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Full Stack Data Analytics equips learners with the technical, analytical, and problem-solving skills needed to transform raw data into actionable insights. This practical, industry-driven program covers data cleaning, analysis, visualization, and reporting—preparing students for high-demand roles in today’s rapidly evolving data-powered economy.

#420,000

#700,000

PROGRAM OVERVIEW

The Full Stack Data Analytics Program is a comprehensive, career-focused training designed to equip learners with the technical and analytical skills required to thrive in today’s data-driven world. Over four intensive months, students gain hands-on experience across the entire analytics pipeline—from data collection and cleaning to analysis, visualization, and insight communication.

The program blends advanced Excel, SQL, Python programming, and Business Intelligence tools such as Power BI or Tableau to build a strong analytical foundation. Learners also explore statistics, predictive analysis, ETL processes, and the essentials of data engineering, ensuring they understand both the analytical and structural components of modern data systems.

Through real-world case studies and practical industry projects, students develop the ability to interpret complex datasets, build dashboards, automate reports, and convert raw data into meaningful business insights. Each participant completes a capstone project and a professional portfolio to showcase their skills to employers.

Ideal for beginners and career switchers, this program prepares students for high-demand roles such as Data Analyst, Business Intelligence Analyst, Product Analyst, and Reporting Specialist. Graduates leave with confidence, technical competence, and job-ready skills to excel in today’s competitive digital economy.

What You'll Learn

MODULE 1: FOUNDATIONS OF DATA ANALYTICS (Week 1–2)

Topics

  • Introduction to Data Analytics & Data Ecosystem

  • Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)

  • Data Lifecycle & Data Value Chain

  • Business Intelligence vs Data Science vs Data Engineering

  • Data Ethics, Privacy & Governance

  • Analytical Thinking & Problem-Solving

  • KPIs, Metrics & Business Requirements Gathering

Practical

  • Designing analytics use cases

  • Hands-on exploratory analysis

MODULE 2: EXCEL FOR DATA ANALYSIS (Week 3–4)

Topics

  • Advanced Excel Functions (LOOKUPs, IFs, TEXT, DATE, Statistical functions)

  • Pivot Tables & Pivot Charts

  • Data Cleaning & Formatting for Analytics

  • Power Query Basics

  • Excel Dashboards

Practical

  • Build a business dashboard using Pivot Tables & Charts

MODULE 3: SQL FOR DATA ANALYSIS (Week 5–7)

Topics

  • Understanding Databases & Data Warehouses

  • SQL Basics (SELECT, WHERE, GROUP BY, HAVING)

  • Joins, UNION, Subqueries, CTEs

  • Window Functions (ROW_NUMBER, RANK, LAG, LEAD)

  • Data Manipulation (INSERT, UPDATE, DELETE)

  • Performance Optimization

  • Building Complex Analytical Queries

Practical

  • Analyze sales database using SQL

  • Write optimized queries for real-world scenarios

MODULE 4: PYTHON FOR DATA ANALYTICS (Week 8–10)

Topics

  • Python Basics for Analytics

  • Data Structures & Control Flow

  • Working with Pandas & NumPy

  • Data Cleaning, Wrangling & Transformation

  • Exploratory Data Analysis (EDA)

  • Data Visualization (Matplotlib, Seaborn, Plotly)

  • Automated Reporting

  • Introduction to APIs & Web Scraping

Practical

  • Build an EDA report using Pandas

  • Visualize trends using Seaborn & Plotly

MODULE 5: DATA VISUALIZATION & BUSINESS INTELLIGENCE (Week 11–12)
Power BI / Tableau (choose one or both)

Topics

  • Connecting to Data Sources

  • Data Modeling (Star Schema & Snowflake)

  • DAX Formulas (Power BI)

  • Calculated Columns & Measures

  • Building Interactive Dashboards

  • Publishing & Sharing Reports

  • BI for Business Decision-Making

Practical

  • Build a fully interactive BI dashboard

MODULE 6: STATISTICS & ANALYTICS MODELLING (Week 13–14)

Topics

  • Descriptive & Inferential Statistics

  • Probability, Distributions & Hypothesis Testing

  • Correlations & Regression Analysis

  • Forecasting Models

  • A/B Testing

  • Feature Importance & Intro to Machine Learning

Practical

  • Build a regression model

  • Run an A/B test simulation

MODULE 7: DATA ENGINEERING BASICS (Week 15)

Topics

  • ETL vs ELT

  • Understanding Data Pipelines

  • Cloud Analytics (AWS/GCP/Azure overview)

  • Data Warehouses (BigQuery, Snowflake, Redshift)

  • Introduction to Big Data Tools (Spark concept)

Practical

  • Build a simple ETL pipeline

  • Load data into a mini warehouse

MODULE 8: CAPSTONE PROJECT & PORTFOLIO (Week 16)

Activities

  • Choose a real-world dataset (finance, e-commerce, health, marketing, logistics, etc.)

  • Perform Data Cleaning & EDA

  • SQL and Python analysis

  • Build BI Dashboard

  • Deliver business recommendations

Career Path After Completion
  • Data Analyst

  • Business Intelligence (BI) Analyst

  • Data Scientist (Junior/Associate)

  • Data Engineer (Entry-Level)

  • SQL Database Analyst

  • Power BI / Tableau Developer

  • Machine Learning Analyst (Junior)

  • Operations / Product Data Analyst

  • Marketing / Growth Data Analyst

  • Financial Data Analyst

  • Business Analyst (Tech-Focused), etc.

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