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How to become Business Analytics?
How to become Business Analytics?
Curriculum
10 Sections
172 Lessons
10 Weeks
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Collapse all sections
Introduction to Business Analytics
23
1.1
1- Definition and Scope of Business Analytics
1.2
Business Analytics (BA)
1.3
Data Collection and Integration
1.4
Data Cleaning and Preprocessing
1.5
Descriptive Analytics
1.6
Data Exploration and Visualization
1.7
Predictive Analytics
1.8
Prescriptive Analytics
1.9
Optimization and Simulation
1.10
Text and Sentiment Analysis
1.11
Data Governance and Security
1.12
Business Intelligence (BI)
1.13
2- Importance of Data-Driven Decision-Making
1.14
Informed Decision-Making
1.15
Competitive Advantage
1.16
Cost Efficiency
1.17
Customer Understanding
1.18
Risk Management
1.19
Strategic Planning
1.20
Continuous Improvement
1.21
Measurable Outcomes
1.22
Case Studies
1.23
Rest
Key Concepts in Business Analytics
14
2.1
Data Collection and Storage
2.2
Data Cleaning and Preprocessing
2.3
Descriptive Analytics
2.4
Predictive Analytics
2.5
Prescriptive Analytics
2.6
Business Intelligence (BI)
2.7
Data Governance and Security
2.8
Big Data Analytics
2.9
Key Performance Indicators (KPIs)
2.10
Data-driven Decision Making
2.11
Agile Analytics
2.12
Ethics in Analytics
2.13
Case Studies
2.14
Rest
Tools and Technologies
4
3.1
Introduction to Analytics Tools
3.2
Overview of Business Intelligence Tools
3.3
Emerging Trends in Analytics Technologies
3.4
Case Studies
Data Types and Sources
17
4.1
1- Data Types
4.2
Structured Data
4.3
Unstructured Data
4.4
2- Data Sources
4.5
Internal Data Sources
4.6
External Data Sources
4.7
3- Data Collection Methods
4.8
Surveys and Questionnaires
4.9
Observation
4.10
Interviews
4.11
Sensor Data
4.12
Web Scraping
4.13
Transaction Data
4.14
Social Media Monitoring
4.15
Secondary Data Analysis
4.16
Case Studies
4.17
Rest
Cleaning and Transformation
25
5.1
1- Dealing with Missing Data
5.2
Identification of Missing Values
5.3
Removal of Missing Values
5.4
Imputation of Missing Values
5.5
Evaluate the Impact
5.6
Documentation
5.7
Iterative Process
5.8
2- Outlier Detection and Treatment
5.9
Identification
5.10
Removal
5.11
Transformation
5.12
Winsorizing
5.13
3- Data Normalization and Standardization
5.14
Normalization (Min-Max Scaling)
5.15
Standardization (Z-score Normalization)
5.16
Robust Scaling (IQR Scaling)
5.17
4- Feature Engineering Techniques
5.18
Binning/Discretization
5.19
One-Hot Encoding
5.20
Polynomial Features
5.21
Interaction Terms
5.22
Feature Scaling
5.23
Dimensionality Reduction
5.24
Case Studies
5.25
Rest
Exploratory Data Analysis (EDA) with SPSS
16
6.1
Introduction
6.2
1- Basic Statistical Analysis
6.3
Descriptive Statistics
6.4
Frequency Distribution
6.5
Central Tendency and Dispersion
6.6
Correlation Analysis
6.7
Inferential Statistics
6.8
2- Data Visualization Techniques:
6.9
Histograms
6.10
Box Plots
6.11
Scatter Plots
6.12
Bar Charts
6.13
Heatmaps
6.14
Pie Charts
6.15
Line Chart
6.16
When should I use them?
Statistical analysis with SPSS
24
7.1
1- Descriptive Statistics
7.2
1-1 Measures of Central Tendency and Dispersion
7.3
Mean, Median, and Mode
7.4
Variability Measures
7.5
1-2 Frequency Distributions
7.6
Histograms
7.7
Percentiles and Quartiles
7.8
1-3 Probability Distributions
7.9
Normal Distribution
7.10
2- Inferential Statistics
7.11
2-1 Hypothesis Testing
7.12
Formulate Hypotheses
7.13
Conduct Statistical Tests
7.14
2-2 Confidence Intervals
7.15
3-1 Confidence Intervals
7.16
Confidence Interval for Mean
7.17
4-1 Regression Analysis
7.18
Simple Linear Regression
7.19
Multiple Regression
7.20
5-1 Correlation Analysis
7.21
Pearson Correlation
7.22
SPSS Functionality
7.23
When should I use them?
7.24
Case Studies
Prescriptive Analytics with SPSS
8
8.1
1- Optimization Techniques
8.2
Linear and Nonlinear Programming
8.3
Integer Programming
8.4
Goal Programming
8.5
2- Simulation Modeling
8.6
Monte Carlo Simulation
8.7
System Dynamics Modeling
8.8
Case Studies
Predictive Modeling with SPSS
9
9.1
1- Types of Predictive Models
9.2
Regression Models
9.3
Classification Models
9.4
2- Model Selection and Evaluation Criteria
9.5
Model Selection
9.6
Evaluation Criteria
9.7
When should I use them?
9.8
Case Studies
9.9
Rest
Business Intelligence and Reporting
33
10.1
1- Dashboard and Visualization Design
10.2
1-1 Principles of Effective Data Visualization
10.3
Clarity and Simplicity
10.4
Relevance
10.5
Consistency
10.6
Interactivity
10.7
Storytelling
10.8
Use of Appropriate Visualization Types
10.9
Color Usage
10.10
Data Accuracy and Precision
10.11
1-2 Dashboard Development and Best Practices
10.12
Define Objectives
10.13
User-Centric Design
10.14
Performance Optimization
10.15
Responsive Design
10.16
Feedback Mechanisms
10.17
Regular Updates
10.18
Testing and Validation
10.19
2- Reporting Tools
10.20
2-1 Introduction to Reporting Tools
10.21
Tableau
10.22
Power BI (Microsoft Power BI)
10.23
2-2 Creating Dynamic and Interactive Reports
10.24
Data Connection
10.25
Report Design
10.26
Interactivity
10.27
Data Refresh and Automation
10.28
Collaboration and Sharing
10.29
Security and Access Control
10.30
Documentation and Training
10.31
Case Studies
10.32
Rest
10.33
Business Analytics
30 Minutes
10 Questions
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