AL-702 (C) · Predictive Analytics/Official Syllabus
Predictive Analytics (AL-702 (C)) - Official Syllabus
1
Unit 1
Introduction Data Product
- Data Product
- Data Product Examples in Enterprise
- Developing a Data Product Strategy
2
Unit 2
Reading Data in Python
- Reading CSV & JSON Files
- Processing Structured Data in Python
- Live-Coding: JSON
- Extracting Simple Statistics from Datasets
- Data Filtering and Cleaning
- Processing Text and Strings in Python
- Processing Times and Dates in Python
3
Unit 3
Python Libraries and Toolkits
- Matrix Processing and Numpy
- Introduction to Data Visualization
- Introduction to Matplotlib
- urllib and BeautifulSoup
4
Unit 4
Gradient Descent Classification in Python
- Introduction to Training and Testing
- Gradient Descent in Python
- Gradient Descent in TensorFlow
5
Unit 5
Diagnostics for Data
- Meaningful Predictive modelling
- Regression Diagnostic
- Over- and Under-Fitting
- Classification Diagnostics: Accuracy and Error
- Classification Diagnostics: Precision and Recall
- Codebase for Evaluation and Validation
- Model Complexity and Regularization
- Evaluating Classifiers for Ranking
Reference Books
- Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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