: Real-World Projects

  • Sales Dashboard: revenue trends, regional performance, KPI tracking
  • Customer Segmentation: RFM analysis, clustering, and targeting strategies
  • HR Analytics: attrition prediction, workforce planning, compensation analysis
  • End-to-end data pipeline: extract, clean, analyze, visualize, and present
  • Storytelling with data: building narratives around findings

Module 7: Interview Prep

  • SQL coding challenges and timed practice sets
  • Case study walkthroughs: business problem to data solution
  • Portfolio building: GitHub, Notion, or personal website showcase
  • Resume crafting for data analyst roles
  • Mock interviews and behavioral question preparation

Who Is This For?

  • Fresh graduates looking to enter the data analytics field with practical skills
  • Working professionals in non-tech roles who want to transition into data-driven positions
  • Business analysts and operations staff seeking to formalize their analytics toolkit
  • Marketing, finance, or HR professionals who want to leverage data for better decision-making
  • Anyone curious about data who wants a structured, career-focused learning path

Career Outcomes

  • Data Analyst at consulting firms, IT companies, and product startups
  • Business Intelligence Analyst building dashboards and reports for leadership
  • MIS Executive managing data pipelines and operational reporting
  • Power BI Developer creating enterprise-grade visualization solutions
  • Analytics Consultant working with clients across industries
  • A job-ready portfolio with dashboards, SQL projects, and case studies
Enroll Now — ₹ ---