Duration: Hours

Category:

## Description

#### Objectives :

1. The introduction and importance of Business Intelligence.
2. Practical explanation and live implementation of all important functions of BI and statistical concepts using Excel, Power BI, and Tableau.
4. Basic concepts of Statistics required for understanding Business Intelligence.
5. Practical implementation of statistical concepts in Excel.
6. Complete guide and tutorial of Power BI from scratch to Publishing Dashboard.
7. Mini projects and quizzes with solutions to reinforce your learning skills of Power BI.
8. Complete guide and tutorial of Tableau from installation to Dashboard.
9. Practical exercises to enhance the learning of Tableau.
10. Mini project of Regression Analysis using Excel.
11. Segregated modules to learn fundamental concepts of Statistics along with explanation and practical implementation using exercises.
12. Power BI Project: Sales Dashboard
13. Tableau Project: Customer Analysis

#### Requirements

a). Basic understanding of Microsoft Excel.
b). An elementary understanding of data science.
c). A willingness to learn and practice.

#### a). Introduction of BI:

1. Why BI?

2. Applications of BI

3. Introduction to the Course Instructor

4. Introduction to the Course and Mini-Projects

5. BI Project Overview

#### b). Types of Business Intelligence Tools and Applications:

2. Online Analytical processing

3. Mobile BI

4. Real-time BI

5. Operation Intelligence

6. Open-Source BI

7. Embedded BI

8. Collaborative BI

9. Location Intelligence

10. Business intelligence vendors and market

#### c). Statistics Overview

1. Welcome to the Statistics Course

2. Descriptive Statistics

2.1 Data Types

2.2 Level of Measurement

2.3 Numerical and Categorical Variables

2.4 Scatter Plot, Cross Table, and Histogram

2.5 Mode, Median, and Mean

2.6 Coefficient of Variation, Standard Deviation, and Variance

2.7. Skewness, Covariance, and Correlation

3. Inferential Statistics

3.1. What Is Inferential Statistics?

3.2. Distribution, Normal Distribution, and Standard Normal Distribution

3.3. What Is a Standard Error?

3.4. Estimators and Estimates

3.5. What Is the Central Limit Theorem?

4. Overview of Confidence Intervals

4.1. What Are Confidence Intervals?

4.2. Clarifications and Margin of Error

4.3. Z-Score

4.4. T-Score and Studentâ€™s T Distribution

4.5. Dependent Samples, Two Mean Test

4.6. Independent Samples, Two Mean Test

5. Hypothesis Testing

5.1. Null vs. Alternative Hypothesis

5.2. Error Types

5.3. Rejection Region, Significance Level, and P-Value

5.4. Population Variance Known

5.5. Population Variance Unknown

5.6. Dependent and Independent Samples for Mean Test

#### d). Statistical Practices Using Excel

1. Descriptive Statistics

2. Measures of Central Tendency

4. Data Visualization

5. Correlation

6. Paired Sample T-Test

7. T-Test for Equal and Unequal Variances

8. Confidence Interval

9. Hypothesis Testing

10. Example Project: Regression Analysis

#### e). Power BI

1. Introduction to Power BI

1.1 Power BI Overview

1.2 Installation

2. Data Sources

2.1 Introduction to Data Sources

2.2 Query Editor

2.3 Importing Files

2.4 Data Modeling

2.5 Lookup Data Tables

2.6 Active vs. Inactive Relationships

2.7 Roles

2.8 Refreshing Data and Hierarchies

3. Data Modeling

3.1 Introduction

3.2 DAX

3.3 Calculated Columns

3.4 Measures

3.5 Complex Functions

3.6 Hybrid Measures

3.7 Star Schema

3.8 Snowflake Schema

3.9 Filter flow

3.10 Bi-directional Cross-filtering

3.11 Time Intelligence

3.12 Defining Day and Date Function

4. Design and Interactive Reports

4.3 Applying Basic Filtering

4.4 Slice and Dice

4.5 Apply Formatting

5. Dashboard

5.1 Add Data to the Dashboard

5.2 Format the Dashboard

5.3 Publish Dashboard to Workspace

6. Career Opportunities with Power BI

#### f). Tableau

1. Introduction to Tableau

1.1. Tableau Overview

1.2. Installation

2. Tableau Fundamentals: Data Sources, First Bar Chart Graph

2.1. Exciting Challenge

2.2. Excel / CSV File Connection with Tableau

2.4. Establish Fields

2.5. Addition of Colors, Labels, and Formatting

2.6. Final Worksheet Exportation

3. Overview of Different Terms

3.1. Overview

3.2. Understand the Extracted Data

3.3. Knowledge of Aggregation, Granularity, and Time-Series

3.4. Level of Detail

3.5. Working with Charts and Filter

4. Overview of First Dashboard, Maps, and Scatter Plots

4.1. Overview

4.2. Joins and Relationship

4.3. Data Joining

4.4. Map Creation

4.5. Scatter Plot Creation

4.6. First Dashboard Creation with Highlighting and Filters

5. Overview of Dual-axis Chart, Joining, Relationship, and Blending

5.1. Overview

5.2. Working with Joins

5.3. Joining with Different Conditions, i.e., Multiple Fields and Duplicate Values

5.4. Difference Between Blending and Joining Data

5.5. Working on Blending Data

5.6. Creation of Dual Axis Chart

5.7. Understanding of Calculated Fields

5.8. Another Exciting Challenge with a Data Set

5.9. Model Dataset

5.10. Understanding of Relationship Data

6. Overview of New Dashboard

6.1. Overview

6.2. Dataset

6.3. Understanding of Mapping

6.4. Table Calculations: For Age

6.5. Table Calculations of Bins and Distribution

6.6. Power Parameters

6.7. Treemap map chart

6.8. New Dashboard

7. Updated Way of Data Preparation

7.1. Overview

7.2. Data Format and Data Interpreter

8. Overview of New Design Feature and Many More

#### g). Real-Time Projects

1. Sales Dashboard Using Power BI (Dashboard)

2. Customer Analysis Using Tableau (Dashboard)

#### h). Career Development

1. Preparing for the Interview