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Example Curriculum

  Section 1 - Introduction
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  PART A: STATISTICS
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  Section 2 - Basic Statistics
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  Section 3 - Intermediary Statistics
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  Section 4 - Linear Regression
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  Section 5 - Multilinear Regression
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  Section 6 - Logistic Regression
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  PART B: ECONOMETRICS & CAUSAL INFERENCE
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  Section 7 - Google Causal Impact (Econometrics and Causal Inference)
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  Section 8 - Matching
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  PART C: SEGMENTATION
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  Section 9 - RFM (Recency, Frequency, Monetary) Analysis
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  Section 10 - Gaussian Mixture
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  PART D: PREDICTIVE ANALYTICS
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  Section 11 - Random Forest
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  Section 12 - Facebook Prophet
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  Where To Go From Here?
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