Data Analytics Combo

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Data Analytics combo typically refers to the strategic combination of various data analytics tools, techniques, and technologies to derive valuable insights and make informed decisions from data. Data analytics involves the systematic analysis of data to uncover patterns, trends, and actionable insights.A Data Analytics combo involves using the appropriate combination of these methods and tools to extract valuable insights from data, whether it’s for business intelligence, marketing analysis, scientific research, or any other domain

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Overview

The Syllabus

Curriculum Designed by Experts

DATA ANALYTICS COMBO PROGRAM (0 → JOB READY)

⏱ Duration: 60 to 80 Hours

🎯 Outcome: Job-ready Data Analyst / BI Analyst

ROADMAP (Learning Journey)

FOUNDATION
   ↓
Excel Basics → Advanced Excel → Dashboards
   ↓
SQL Basics → Joins → Advanced SQL → Real Queries
   ↓
Python Basics → Pandas → EDA → Visualization
   ↓
Power BI Basics → Data Modeling → DAX → Dashboards
   ↓
END-TO-END PROJECTS
   ↓
JOB PREPARATION (Resume + Interview + Case Studies)

DATA ANALYTICS FOUNDATION

⏱ Duration: 6–10 Hours

🎯 Outcome: Strong conceptual understanding of Data Analytics

ROADMAP (Learning Journey)

What is Data?
   ↓
Types of Data
   ↓
Data Analytics Concepts
   ↓
Data Lifecycle
   ↓
Business Understanding
   ↓
Basic Statistics
   ↓
Tools Overview
   ↓
Case Studies & Problem Solving

Module 1 – Introduction To Data & Analytics (1.5 Hours)

Topics
What is Data?
Types of Data:
Structured
Unstructured
Semi-structured
What is Data Analytics
Importance of data in business
📌 Assignment
Identify types of data used in:
Banking
Retail
Healthcare

Module 2 – Types Of Data Analytics (1 Hour)

Topics
Descriptive Analytics (What happened?)
Diagnostic Analytics (Why it happened?)
Predictive Analytics (What will happen?)Prescriptive Analytics (What should be done?)
💼 Job Scenario
Analyze why sales dropped in a company

Module 3 – Data Analytics Lifecycle (1 Hour)

Topics
Data Collection
Data Cleaning
Data Processing
Data Analysis
Data Visualization
Decision Making
📌 Assignment
Map lifecycle for a real-world example

Module 4 – Business Understanding (1 Hour)

Topics
Business problems vs technical problems
Performance Indicators)
Metrics:
Revenue
Profit
Customer retention
💼 Job Scenario
Define KPIs for a retail company

Module 5 – Basic Statistics For Data Analytics (1.5–2 Hours)

Topics
Mean, Median, Mode
Variance & Standard Deviation
Correlation
Distribution basics
🧪 Lab
Calculate statistics using sample data
📌 Assignment
Analyze dataset using statistical measures

Module 6 – Problem Solving & Analytical Thinking (1 Hour)

Topics
Breaking business problems
Logical thinking
Data-driven decision making
💼 Job Scenario
Identify reasons for customer churn

Module 7 – Tools Overview (1 Hour)

Topics
Excel (data cleaning & reporting)
SQL (data extraction)
Python (data analysis)
Power BI (visualization)👉 No deep learning here—just awareness

Module 8 – Case Studies & Mini Project (1–2 Hours)

Mini Project – Sales Data Analysis (Conceptual)
Tasks
Identify trends
Define KPIs
Suggest business improvements

Real - Time Job Scenarios

Why sales decreased last quarter?
Which product generates highest revenue?
Why customers are leaving?
Which region performs best?

Assignments Structure

Identify data types
Define KPIs
Solve business case
Perform basic statistical analysis

ADVANCED EXCEL (XL) FOR DATA ANALYTICS

⏱ Duration: 15–20 Hours

🎯 Outcome: Data Analyst / Reporting Analyst

🧭 📌 ROADMAP

Excel Basics
   ↓
Data Cleaning & Formatting
   ↓
Functions & Formulas
   ↓
Lookup & Data Analysis
   ↓
Pivot Tables & Charts
   ↓
Power Query (ETL)
   ↓
Dashboards & Automation
   ↓
Projects + Job Scenarios

Module 1 – Excel Basics Refresher (2 Hours)

Topics
Excel interface & shortcuts
Data types & formatting
Sorting & filtering
🧪 Lab
Format raw dataset
Assignment
Clean and organize data

Module 2 – Data Cleaning & Preparation (3 Hours)

Topics
Remove duplicates
Text to Columns
Flash Fill
Data validation
Handling missing data
🧪 Lab
Clean messy dataset
📌 Assignment
Prepare dataset for analysis
💼 Job Scenario
Clean customer data received from CRM

Module 3 – Functions & Formulas (5 Hours)

Logical Functions
IF, nested IF
AND, OR
Lookup Functions
VLOOKUP, HLOOKUP
INDEX + MATCH (very important)
XLOOKUP
Text Functions
LEFT, RIGHT, MID
CONCAT, LEN
Date Functions
TODAY, NOW
DATEDIF
Statistical Functions
COUNT, COUNTA
SUMIF, COUNTIF
🧪 Lab
Apply formulas on datasets
📌 Assignment
Build formula-based report
💼 Job Scenario
Generate KPI report using formulas

Module 4 – Data Analysis Using Excel (3 Hours)

Topics
Conditional formatting
Data analysis techniques
What-if analysis
Goal Seek & Scenario Manager
🧪 Lab
Highlight trends
📌 Assignment
Analyze sales performance

Module 5 – Pivot Tables & Pivot Charts (3 Hours)

Topics
Create pivot tables
Grouping & filtering
Calculated fields
Pivot charts
🧪 Lab
Build pivot reports
📌 Assignment
Sales summary using pivot tables
💼 Job Scenario
Generate monthly business report

Module 6 – Power Query (etl In Excel) (2–3 Hours)

Topics
Import data from multiple sources
Data transformation
Merge & append queries
🧪 Lab
Combine multiple datasets
📌 Assignment
Create ETL workflow in Excel

Module 7 – Dashboard Design (3 Hours)

Topics
KPI dashboards
Slicers & timelines
Charts (bar, line, pie)
Interactive dashboards
🧪 Lab
Build dashboard
📌 Assignment
Create executive dashboard
💼 Job Scenario
Design dashboard for managementTopics
KPI dashboards
Slicers & timelines
Charts (bar, line, pie)
Interactive dashboards
🧪 Lab
Build dashboard
📌 Assignment
Create executive dashboard
💼 Job Scenario
Design dashboard for management

Capstone Projects

Project 1 – Sales Dashboard
Features
Revenue trends
Region-wise analysis
KPI tracking
Project 2 – Customer Data Analysis
Features
Customer segmentation
Purchase trends
Project 3 – Financial Reporting System
Features
Profit & loss analysis
Expense tracking
REAL-TIME JOB SCENARIOS
Clean messy data from Excel sheets
Generate reports using formulas
Create dashboards for management
Analyze sales and revenue trends
Automate repetitive tasks
ASSIGNMENTS STRUCTURE
Data cleaning assignment
Formula-based reporting
Pivot table analysis
Dashboard creation
Case study (Retail / Finance)

Interview Preparation

Important Topics
VLOOKUP vs INDEX MATCH
Pivot tables
Conditional formatting
Data cleaning techniques

SQL FOR DATA ANALYTICS – COMPLETE TRAINING SYLLABUS

⏱ Duration: 15 Hours🎯 Outcome: Job-ready SQL Data Analyst

🧭 📌 LEARNING ROADMAP

SQL Basics
↓
Filtering & Sorting
↓

Joins & Relationships
   ↓
Aggregation & Grouping
   ↓
Subqueries
   ↓
Advanced SQL (Window Functions, CTE)
   ↓
Real-Time Data Analysis
   ↓
Projects & Case Studies

Module 1 – Sql Fundamentals

Topics
What is Database & RDBMS Tables, Rows, Columns Primary Key, Foreign Key Introduction to SQL
Commands
SELECT
DISTINCT
Lab
Retrieve data from sample tables
📌 Assignment
Display all records from customer table

Module 2 – Filtering & Sorting

Topics
WHERE clause
AND, OR, NOT
BETWEEN, IN, LIKE
ORDER BY
🧪 Lab
Filter customer and sales data
📌 Assignment
Find customers from specific city
💼 Job Scenario
Extract region-based sales data

Module 3 – Joins & Table Relationships

Topics
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL JOIN
Self Join
🧪 Lab
Combine multiple tables📌 Assignment
Join customer and order tables
💼 Job Scenario
Analyze customer purchase behavior

Module 4 – Aggregation & Grouping

Topics
COUNT, SUM, AVG, MIN, MAX
GROUP BY
HAVING
🧪 Lab
Generate summary reports📌 Assignment
Total sales by region
💼 Job Scenario
Identify top-performing products

Module 5 – Subqueries & Nested Queries

Topics
Subqueries (Single-row, Multi-row)
Correlated subqueries
🧪 Lab
Write nested queries
📌 Assignment
Find customers with highest orders

Module 6 – Advanced Sql (5 Hours)

Topics
Window Functions:
RANK()
DENSE_RANK()
ROW_NUMBER()
CTE (Common Table Expressions)
CASE Statements
Views
🧪 Lab
Ranking and analytics queries
📌 Assignment
Top 5 customers by revenue
Job Scenario
Sales trend and ranking analysis

Module 7 – Real-time Data Analysis

Topics
Writing business queries
Data extraction for dashboards
KPI calculations
💼 Real-Time Scenarios
Customer churn analysis
Sales trend analysis
Revenue forecasting

Mini Projects

Project 1 – Sales Data Analysis
Tasks
Total revenue
Top products
Monthly trends
Project 2 – Customer Insights
Tasks
Top customers
Customer segmentation
Purchase patterns
Project 3 – Retail Business Case Study
Tasks
Inventory analysis
Profitability analysis
Region-wise sales

Assignments (sample)

Write 20 SQL queries for business scenarios
Create joins across 3 tables
Perform aggregation analysis
Solve real-time case studies

Job-oriented Scenarios

Find top 10 customers by revenue
Identify low-performing products
Analyze monthly sales growth
Detect duplicate records
Generate management reports

PYTHON FOR DATA ANALYTICS

⏱ Duration: 15 – 20 Hours
🎯 Outcome: Data Analyst / Junior Data Scientist

🧭 📌 LEARNING ROADMAP

Python Basics
   ↓
Data Structures
   ↓
NumPy (Numerical Analysis)
   ↓
Pandas (Data Analysis)
   ↓
Data Cleaning & Transformation
   ↓
EDA (Exploratory Data Analysis)
   ↓
Data Visualization
   ↓
Real-Time Case Studies
   ↓
Capstone Projects + Interview Prep

Module 1 – Python Fundamentals

Topics
Introduction to Python
Installation & Setup (Jupyter Notebook)
Variables & Data Types
Operators
Control Statements (if, loops) Functions
🧪 Lab
Write basic programs
📌 Assignment
Create a simple calculator
Find even/odd numbers
💼 Job Scenario
Automate small data processing tasks

Module 2 – Python Data Structures

Topics
Lists
Tuples
Sets
Dictionaries
🧪 Lab
Work with collections
📌 Assignment
Store and manipulate customer data

Module 3 – Numpy

Topics
Arrays
Array operations
Mathematical functions
🧪 Lab
Perform numerical operations
📌 Assignment
Analyze numerical dataset

Module 4 – Pandas (core Of Data Analytics)

Topics
Series & DataFrames
Reading data (CSV, Excel)
Data selection & filtering
Data cleaning:
Handling null values
Removing duplicates
Data transformation
🧪 Labs
Load and clean datasets
📌 Assignment
Clean messy sales dataset
💼 Job Scenario
Prepare raw business data for reporting

Module 5 – Data Analysis & EDA

Topics
Descriptive statistics
Grouping & aggregation
Correlation analysis
Feature engineering basics
🧪 Lab
Perform EDA
📌 Assignment
Analyze customer data
💼 Job Scenario
Identify trends in sales/customer data

Module 6 – Data Visualization

Tools
Matplotlib
Seaborn
Topics
Bar charts, line charts
Histograms
Heatmaps
🧪 Lab
Create visualizations
📌 Assignment
Visualize business KPIs
💼 Job Scenario
Present insights to stakeholders

Module 7 – Advanced Topics

Topics
Working with APIs
Basic automation scripts
Intro to machine learning (optional)
🧪 Lab
Fetch data from API

Module 8 – End-to-end Data Analytics Workflow

Topics
Data extraction
Data cleaning
Data analysis
Visualization

Capstone Projects (very Important)

Project 1 – Sales Data Analysis
Features
Revenue trends
Product performance
Monthly analysis
👉 Tools: Pandas + Visualization

Project 2 – Customer Segmentation Analysis
Features
Customer grouping
Purchase patterns
Insights generatio
👉 Tools: Pandas + EDA

Project 3 – Retail Data Analytics System
Features
Data cleaning
Analysis
Visualization
👉 Tools: Python End-to-End

Project 4 – Real-Time Business Case Study
Features
KPI analysis
Business insights
Decision-making

Real-time Job Scenarios

Clean messy datasets
Analyze sales trends
Identify top customers
Automate reports using Python
Perform customer segmentation
Generate business insights

Assignments Structure

Python basics problems
Pandas data cleaning tasks
EDA assignment
Visualization assignment
Case study analysis

POWER BI

⏱ Duration: 30 Hours🎯 Outcome: Power BI Developer / Data Analyst

🧭 📌 LEARNING ROADMAP
Power BI Basics
   ↓
Data Loading (Excel/SQL)
   ↓
Power Query (Data Cleaning)
   ↓
Data Modeling (Relationships)
   ↓
DAX (Basic → Advanced)
   ↓
Visualization & Dashboarding
   ↓
Advanced Features (RLS, Optimization)
   ↓
Deployment (Power BI Service)
   ↓
Capstone Projects + Interview Prep

Module 1 – Introduction To Business Intelligence & Power Bi (2 Hours)

Topics
BI Concepts & Importance
Power BI Ecosystem:
Desktop
Service
Mobile
Installation & Interface Overview
🧪 Lab
Install Power BI & explore UI
Assignment
Identify 5 business dashboards used in industry

Module 2 – Data Connection & Power Query (5 Hours)

Data Sources
Excel, CSV, SQL Server, Web
Data Cleaning
Remove duplicates
Handle missing values
Change data types
Data Transformation
Merge queries
Append queries
Pivot / Unpivot
🧪 LABS
Clean messy dataset
Combine multiple files
📌 ASSIGNMENT
Prepare cleaned dataset for reporting
💼 JOB SCENARIO
Combine sales data from multiple branches

Module 3 – Data Modeling (4 Hours)

Topics
Relationships (1:M, M:M)
Star Schema (very important for interviews)
Fact vs Dimension tables
Cardinality & cross-filter direction
🧪 LAB
Create star schema model
📌 ASSIGNMENT
Design data model for retail business
💼 JOB SCENARIO
Build enterprise-level reporting model

Module 4 – Dax (core + Advanced) (7 Hours)

Basics
Calculated Columns vs Measures
SUM, COUNT, AVERAGE
Intermediate
IF, SWITCH
FILTER, CALCULATE
Advanced (Very Important)
Time Intelligence:
YTD, MTD, YoY
ALL, ALLEXCEPT
Context (Row vs Filter Context)
🧪 LABS
Create KPIs
Build dynamic calculations
📌 ASSIGNMENT
Revenue growth & comparison metrics
💼 JOB SCENARIO
Create business KPIs for management dashboards

MODULE 5 – DATA VISUALIZATION & DASHBOARD DESIGN (5 Hours)

Topics
Charts:
Bar, Line, Area
Table, Matrix
KPI Cards
Slicers & Filters
Drill-down / Drill-through
Tooltip & bookmarks
🧪 LABS
Build interactive dashboard
📌 ASSIGNMENT
Create executive dashboard
💼 JOB SCENARIO
Design dashboard for CXO-level reporting

Module 6 – Advanced Power Bi Features (3–4 Hours)

Topics
Row-Level Security (RLS)
Performance optimization
Query reduction techniques
Custom visuals
🧪 LAB
Implement RLS
📌 ASSIGNMENT
Optimize slow dashboard

Module 7 – Power Bi Service & Deployment (2–3 Hours)

Topics
Publishing reports
Workspace management
Dashboard sharing
Scheduled refresh
🧪 LAB
Publish report & share

Module 8 – End-to-end Data Analytics Flow (2 Hours)

Topics
SQL → Data extraction
Power Query → Cleaning
Power BI → Visualization
Storytelling

Capstone Projects (very Important)

Project 1 – Sales Analytics Dashboard

Tools
Excel + Power BI
Features
Revenue trends
Region-wise performance
KPI tracking
👉 Outcome: Business decision dashboard

Project 2 – Financial Analytics Dashboard

Tools
Power BI
Features
Profit & loss analysis
Budget vs actual
Expense tracking

👉 Outcome: CFO-level dashboard

Project 3 – HR Analytics Dashboard

Tools
Power BI
Features
Employee attrition
Hiring trends
Department performance👉 Outcome: HR decision dashboard

Project 4 – End-to-end Business Intelligence System

Tools
SQL + Power BI
Features
Data extraction
Data modeling
Dashboard
👉 Outcome: Complete BI solution

Real-time Job Scenarios

Build dashboards for management reporting
Analyze sales and revenue trends
Create KPIs for business decisions
Handle messy data from multiple sources
Implement security for different users
Optimize slow dashboards

Assignment Structure

Data cleaning assignment (Power Query)
Data modeling assignment
DAX calculations assignment
Dashboard design assignment
Case study: Retail / Finance

Job-Ready Program (Bonus)

Resume Preparation
ATS-friendly resume
Project description
Portfolio
GitHub / Power BI portfolio
Interview Preparation
DAX interview questions
Scenario-based questions

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Data Analytics Combo Course Certification With Training In Bangalore

Q: DATA ANALYTICS COMBO PROGRAM (0 → JOB READY)

⏱ Duration: 60 to 80 Hours 🎯 Outcome: Job-ready Data Analyst / BI Analyst ROADMAP (Learning Journey) FOUNDATION ↓ Excel Basics → Advanced Excel → Dashboards ↓ SQL Basics → Joins → Advanced SQL → Real Queries ↓ Python Basics → Pandas → EDA → Visualization ↓ Power BI Basics → Data Modeling → DAX → Dashboards ↓ END-TO-END PROJECTS ↓ JOB PREPARATION (Resume + Interview + Case Studies)

Q: DATA ANALYTICS FOUNDATION

⏱ Duration: 6–10 Hours 🎯 Outcome: Strong conceptual understanding of Data Analytics ROADMAP (Learning Journey) What is Data? ↓ Types of Data ↓ Data Analytics Concepts ↓ Data Lifecycle ↓ Business Understanding ↓ Basic Statistics ↓ Tools Overview ↓ Case Studies & Problem Solving

Q: Module 1 – Introduction To Data & Analytics (1.5 Hours)

Topics What is Data? Types of Data: Structured Unstructured Semi-structured What is Data Analytics Importance of data in business 📌 Assignment Identify types of data used in: Banking Retail Healthcare

Q: Module 2 – Types Of Data Analytics (1 Hour)

Topics Descriptive Analytics (What happened?) Diagnostic Analytics (Why it happened?) Predictive Analytics (What will happen?)Prescriptive Analytics (What should be done?) 💼 Job Scenario Analyze why sales dropped in a company

Q: Module 3 – Data Analytics Lifecycle (1 Hour)

Topics Data Collection Data Cleaning Data Processing Data Analysis Data Visualization Decision Making 📌 Assignment Map lifecycle for a real-world example

Q: Module 4 – Business Understanding (1 Hour)

Topics Business problems vs technical problems Performance Indicators) Metrics: Revenue Profit Customer retention 💼 Job Scenario Define KPIs for a retail company

Q: Module 5 – Basic Statistics For Data Analytics (1.5–2 Hours)

Topics Mean, Median, Mode Variance & Standard Deviation Correlation Distribution basics 🧪 Lab Calculate statistics using sample data 📌 Assignment Analyze dataset using statistical measures

Q: Module 6 – Problem Solving & Analytical Thinking (1 Hour)

Topics Breaking business problems Logical thinking Data-driven decision making 💼 Job Scenario Identify reasons for customer churn

Q: Module 7 – Tools Overview (1 Hour)

Topics Excel (data cleaning & reporting) SQL (data extraction) Python (data analysis) Power BI (visualization)👉 No deep learning here—just awareness

Q: Module 8 – Case Studies & Mini Project (1–2 Hours)

Mini Project – Sales Data Analysis (Conceptual) Tasks Identify trends Define KPIs Suggest business improvements

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