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How to make the right Business Decisions?
How to make the right Business Decisions?
Curriculum
19 Sections
337 Lessons
10 Weeks
Expand all sections
Collapse all sections
Introduction to Business Decision Making
3
1.1
Importance of Decision Making in Business
1.2
Key Concepts
1.3
Methodologies
Introduction to Business Analytics
23
2.1
1- Definition and Scope of Business Analytics
2.2
Business Analytics (BA)
2.3
Data Collection and Integration
2.4
Data Cleaning and Preprocessing
2.5
Descriptive Analytics
2.6
Data Exploration and Visualization
2.7
Predictive Analytics
2.8
Prescriptive Analytic
2.9
Optimization and Simulation
2.10
Text and Sentiment Analysis
2.11
Data Governance and Securit
2.12
Business Intelligence (BI)
2.13
2- Importance of Data-Driven Decision-Making
2.14
Informed Decision-Making
2.15
Competitive Advantage
2.16
Cost Efficiency
2.17
Customer Understanding
2.18
Risk Management
2.19
Strategic Planning
2.20
Continuous Improvement
2.21
Measurable Outcomes
2.22
Case Studies
2.23
Rest
Key Concepts in Business Analytics
14
3.1
Data Collection and Storage
3.2
Data Cleaning and Preprocessing
3.3
Descriptive Analytics
3.4
Predictive Analytics
3.5
Prescriptive Analytics
3.6
Business Intelligence (BI)
3.7
Data Governance and Security
3.8
Big Data Analytics
3.9
Key Performance Indicators (KPIs)
3.10
Data-driven Decision Making
3.11
Agile Analytics
3.12
Ethics in Analytics
3.13
Case Studies
3.14
Rest
Tools and Technologies
4
4.1
Introduction to Analytics Tools
4.2
Overview of Business Intelligence Tools
4.3
Emerging Trends in Analytics Technologies
4.4
Case Studies
Data Types and Sources
17
5.1
1- Data Types
5.2
Structured Data
5.3
Unstructured Data
5.4
2- Data Sources
5.5
Internal Data Sources
5.6
External Data Sources
5.7
3- Data Collection Methods
5.8
Surveys and Questionnaires
5.9
Observation
5.10
Interviews
5.11
Sensor Data
5.12
Web Scraping
5.13
Transaction Data
5.14
Social Media Monitoring
5.15
Secondary Data Analysis
5.16
Case Studies
5.17
Rest
Cleaning and Transformation
25
6.1
1- Dealing with Missing Data
6.2
Identification of Missing Values
6.3
Removal of Missing Values
6.4
Imputation of Missing Values
6.5
Evaluate the Impact
6.6
Documentation
6.7
Iterative Process
6.8
2- Outlier Detection and Treatment
6.9
Identification
6.10
Removal
6.11
Transformation
6.12
Winsorizing
6.13
3- Data Normalization and Standardization
6.14
Normalization (Min-Max Scaling)
6.15
Standardization (Z-score Normalization)
6.16
Robust Scaling (IQR Scaling)
6.17
4- Feature Engineering Techniques
6.18
Binning/Discretization
6.19
One-Hot Encoding
6.20
Polynomial Features
6.21
Interaction Terms
6.22
Feature Scaling
6.23
Dimensionality Reduction
6.24
Case Studies
6.25
Rest
Predictive Modeling with SPSS
9
7.1
1- Types of Predictive Models
7.2
Regression Models
7.3
Classification Models
7.4
2- Model Selection and Evaluation Criteria
7.5
Model Selection
7.6
Evaluation Criteria
7.7
When should I use them?
7.8
Case Studies
7.9
Rest
Exploratory Data Analysis (EDA) with SPSS
16
8.1
Introduction
8.2
1- Basic Statistical Analysis
8.3
Descriptive Statistics
8.4
Frequency Distribution
8.5
Central Tendency and Dispersion
8.6
Correlation Analysis
8.7
Inferential Statistics
8.8
2- Data Visualization Technique
8.9
Histograms
8.10
Box Plots
8.11
Scatter Plots
8.12
Bar Charts
8.13
Heatmaps
8.14
Pie Charts
8.15
Line Chart
8.16
When should I use them?
Statistical analysis with SPSS
24
9.1
1- Descriptive Statistics
9.2
Measures of Central Tendency and Dispersion
9.3
Mean, Median, and Mode
9.4
Variability Measures
9.5
1-2 Frequency Distributions
9.6
Histograms
9.7
Percentiles and Quartiles
9.8
1-3 Probability Distributions
9.9
Normal Distribution
9.10
2- Inferential Statistics
9.11
2-1 Hypothesis Testing
9.12
Formulate Hypotheses
9.13
Conduct Statistical Tests
9.14
2-2 Confidence Intervals
9.15
3-1 Confidence Intervals
9.16
Confidence Interval for Mean
9.17
4-1 Regression Analysis
9.18
Simple Linear Regression
9.19
Multiple Regression
9.20
5-1 Correlation Analysis
9.21
Pearson Correlation
9.22
SPSS Functionality
9.23
When should I use them?
9.24
Case Studies
Prescriptive Analytics with SPSS
8
10.1
1- Optimization Techniques
10.2
Linear and Nonlinear Programming
10.3
Integer Programming
10.4
Goal Programming
10.5
2- Simulation Modeling
10.6
Monte Carlo Simulation
10.7
System Dynamics Modeling
10.8
Case Studies
Business Intelligence and Reporting
33
11.1
1- Dashboard and Visualization Design
11.2
1-1 Principles of Effective Data Visualization
11.3
Clarity and Simplicity
11.4
Relevance
11.5
Consistency
11.6
Interactivity
11.7
Storytelling
11.8
Use of Appropriate Visualization Types
11.9
Color Usage
11.10
Data Accuracy and Precision
11.11
1-2 Dashboard Development and Best Practices
11.12
Define Objectives
11.13
User-Centric Design
11.14
Performance Optimization
11.15
Responsive Design
11.16
Feedback Mechanisms
11.17
Regular Updates
11.18
Testing and Validation
11.19
2- Reporting Tools
11.20
2-1 Introduction to Reporting Tools
11.21
Tableau
11.22
Power BI (Microsoft Power BI)
11.23
2-2 Creating Dynamic and Interactive Reports
11.24
Data Connection
11.25
Report Design
11.26
Interactivity
11.27
Data Refresh and Automation
11.28
Collaboration and Sharing
11.29
Security and Access Control
11.30
Documentation and Training
11.31
Case Studies
11.32
Rest
11.33
Contact Form
Decision Making Models
34
12.1
1- Rational Decision Making Model
12.2
Problem Identification Module
12.3
Alternative Generation Module
12.4
Alternative Evaluation Module
12.5
Alternative Selection Module
12.6
Implementation Module
12.7
Monitoring and Evaluation Module
12.8
2- Bounded Rationality
12.9
Constraint Recognition Module
12.10
Satisficing Objective Module
12.11
Heuristic Utilization Module
12.12
Risk Assessment Module
12.13
Adaptive Learning Module
12.14
3- Intuitive Decision Making
12.15
Intuition Recognition Module
12.16
Expertise Utilization Module
12.17
Situation Assessment Module
12.18
Bias Awareness Module
12.19
Supplementary Rational Analysis Module
12.20
4- Normative Decision Theory
12.21
Assumption Identification Module
12.22
Criterion Specification Module
12.23
Logical Coherence Assessment Module
12.24
Rationality Evaluation Module
12.25
Decision Prescription Module
12.26
5- Behavioral Decision Making
12.27
Psychological Factors Identification Module
12.28
Social Influence Recognition Module
12.29
Cognitive Bias Awareness Module
12.30
Emotional Influence Module
12.31
Heuristic Utilization Module
12.32
Bias Mitigation Strategies Module
12.33
Case Stdies
12.34
Rest
Risk Management and Uncertainty
33
13.1
1- Identifying Risks
13.2
Threat Recognition Module
13.3
Source Analysis Module
13.4
Environmental Scan Module
13.5
Risk Cataloging Module
13.6
Risk Impact Assessment Module
13.7
Risk Probability Assessment Module
13.8
Risk Interdependency Analysis Module
13.9
2- Risk Assessment
13.10
Probability Assessment Module
13.11
Source Analysis Module:
13.12
Environmental Scan Module
13.13
Risk Cataloging Module
13.14
Risk Impact Assessment Module
13.15
Scenario Analysis Module
13.16
3- Risk Analysis Techniques
13.17
Quantitative Analysis Module
13.18
Qualitative Analysis Module
13.19
Scenario Analysis Module
13.20
Sensitivity Analysis Module
13.21
4- Risk Mitigation Strategies
13.22
Avoidance Strategy Module
13.23
Reduction Strategy Module
13.24
Transfer Strategy Module
13.25
Acceptance Strategy Module
13.26
5- Monitoring and Review
13.27
Effectiveness Tracking Module
13.28
Risk Identification Module
13.29
Adaptation and Adjustment Module
13.30
Communication and Reporting Module
13.31
Continuous Improvement Module
13.32
Case Studies
13.33
Rest
Cost-Benefit Analysis
36
14.1
1- Identifying Costs and Benefits
14.2
Direct Costs Identification
14.3
Indirect Costs Identification
14.4
Benefit Identification
14.5
Monetary Valuation
14.6
Non-Monetary Valuation
14.7
2- Quantifying Costs and Benefits
14.8
Direct Cost Quantification
14.9
Indirect Cost Quantification
14.10
Benefit Quantification
14.11
Complex Analysis and Estimation Techniques
14.12
3- Discounting Future Costs and Benefits
14.13
Time Value of Money
14.14
Discount Rate
14.15
Discounting Future Costs and Benefits
14.16
Inflation Adjustment
14.17
Sensitivity Analysis
14.18
Net Present Value (NPV)
14.19
4- Calculating Net Present Value (NPV)
14.20
Identifying Cash Flows
14.21
Discounting Future Cash Flows
14.22
Summing Present Values
14.23
Interpreting NPV
14.24
Interpreting NPV
14.25
Sensitivity Analysis
14.26
5- Sensitivity Analysis
14.27
Identifying Key Variables
14.28
Defining Ranges for Variation
14.29
Selecting the Sensitivity Analysis Method
14.30
Conducting One-Way Sensitivity Analysis
14.31
Conducting Multi-Way Sensitivity Analysis
14.32
Interpreting Results
14.33
Scenario Analysis
14.34
Risk Mitigation and Decision-Making
14.35
Case Studies
14.36
Rest
Decision Trees and Scenario Analysis
2
15.1
Decision Trees
15.2
Scenario Analysis
Game Theory and Strategic Decision Making
10
16.1
Player
16.2
Strategies
16.3
Payoffs
16.4
Information
16.5
Equilibrium
16.6
Types of Games
16.7
Applications in Strategic Decision Making
16.8
Nash Equilibrium
16.9
Case Studies
16.10
Rest
Ethical Decision Making
10
17.1
Identifying Ethical Issues
17.2
Stakeholder Analysis
17.3
Ethical Frameworks
17.4
Legal and Regulatory Compliance
17.5
Transparency and Accountability
17.6
Consider Long-Term Consequences
17.7
Seek Guidance and Consultation
17.8
Continuous Learning and Improvement
17.9
Case Studies
17.10
Rest
Implementation and Monitoring
12
18.1
Clear Communication
18.2
Action Planning
18.3
Resource Allocation
18.4
Change Management
18.5
Monitoring and Evaluation
18.6
Feedback Loops
18.7
Adaptability and Flexibility
18.8
Continuous Improvement
18.9
Performance Management
18.10
Celebrate Milestones
18.11
Case Studies
18.12
Rest
Decision Making in Cross-functional Teams
25
19.1
1- Benefits of Cross-functional Teams
19.2
Diverse Perspectives
19.3
Specialized Expertise
19.4
Improved Decision Making
19.5
Enhanced Communication
19.6
2- Challenges of Decision Making in Cross-functional Teams
19.7
Conflicting Priorities
19.8
Communication Barriers
19.9
Differing Viewpoints
19.10
3- Dynamics of Decision Making in Cross-functional Teams
19.11
Power Dynamics
19.12
Group Dynamics
19.13
Individual Personalities
19.14
4- Techniques for Effective Collaboration
19.15
Active Listening
19.16
Consensus-Building
19.17
Conflict Resolution
19.18
Tools and Methodologies
19.19
5- Decision-making Techniques for Cross-functional Teams
19.20
Multi-voting
19.21
Decision Matrices
19.22
RAPID Framework (Responsible, Accountable, Consulted, Informed)
19.23
Case Studies
19.24
Rest
19.25
Business Decisions
30 Minutes
10 Questions
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