Free SOA Exam PA Lessons: All 22 Topics

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Predictive Analytics Problem Definition

Assess whether descriptive, predictive, and prescriptive analytics applies to a business problem. (8 min)
Describe the characteristics of predictive modeling problems. (7 min)
Explain the concepts of bias, variance, model complexity, and the bias-variance trade-off. (8 min)
Translate a vague question into one that can be analyzed with statistics and predictive analytics to solve a business problem. (8 min)
Consider factors such as available data and technology, significance of business impact, and implementation challenges to define the problem. (9 min)
Assess what additional information or next steps would improve the ability to apply predictive analytics to a business problem. (8 min)

Data Exploration and Visualization

Identify structured and unstructured data types. (11 min)
Identify the types of variables and terminology used in predictive modeling. (11 min)
Evaluate effective data design with respect to time frame, sampling, and granularity. (12 min)
Apply the key principles of constructing graphs. (10 min)
Apply univariate data exploration techniques. (11 min)
Apply bivariate data exploration techniques. (12 min)

Data Transformations and Unsupervised Learning Techniques

Create features from existing data that may add value. (13 min)
Apply principal components analysis to transform data. (12 min)
Apply K-means and hierarchical clustering to transform data. (12 min)

Generalized Linear Models

Select and validate a GLM as appropriate for a business problem. (15 min)
Apply offsets and weights as appropriate. (16 min)
Interpret model coefficients, including interaction terms. (14 min)
Select appropriate hyperparameters for regularized regression. (15 min)

Tree-Based Models

Construct, prune, and validate regression and classification trees. (14 min)
Apply bagging and random forests as appropriate. (11 min)
Apply boosting as appropriate. (12 min)

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