1. Data and Statistical Methods
1.1. Types of Data
1.1.1. Quantitative
1.1.1.1. Discrete_Data
1.1.1.2. Continuous Data
1.1.2. Qualitative
1.1.2.1. Nominal Data
1.1.2.2. Ordinal Data
1.2. Statistical Methods
1.2.1. Descriptive_Statistics
1.2.1.1. Measures of Central Tendency
1.2.1.1.1. Mean
1.2.1.1.2. Median
1.2.1.1.3. Mode
1.2.1.2. Measures of Dispersion
1.2.1.2.1. Range
1.2.1.2.2. Variance
1.2.1.2.3. Standard Deviation
1.2.1.3. Graphical Representations
1.2.1.3.1. Histograms
1.2.1.3.2. Box Plots
1.2.1.3.3. Bar Charts
1.2.2. Inferential_Statistics
1.2.2.1. Hypothesis Tests
1.2.2.1.1. t Test
1.2.2.1.2. z Test
1.2.2.1.3. ANOVA
1.2.2.2. Confidence Intervals
1.2.2.3. Regression
1.2.2.3.1. Linear Regression
1.2.2.3.2. Logistic Regression
1.2.3. Exploratory Statistics
1.2.3.1. PCA
1.2.3.2. Clustering
1.2.3.3. Correspondence Analysis
1.2.4. Predictive Statistics
1.2.4.1. Regression Models
1.2.4.1.1. Multiple Linear Regression
1.2.4.1.2. Polynomial Regression
1.2.4.1.3. Logistic Regression
1.2.4.2. Decision Trees
1.2.4.3. Neural Networks
1.2.4.4. SVM
1.3. Applications
1.3.1. Business
1.3.2. Health
1.3.3. Social Sciences
1.3.4. Engineering
1.4. Best Practices
1.4.1. Data Collection
1.4.1.1. Random Sampling
1.4.1.2. Surveys and Questionnaires
1.4.1.3. Direct Observation
1.4.2. Critical Analysis
1.4.2.1. Data Validation
1.4.2.2. Assumption Evaluation
1.4.2.3. Result Interpretation
1.4.3. Clear Presentation
1.4.3.1. Data Visualization
1.4.3.2. Reports and Dashboards
1.4.3.3. Effective Communication
2. Business Analytics
2.1. Types of Analytics
2.1.1. Descriptive Analytics
2.1.2. Diagnostic Analytics
2.1.3. Predictive Analytics
2.1.4. Prescriptive Analytics
2.2. Tools and Technologies
2.2.1. Data Visualization Tools
2.2.1.1. Tableau
2.2.1.2. Power BI
2.2.1.3. QlikView
2.2.2. Statistical Analysis Tools
2.2.2.1. R
2.2.2.2. Python
2.2.2.3. SAS
2.2.3. Big Data Platforms
2.2.3.1. Hadoop
2.2.3.2. Spark
2.2.3.3. NoSQL Databases
2.2.3.3.1. MongoDB
2.2.3.3.2. Cassandra
2.2.4. Machine Learning Tools
2.2.4.1. Scikit-learn
2.2.4.2. TensorFlow
2.2.4.3. Keras
2.3. Processes and Methodologies
2.3.1. Data Analysis Lifecycle
2.3.1.1. Data Collection
2.3.1.2. Data Cleaning
2.3.1.3. Data Analysis
2.3.1.4. Data Visualization
2.3.1.5. Decision Making
2.3.2. Analysis Methodologies
2.3.2.1. CRISP-DM
2.3.2.1.1. Business Understanding
2.3.2.1.2. Data Understanding
2.3.2.1.3. Data Preparation
2.3.2.1.4. Modeling
2.3.2.1.5. Evaluation
2.3.2.1.6. Deployment
2.3.2.2. Agile Analytics
2.4. Applications
2.4.1. Finance
2.4.1.1. Risk Analysis
2.4.1.2. Fraud Prediction
2.4.1.3. Portfolio Optimization
2.4.2. Marketing
2.4.2.1. Customer Segmentation
2.4.2.2. Campaign Analysis
2.4.2.3. Offer Personalization
2.4.3. Human Resources
2.4.3.1. Employee Turnover Analysis
2.4.3.2. Performance Evaluation
2.4.3.3. Workforce Planning
2.4.4. Operations
2.4.4.1. Supply Chain Optimization
2.4.4.2. Inventory Management
2.4.4.3. Operational Efficiency Analysis
2.5. Importance
2.5.1. Efficiency and Productivity
2.5.2. Effective Decision Making
2.5.3. Financial Performance
2.5.4. Competitive Advantage