What I learn from this course?
Participants in this Business Analytics course will acquire a foundational understanding of the field, starting with an overview of its importance in decision-making and the evolution of analytics. The course progresses to cover fundamental data analysis skills, including data types, sources, and exploratory data analysis. It then delves into statistical analysis, introducing concepts like probability, inferential statistics, hypothesis testing, and regression analysis. Participants will also learn the significance of data visualization, tools for visualization, and design principles for effective communication through case studies. Additionally, the course covers predictive analytics, business intelligence, data warehousing, big data analytics, decision support systems, and addresses ethical and legal considerations in analytics. The culmination involves practical applications through case studies, group projects, and a capstone project, ensuring participants are well-equipped with theoretical knowledge and practical skills for the field of business analytics. Assessment methods include quizzes, exams, assignments, group project participation, and evaluation of the final capstone project.
* To find out the content of the course, click on “Curriculum” above.
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Curriculum
- 10 Sections
- 172 Lessons
- 10 Weeks
- Introduction to Business Analytics23
- 1.11- Definition and Scope of Business Analytics
- 1.2Business Analytics (BA)
- 1.3Data Collection and Integration
- 1.4Data Cleaning and Preprocessing
- 1.5Descriptive Analytics
- 1.6Data Exploration and Visualization
- 1.7Predictive Analytics
- 1.8Prescriptive Analytics
- 1.9Optimization and Simulation
- 1.10Text and Sentiment Analysis
- 1.11Data Governance and Security
- 1.12Business Intelligence (BI)
- 1.132- Importance of Data-Driven Decision-Making
- 1.14Informed Decision-Making
- 1.15Competitive Advantage
- 1.16Cost Efficiency
- 1.17Customer Understanding
- 1.18Risk Management
- 1.19Strategic Planning
- 1.20Continuous Improvement
- 1.21Measurable Outcomes
- 1.22Case Studies
- 1.23Rest
- Key Concepts in Business Analytics14
- 2.1Data Collection and Storage
- 2.2Data Cleaning and Preprocessing
- 2.3Descriptive Analytics
- 2.4Predictive Analytics
- 2.5Prescriptive Analytics
- 2.6Business Intelligence (BI)
- 2.7Data Governance and Security
- 2.8Big Data Analytics
- 2.9Key Performance Indicators (KPIs)
- 2.10Data-driven Decision Making
- 2.11Agile Analytics
- 2.12Ethics in Analytics
- 2.13Case Studies
- 2.14Rest
- Tools and Technologies4
- Data Types and Sources17
- 4.11- Data Types
- 4.2Structured Data
- 4.3Unstructured Data
- 4.42- Data Sources
- 4.5Internal Data Sources
- 4.6External Data Sources
- 4.73- Data Collection Methods
- 4.8Surveys and Questionnaires
- 4.9Observation
- 4.10Interviews
- 4.11Sensor Data
- 4.12Web Scraping
- 4.13Transaction Data
- 4.14Social Media Monitoring
- 4.15Secondary Data Analysis
- 4.16Case Studies
- 4.17Rest
- Cleaning and Transformation25
- 5.11- Dealing with Missing Data
- 5.2Identification of Missing Values
- 5.3Removal of Missing Values
- 5.4Imputation of Missing Values
- 5.5Evaluate the Impact
- 5.6Documentation
- 5.7Iterative Process
- 5.82- Outlier Detection and Treatment
- 5.9Identification
- 5.10Removal
- 5.11Transformation
- 5.12Winsorizing
- 5.133- Data Normalization and Standardization
- 5.14Normalization (Min-Max Scaling)
- 5.15Standardization (Z-score Normalization)
- 5.16Robust Scaling (IQR Scaling)
- 5.174- Feature Engineering Techniques
- 5.18Binning/Discretization
- 5.19One-Hot Encoding
- 5.20Polynomial Features
- 5.21Interaction Terms
- 5.22Feature Scaling
- 5.23Dimensionality Reduction
- 5.24Case Studies
- 5.25Rest
- Exploratory Data Analysis (EDA) with SPSS16
- 6.1Introduction
- 6.21- Basic Statistical Analysis
- 6.3Descriptive Statistics
- 6.4Frequency Distribution
- 6.5Central Tendency and Dispersion
- 6.6Correlation Analysis
- 6.7Inferential Statistics
- 6.82- Data Visualization Techniques:
- 6.9Histograms
- 6.10Box Plots
- 6.11Scatter Plots
- 6.12Bar Charts
- 6.13Heatmaps
- 6.14Pie Charts
- 6.15Line Chart
- 6.16When should I use them?
- Statistical analysis with SPSS24
- 7.11- Descriptive Statistics
- 7.21-1 Measures of Central Tendency and Dispersion
- 7.3Mean, Median, and Mode
- 7.4Variability Measures
- 7.51-2 Frequency Distributions
- 7.6Histograms
- 7.7Percentiles and Quartiles
- 7.81-3 Probability Distributions
- 7.9Normal Distribution
- 7.102- Inferential Statistics
- 7.112-1 Hypothesis Testing
- 7.12Formulate Hypotheses
- 7.13Conduct Statistical Tests
- 7.142-2 Confidence Intervals
- 7.153-1 Confidence Intervals
- 7.16Confidence Interval for Mean
- 7.174-1 Regression Analysis
- 7.18Simple Linear Regression
- 7.19Multiple Regression
- 7.205-1 Correlation Analysis
- 7.21Pearson Correlation
- 7.22SPSS Functionality
- 7.23When should I use them?
- 7.24Case Studies
- Prescriptive Analytics with SPSS8
- Predictive Modeling with SPSS9
- Business Intelligence and Reporting33
- 10.11- Dashboard and Visualization Design
- 10.21-1 Principles of Effective Data Visualization
- 10.3Clarity and Simplicity
- 10.4Relevance
- 10.5Consistency
- 10.6Interactivity
- 10.7Storytelling
- 10.8Use of Appropriate Visualization Types
- 10.9Color Usage
- 10.10Data Accuracy and Precision
- 10.111-2 Dashboard Development and Best Practices
- 10.12Define Objectives
- 10.13User-Centric Design
- 10.14Performance Optimization
- 10.15Responsive Design
- 10.16Feedback Mechanisms
- 10.17Regular Updates
- 10.18Testing and Validation
- 10.192- Reporting Tools
- 10.202-1 Introduction to Reporting Tools
- 10.21Tableau
- 10.22Power BI (Microsoft Power BI)
- 10.232-2 Creating Dynamic and Interactive Reports
- 10.24Data Connection
- 10.25Report Design
- 10.26Interactivity
- 10.27Data Refresh and Automation
- 10.28Collaboration and Sharing
- 10.29Security and Access Control
- 10.30Documentation and Training
- 10.31Case Studies
- 10.32Rest
- 10.33Business Analytics30 Minutes10 Questions
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