Lesson 1: Introduction to Data Analytics
1.1. Definition and Importance of Data Analytics
1.2. History and Evolution of Data Analytics
1.3. Key Components of Data Analytics
1.4. Types of Data Analytics
1.5. Data Analytics in Business Decision-Making
1.6. Data Analytics Tools and Technologies
1.7. Career Opportunities in Data Analytics
1.8. Ethical Considerations in Data Analytics
1.9. Data Analytics vs. Data Science
1.10. Real-World Applications of Data Analytics
Lesson 2: Data Collection and Management
2.1. Sources of Data
2.2. Data Collection Methods
2.3. Data Storage Solutions
2.4. Data Warehousing
2.5. Data Lakes
2.6. Data Integration Techniques
2.7. Data Cleaning and Preprocessing
2.8. Data Governance and Compliance
2.9. Data Security and Privacy
2.10. Data Management Best Practices
Lesson 3: Statistical Foundations for Data Analytics
3.1. Descriptive Statistics
3.2. Inferential Statistics
3.3. Probability Theory
3.4. Hypothesis Testing
3.5. Confidence Intervals
3.6. Correlation and Regression Analysis
3.7. Statistical Distributions
3.8. Central Limit Theorem
3.9. Sampling Techniques
3.10. Statistical Software Tools
Lesson 4: Data Visualization Techniques
4.1. Importance of Data Visualization
4.2. Types of Charts and Graphs
4.3. Data Visualization Tools
4.4. Creating Effective Visualizations
4.5. Interactive Dashboards
4.6. Storytelling with Data
4.7. Visualizing Time Series Data
4.8. Geospatial Data Visualization
4.9. Visualizing Big Data
4.10. Best Practices in Data Visualization
Lesson 5: Introduction to IBM Data Analytics Tools
5.1. Overview of IBM Data Analytics Tools
5.2. IBM Watson Analytics
5.3. IBM Cognos Analytics
5.4. IBM SPSS Statistics
5.5. IBM DataStage
5.6. IBM InfoSphere
5.7. IBM Db2
5.8. IBM Cloud Pak for Data
5.9. Integrating IBM Tools with Other Platforms
5.10. Case Studies: IBM Data Analytics in Action
Lesson 6: Data Wrangling and Preprocessing
6.1. Understanding Data Wrangling
6.2. Data Cleaning Techniques
6.3. Handling Missing Data
6.4. Data Transformation
6.5. Data Normalization
6.6. Feature Engineering
6.7. Data Scaling
6.8. Data Encoding
6.9. Data Imputation
6.10. Automating Data Preprocessing
Lesson 7: Advanced Statistical Analysis
7.1. Multivariate Analysis
7.2. Principal Component Analysis (PCA)
7.3. Factor Analysis
7.4. Cluster Analysis
7.5. Discriminant Analysis
7.6. Time Series Analysis
7.7. Survival Analysis
7.8. Non-parametric Statistics
7.9. Bayesian Statistics
7.10. Advanced Regression Techniques
Lesson 8: Machine Learning for Data Analytics
8.1. Introduction to Machine Learning
8.2. Supervised Learning
8.3. Unsupervised Learning
8.4. Reinforcement Learning
8.5. Model Selection and Evaluation
8.6. Overfitting and Underfitting
8.7. Bias-Variance Tradeoff
8.8. Feature Selection
8.9. Ensemble Methods
8.10. Machine Learning Libraries and Frameworks
Lesson 9: Deep Learning for Data Analytics
9.1. Introduction to Deep Learning
9.2. Neural Networks
9.3. Convolutional Neural Networks (CNNs)
9.4. Recurrent Neural Networks (RNNs)
9.5. Long Short-Term Memory (LSTM) Networks
9.6. Generative Adversarial Networks (GANs)
9.7. Transfer Learning
9.8. Deep Learning Frameworks
9.9. Applications of Deep Learning in Data Analytics
9.10. Challenges and Limitations of Deep Learning
Lesson 10: Natural Language Processing (NLP) for Data Analytics
10.1. Introduction to NLP
10.2. Text Preprocessing
10.3. Tokenization
10.4. Part-of-Speech Tagging
10.5. Named Entity Recognition (NER)
10.6. Sentiment Analysis
10.7. Topic Modeling
10.8. Text Classification
10.9. NLP Libraries and Tools
10.10. Applications of NLP in Data Analytics
Lesson 11: Time Series Analysis and Forecasting
11.1. Introduction to Time Series Data
11.2. Time Series Components
11.3. Stationarity and Differencing
11.4. Autocorrelation and Partial Autocorrelation
11.5. ARIMA Models
11.6. Seasonal Decomposition
11.7. Exponential Smoothing
11.8. Forecasting Techniques
11.9. Evaluating Forecasting Models
11.10. Applications of Time Series Analysis
Lesson 12: Data Analytics in Finance
12.1. Financial Data Sources
12.2. Financial Statement Analysis
12.3. Risk Management
12.4. Portfolio Optimization
12.5. Credit Scoring
12.6. Fraud Detection
12.7. Algorithmic Trading
12.8. Financial Forecasting
12.9. Regulatory Compliance
12.10. Case Studies in Financial Data Analytics
Lesson 13: Data Analytics in Healthcare
13.1. Healthcare Data Sources
13.2. Electronic Health Records (EHR)
13.3. Predictive Analytics in Healthcare
13.4. Patient Outcome Analysis
13.5. Disease Surveillance
13.6. Personalized Medicine
13.7. Healthcare Cost Analysis
13.8. Clinical Trials Data Analysis
13.9. Healthcare Data Privacy
13.10. Case Studies in Healthcare Data Analytics
Lesson 14: Data Analytics in Marketing
14.1. Marketing Data Sources
14.2. Customer Segmentation
14.3. Market Basket Analysis
14.4. Customer Lifetime Value (CLV)
14.5. Churn Prediction
14.6. Social Media Analytics
14.7. A/B Testing
14.8. Marketing Mix Modeling
14.9. Customer Journey Mapping
14.10. Case Studies in Marketing Data Analytics
Lesson 15: Data Analytics in Supply Chain Management
15.1. Supply Chain Data Sources
15.2. Inventory Management
15.3. Demand Forecasting
15.4. Supply Chain Optimization
15.5. Logistics and Transportation Analytics
15.6. Supplier Performance Analysis
15.7. Risk Management in Supply Chain
15.8. Sustainability in Supply Chain
15.9. Real-Time Supply Chain Analytics
15.10. Case Studies in Supply Chain Data Analytics
Lesson 16: Data Analytics in Human Resources
16.1. HR Data Sources
16.2. Employee Performance Analysis
16.3. Talent Acquisition Analytics
16.4. Employee Retention Analysis
16.5. Diversity and Inclusion Analytics
16.6. Compensation and Benefits Analysis
16.7. Training and Development Analytics
16.8. Employee Engagement Analytics
16.9. HR Data Privacy
16.10. Case Studies in HR Data Analytics
Lesson 17: Data Analytics in Retail
17.1. Retail Data Sources
17.2. Sales Forecasting
17.3. Inventory Optimization
17.4. Customer Behavior Analysis
17.5. Price Optimization
17.6. Store Performance Analysis
17.7. Omnichannel Analytics
17.8. Loyalty Program Analytics
17.9. Retail Data Visualization
17.10. Case Studies in Retail Data Analytics
Lesson 18: Data Analytics in Manufacturing
18.1. Manufacturing Data Sources
18.2. Production Efficiency Analysis
18.3. Quality Control Analytics
18.4. Predictive Maintenance
18.5. Supply Chain Integration
18.6. Cost Analysis in Manufacturing
18.7. Sustainability in Manufacturing
18.8. Real-Time Manufacturing Analytics
18.9. Manufacturing Data Visualization
18.10. Case Studies in Manufacturing Data Analytics
Lesson 19: Data Analytics in Energy
19.1. Energy Data Sources
19.2. Energy Consumption Analysis
19.3. Renewable Energy Analytics
19.4. Energy Efficiency Analysis
19.5. Predictive Maintenance in Energy
19.6. Energy Market Forecasting
19.7. Sustainability in Energy
19.8. Real-Time Energy Analytics
19.9. Energy Data Visualization
19.10. Case Studies in Energy Data Analytics
Lesson 20: Data Analytics in Telecommunications
20.1. Telecommunications Data Sources
20.2. Network Performance Analysis
20.3. Customer Churn Prediction
20.4. Service Quality Analytics
20.5. Fraud Detection in Telecommunications
20.6. Revenue Assurance
20.7. Customer Segmentation
20.8. Real-Time Telecommunications Analytics
20.9. Telecommunications Data Visualization
20.10. Case Studies in Telecommunications Data Analytics
Lesson 21: Advanced Data Visualization Techniques
21.1. Interactive Data Visualizations
21.2. Dynamic Dashboards
21.3. Geospatial Data Visualization
21.4. Visualizing Big Data
21.5. Visualizing Time Series Data
21.6. Visualizing Network Data
21.7. Visualizing Hierarchical Data
21.8. Visualizing Multivariate Data
21.9. Visualizing Text Data
21.10. Best Practices in Advanced Data Visualization
Lesson 22: Data Analytics Project Management
22.1. Project Planning and Scheduling
22.2. Resource Management
22.3. Risk Management
22.4. Stakeholder Management
22.5. Data Analytics Project Lifecycle
22.6. Agile Methodologies in Data Analytics
22.7. Project Documentation
22.8. Project Communication
22.9. Project Evaluation and Reporting
22.10. Case Studies in Data Analytics Project Management
Lesson 23: Data Analytics Ethics and Compliance
23.1. Ethical Considerations in Data Analytics
23.2. Data Privacy and Security
23.3. Regulatory Compliance
23.4. Bias and Fairness in Data Analytics
23.5. Transparency and Accountability
23.6. Data Governance
23.7. Ethical Data Collection and Use
23.8. Ethical Data Sharing and Storage
23.9. Ethical Data Visualization
23.10. Case Studies in Data Analytics Ethics
Lesson 24: Data Analytics for Decision Making
24.1. Data-Driven Decision Making
24.2. Decision Support Systems
24.3. Scenario Analysis
24.4. Sensitivity Analysis
24.5. Cost-Benefit Analysis
24.6. Risk Analysis
24.7. Decision Trees
24.8. Multi-Criteria Decision Analysis
24.9. Decision Making under Uncertainty
24.10. Case Studies in Data-Driven Decision Making
Lesson 25: Data Analytics in Customer Experience
25.1. Customer Experience Data Sources
25.2. Customer Journey Mapping
25.3. Customer Satisfaction Analysis
25.4. Net Promoter Score (NPS) Analysis
25.5. Customer Feedback Analysis
25.6. Customer Segmentation
25.7. Personalized Customer Experience
25.8. Customer Churn Prediction
25.9. Customer Lifetime Value (CLV) Analysis
25.10. Case Studies in Customer Experience Data Analytics
Lesson 26: Data Analytics in Operations Management
26.1. Operations Data Sources
26.2. Process Optimization
26.3. Quality Control Analytics
26.4. Inventory Management
26.5. Supply Chain Analytics
26.6. Cost Analysis
26.7. Performance Metrics
26.8. Real-Time Operations Analytics
26.9. Operations Data Visualization
26.10. Case Studies in Operations Management Data Analytics
Lesson 27: Data Analytics in Product Development
27.1. Product Development Data Sources
27.2. Market Research Analytics
27.3. Product Lifecycle Analysis
27.4. Customer Feedback Analysis
27.5. Feature Prioritization
27.6. Prototype Testing Analytics
27.7. Product Launch Analytics
27.8. Post-Launch Performance Analysis
27.9. Product Iteration Analytics
27.10. Case Studies in Product Development Data Analytics
Lesson 28: Data Analytics in Risk Management
28.1. Risk Management Data Sources
28.2. Risk Identification and Assessment
28.3. Risk Mitigation Strategies
28.4. Risk Monitoring and Reporting
28.5. Financial Risk Analysis
28.6. Operational Risk Analysis
28.7. Compliance Risk Analysis
28.8. Strategic Risk Analysis
28.9. Risk Data Visualization
28.10. Case Studies in Risk Management Data Analytics
Lesson 29: Data Analytics in Sustainability
29.1. Sustainability Data Sources
29.2. Environmental Impact Analysis
29.3. Energy Efficiency Analytics
29.4. Waste Management Analytics
29.5. Carbon Footprint Analysis
29.6. Sustainable Supply Chain Analytics
29.7. Corporate Social Responsibility (CSR) Analytics
29.8. Sustainability Reporting
29.9. Sustainability Data Visualization
29.10. Case Studies in Sustainability Data Analytics
Lesson 30: Data Analytics in Public Sector
30.1. Public Sector Data Sources
30.2. Policy Analysis
30.3. Public Service Performance Analytics
30.4. Budget and Financial Analytics
30.5. Citizen Engagement Analytics
30.6. Public Health Analytics
30.7. Education Analytics
30.8. Public Safety Analytics
30.9. Public Sector Data Visualization
30.10. Case Studies in Public Sector Data Analytics
Lesson 31: Advanced Data Wrangling Techniques
31.1. Data Cleaning and Transformation
31.2. Handling Missing Data
31.3. Data Normalization
31.4. Data Scaling
31.5. Feature Engineering
31.6. Data Imputation
31.7. Data Encoding
31.8. Data Aggregation
31.9. Data Merging and Joining
31.10. Automating Data Wrangling
Lesson 32: Advanced Statistical Modeling
32.1. Generalized Linear Models (GLM)
32.2. Mixed Effects Models
32.3. Survival Analysis
32.4. Time Series Modeling
32.5. Spatial Data Analysis
32.6. Bayesian Modeling
32.7. Model Selection Criteria
32.8. Model Diagnostics
32.9. Model Interpretation
32.10. Advanced Statistical Software
Lesson 33: Advanced Machine Learning Techniques
33.1. Ensemble Learning
33.2. Boosting Algorithms
33.3. Bagging Algorithms
33.4. Stacking Algorithms
33.5. Model Interpretability
33.6. Hyperparameter Tuning
33.7. Cross-Validation Techniques
33.8. Imbalanced Data Handling
33.9. Anomaly Detection
33.10. Reinforcement Learning
Lesson 34: Advanced Deep Learning Techniques
34.1. Transfer Learning
34.2. Meta-Learning
34.3. Autoencoders
34.4. Variational Autoencoders (VAEs)
34.5. Generative Adversarial Networks (GANs)
34.6. Transformer Models
34.7. Attention Mechanisms
34.8. Model Interpretability in Deep Learning
34.9. Hyperparameter Tuning in Deep Learning
34.10. Advanced Deep Learning Frameworks
Lesson 35: Advanced Natural Language Processing (NLP) Techniques
35.1. Word Embeddings
35.2. Contextual Embeddings
35.3. Sequence-to-Sequence Models
35.4. Transformer Models for NLP
35.5. Sentiment Analysis
35.6. Topic Modeling
35.7. Named Entity Recognition (NER)
35.8. Text Generation
35.9. Machine Translation
35.10. Advanced NLP Libraries and Tools
Lesson 36: Advanced Time Series Analysis
36.1. Seasonal Decomposition
36.2. ARIMA Models
36.3. SARIMA Models
36.4. Exponential Smoothing
36.5. State Space Models
36.6. Vector Autoregression (VAR)
36.7. Long Short-Term Memory (LSTM) Networks
36.8. Prophet Model
36.9. Change Point Detection
36.10. Anomaly Detection in Time Series
Lesson 37: Data Analytics in Emerging Technologies
37.1. Blockchain Analytics
37.2. IoT Data Analytics
37.3. Quantum Computing for Data Analytics
37.4. Edge Computing for Data Analytics
37.5. Augmented Reality (AR) and Virtual Reality (VR) Data Analytics
37.6. 5G Data Analytics
37.7. Autonomous Vehicles Data Analytics
37.8. Smart Cities Data Analytics
37.9. Wearable Technology Data Analytics
37.10. Case Studies in Emerging Technologies Data Analytics
Lesson 38: Data Analytics in Cybersecurity
38.1. Cybersecurity Data Sources
38.2. Threat Detection and Analysis
38.3. Intrusion Detection Systems (IDS)
38.4. Anomaly Detection in Cybersecurity
38.5. Incident Response Analytics
38.6. Vulnerability Assessment
38.7. Risk Management in Cybersecurity
38.8. Compliance and Regulatory Analytics
38.9. Cybersecurity Data Visualization
38.10. Case Studies in Cybersecurity Data Analytics
Lesson 39: Data Analytics in Artificial Intelligence (AI)
39.1. AI Data Sources
39.2. AI Model Training and Evaluation
39.3. AI Model Interpretability
39.4. AI Ethics and Bias
39.5. AI in Decision Making
39.6. AI in Customer Experience
39.7. AI in Operations Management
39.8. AI in Risk Management
39.9. AI in Sustainability
39.10. Case Studies in AI Data Analytics
Lesson 40: Capstone Project: End-to-End Data Analytics Project
40.1. Project Planning and Design
40.2. Data Collection and Preprocessing
40.3. Exploratory Data Analysis (EDA)
40.4. Data Visualization
40.5. Statistical Analysis
40.6. Machine Learning Modeling
40.7. Model Evaluation and Selection
40.8. Model Deployment
40.9. Project Documentation and Reporting
40.10. Project Presentation and Review
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