Data & Analytics Weka TutorialSyllabus:1. Introduction to Data & Analytics
- What is data analysis?
- Why is data analysis important?
- Introduction to analytics tools
2. Introduction to Weka
- What is Weka?
- Features and capabilities of Weka
- Installing Weka
3. Data Preprocessing
- Importance of data preprocessing
- Data cleaning techniques
- Data transformation techniques
- Handling missing values
4. Exploratory Data Analysis
- Understanding the data
- Descriptive statistics
- Data visualization techniques
- Identifying outliers and anomalies
5. Data Mining with Weka
- Introduction to data mining
- Classification techniques in Weka
- Clustering techniques in Weka
- Association rule mining in Weka
6. Evaluating Models
- Cross-validation
- Performance metrics for classification models
- Performance metrics for clustering models
- Model selection and comparison
7. Feature Selection and Dimensionality Reduction
- Importance of feature selection
- Feature selection techniques in Weka
- Dimensionality reduction techniques in Weka
8. Advanced Techniques in Weka
- Ensemble learning with Weka
- Time series analysis with Weka
- Text mining with Weka
- Big data analytics with Weka
9. Case Studies and Practical Applications
- Real-world applications of Weka
- Case studies showcasing Weka's capabilities
- Hands-on exercises and projects using Weka
10. Conclusion and Further Learning
- Recap of key concepts and techniques covered
- Resources for further learning and exploration in Data & Analytics
- Importance of continuous learning in the field
Note: This syllabus is meant to provide an overview of the topics covered in a Data & Analytics Weka Tutorial. The actual content and duration of the tutorial may vary depending on the specific requirements and target audience.
This course is helpful for the following exams: Data & Analytics