Book Cover

Advanced Data Analytics for Organizations


Data Analytics

ISBN #978-1-959142-07-2

Advanced Data Analytics for Organizations

Douglas Dean, PhD

This resource uses a variety of data analytics processing applications—such as Python, Jupyter Notebook, and Microsoft Excel, as well as several other softwares—to introduce students to the data analytics process. This resource is intended to help students gain experience with key machine learning algorithms and provide students with the knowledge they need to use that experience in real life applications.

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Course Objective

This resource introduces students to the data analytics process using tools like Python, Jupyter Notebook, and Microsoft Excel, helping them gain experience with machine learning algorithms for real-life applications.

Table of Contents

1: Course Intro
2: Under and Overfitting
3: Anomaly/Outlier Detection
4: Python Modeling
5: CART: Classification and Regression Trees
6: MLR: Multiple Linear Regression
7: Related Input Variables and Related Output Variables
8: TSF: Time Series Forecasting
9: Categorical Prediction Model Evaluation
10: LogReg: Logistic Regression
11: KNN: K-Nearest Neighbors
12: Midterm Exam
13: ANN: Artificial Neural Networks
14: GB: Gradient Boosting
15: RF: Random Forests
16: Cluster Analysis and Subpopulations
17: SVM: Support Vector Machines and Profit Analysis
18: Project: Putting It All Together
19: Final Exam
20: Foundation Stats (Resource)
21: Python Basics (Resource)
22: Missing Values (Resource)
23: Data Management and Sampling (Resource)
24: Changing the Probability Cutoff

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