Foundation|Predictive Statistics
Predictive Statistics focuses on using historical data to forecast future outcomes and trends. It employs regression analysis, including linear, multiple, and logistic regression, to model relationships between variables. Time series analysis techniques like ARIMA and exponential smoothing predict trends and seasonal patterns. Classification methods such as decision trees, random forests, and support vector machines aid in categorizing data. Predictive statistics also incorporates clustering techniques like K-means for pattern recognition and feature engineering to enhance model accuracy.
Key Topics for Certifications
- Introduction to Predictive Statistics
- Fundamentals of Predictive Modeling
- Regression Analysis
- Classification Techniques
- Clustering Techniques (Unsupervised Predictive Methods)
- Time Series Analysis
- Feature Engineering
- Model Evaluation and Validation
- Probability and Predictive Distributions
- Predictive Analytics Tools
- Machine Learning for Predictive Analytics
- Real-world Applications
Certifications Details
| Certification Exam Code | : | DF-CL-STS-25 |
| Certification Name | : | Fundamentals of Predictive Statistics |
| Difficulty Level | : | Foundation |
| Mode | : | Online |
| Exam Proctoring | : | Online Proctoring |
| No. of Questions | : | 20-30 |
| Online Exam Duration | : | 15-30 Minutes |
| Practical Viva | : | Not Applicable |
| Viva Duration | : | Not Applicable |
| Total Duration | : | 15-30 Minutes |
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