BUSINESS ANALYST COURSE IN FARIDABAD

Module 1: Introduction to Business Analytics

• Overview of Business Analytics
• Evolution of Business Analytics
• Classification of Analytics

Module 2: Introduction to Data Visualization

• Principles of Effective Data Visualization
• Visual Plots and Charts
• Data Collection Methods and Techniques

Module 3: Applications of Business Analytics

• Industry Applications
• Business Analyst Roles

Module 1: Statistics Fundamentals

• Descriptive Statistics
• Normal Distribution and Empirical Rule
• Standardisation and z-score
• Correlation among variables

Module 2: Exploratory Data Analysis

• Use of distributions
• Exploratory Data Analysis (EDA) Techniques

Module 1: Pivot Table in MS Excel

• Pivot Table
• Comparison Analysis
• Variance Analysis and Frequency Analysis
• Ranking Analysis

Module 2: Visual Analytics in MS Excel

• Pareto Rule and Pareto Analysis
• Time Series and Trend Analysis

Module 1: Tableau Fundamentals

• Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

Module 2: POWER-BI Basics

• Power BI Introduction
• Basic Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX

Module 4: Data Transformation Techniques

• Exploring Query Editor
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

Module 5: Connecting to Various Data Sources

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

Module 1: Machine Learning Introduction

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification and Regression
• Supervised Vs Unsupervised

Module 2: ML Algorithm – Linear Regression

• Introduction to Linear Regression
• How It Works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

Module 3: ML Algorithm – Logistic Regression

• Introduction to Logistic Regression; Classification & Sigmoid Curve
• Hands-on Logistic Regression with ML Tool

Module 4: ML Algorithm – K-Means Clustering

• K-Means Clustering (Unsupervised): How It Works
• Hands-on K-Means Clustering

Module 5: ML Algorithm – Decision Tree

• Decision Tree and How It Works
• Hands-on Decision Tree with ML Tool

Module 6: ML Algorithm – Naive Bayes

• Bayes' Theorem
• How It Works: Naive Bayes for Text Classification
• Hands-on Naive Bayes with ML Tool

Module 7: ML Algorithm – Support Vector Machine (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Hands-on SVM with ML Tool

Module 8: Artificial Neural Network (ANN)

• Introduction to ANN
• How It Works: Backpropagation and Gradient Descent
• Hands-on ANN with ML Tool
• Enable microphone access in Settings

Module 1: Business Analytics Overview

• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model

Module 2: Spreadsheet Applications for Business Insights

• Advanced Functions: IF, INDEX-MATCH, and Nested Formulas
• Data Cleaning and Preparation: Removing Duplicates, Text-to-Columns, and Data Validation
• VLOOKUP, XLOOKUP, and Dynamic Array Functions

Module 3: Optimization for Business Scenarios

• Solver Applications (e.g., Cost Minimization, Resource Allocation)
• Predictive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Case Study: WonderZon Network Optimization
• Assignment: KERC Inc. – Optimum Manufacturing Quantity

Module 4: Decision Modeling

• Predictive Analytics with High Uncertainty (Monte Carlo Simulation)
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study: Supplier Decision Modeling – Kickathlon Sports Retailer

Module 5: Predictive Analytics with Regression

• Mathematics Behind Linear Regression
• Case Study: Sales Promotion Decision with Regression Analysis
• Hands-on Regression Modeling in Excel