Data Sciences — Harrisburg University

Welcome to ANLY 500 Analytics I

This course provides comprehensive R programming instruction for data analytics, covering foundational concepts through advanced statistical modeling and visualization. Each week builds upon previous knowledge with hands-on, code-first workflows grounded in statistical theory and reproducible research practices.

Primary Textbook: Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. SAGE Publications.

Author: Ziyuan Huang | Course: Analytics I | Institution: Harrisburg University

View Repository on GitHub 📄 Course Syllabus (PDF)

📚 Weekly Tutorials

Week 01: Introduction to R

Complete beginner's guide to R programming! Learn R basics, data types, structures (vectors, matrices, data frames), functions, data manipulation, visualization, and practical analysis with Palmer Penguins dataset. 40+ visualizations with plain English explanations.

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Week 02: R for Data Analytics

Introduction to R programming, data types, descriptive statistics, and basic hypothesis testing. Learn central tendency, dispersion measures, and visualization fundamentals.

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Week 03: Data Wrangling & EDA

Master data import, tidy data principles, dplyr verbs, handling missing values, and exploratory data analysis with ggplot2. Includes correlation and basic inference.

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Week 04: Statistical Inference

Deep dive into hypothesis testing, confidence intervals, effect sizes (Cohen's d), statistical power, and Type I/II errors. Learn the fundamental equation: Outcome = Model + Error.

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Week 05: Data Visualization

Comprehensive ggplot2 tutorial covering histograms, scatterplots, bar graphs, line graphs, data reshaping, and professional themes. Master the grammar of graphics!

📄 View Tutorial 📊 Original Lecture

Week 06: Data Screening Part 1

Essential data cleaning techniques: accuracy checking, handling missing data (MCAR, MAR, MNAR), multiple imputation with MICE, and multivariate outlier detection using Mahalanobis distance.

📄 View Tutorial 📊 Original Lecture

Week 07: Data Screening Part 2

Comprehensive assumption checking with 60+ visualizations! Master independence, multicollinearity, linearity, normality, and homoscedasticity. Includes influential cases, Cook's Distance, and transformation guide.

📄 View Tutorial 📊 Original Lecture

Week 08: Correlation Analysis

Complete correlation guide: Pearson, Spearman, Kendall, point-biserial, partial & semi-partial correlations. Modern visualizations with corrplot & ggpairs. Includes comparing correlations, effect sizes, and APA reporting.

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Week 09: Linear Regression

Introduction to Linear Regression. Simple and multiple regression, hierarchical models, dummy coding, and regression diagnostics. Learn to predict outcomes and test specific hypotheses.

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Week 10: Mediation & Moderation

Advanced regression topics: Mediation (mechanisms) and Moderation (interactions). Learn modern Bootstrapping methods, Simple Slopes analysis, and how to interpret "It Depends" effects.

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Week 11: Comparing Two Means

Comparing two means using the t-test (Independent & Paired). Learn about Signal-to-Noise Ratio, Assumptions (Normality, Homogeneity), and Effect Sizes (Cohen's d).

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Week 12: Comparing Several Means (ANOVA)

Introduction to One-Way ANOVA. Understand the F-Ratio (Signal-to-Noise), Post Hoc Tests (Bonferroni), and Effect Sizes (Omega Squared). Visualizing SST, SSM, and SSR.

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🎯 Key Features

Code-First Approach: All examples are executable and reproducible
Theory-Grounded: IEEE-style citations from Field et al. (2012)
Beginner-Friendly: Extensive explanations and interpretations
Professional Quality: Publication-ready figures and tables
Reproducible: Seedhash integration for consistent results

📖 References

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Lawrence Erlbaum Associates.
Field, A., Miles, J. and Field, Z. (2012). Discovering Statistics Using R. Sage Publications Ltd., London.
Hays, W. L. (1994). Statistics (5th ed.). Harcourt Brace.
Howell, D. C. (2012). Statistical Methods for Psychology (8th ed.). Cengage Learning.
Little, R. J. A., & Rubin, D. B. (2020). Statistical Analysis with Missing Data (3rd ed.). Wiley.
Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.
Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.
Van Buuren, S. (2018). Flexible Imputation of Missing Data (2nd ed.). CRC Press.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (2nd ed.). Springer.

Welcome to ANLY 530 Machine Learning

This course introduces the theory and practice of machine learning using Python with industry-standard libraries. Topics progress from classical supervised and unsupervised learning through deep neural networks, covering the end-to-end ML project workflow, model evaluation, hyperparameter tuning, and modern architectures.

Primary Textbook: Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media.

Author: Ziyuan Huang | Course: Machine Learning | Institution: Harrisburg University

View Repository on GitHub 📄 Course Syllabus (PDF)

📚 Weekly Tutorials

Week 01: Introduction to Machine Learning NEW

Overview of the ML landscape: supervised vs. unsupervised learning, batch vs. online learning, and instance-based vs. model-based learning. Covers the end-to-end ML project workflow and key challenges.

📄 View Tutorial 🐙 Source on GitHub

Week 02: Decision Trees NEW

Deep dive into the CART algorithm — Gini impurity, entropy, class probabilities, decision boundaries, regularization (cp, max_depth, minsplit), instability, regression trees, feature importance, and partial dependence plots. Includes IEEE-cited references to Géron (2019) and Boehmke & Greenwell (2020).

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Week 03: Ensemble Learning & Random Forests NEW

From bagging and voting classifiers to Random Forests, Extra-Trees, and Gradient Boosting. Covers OOB evaluation, feature importance, hyperparameter tuning (mtry, nodesize), regression forests, and stacking. Includes IEEE-cited references to Géron (2019) and Boehmke & Greenwell (2020).

📄 View Tutorial 🐙 Source on GitHub

Weeks 04–15 Coming Soon

Gradient Boosting, XGBoost, SVMs, dimensionality reduction, neural networks, CNNs, RNNs, model evaluation, hyperparameter tuning, and production deployment strategies.

🚧 Under Development

🎯 Key Features

Code-First Approach: All examples are executable and reproducible in Python
Theory-Grounded: Concepts tied to Géron (2019) and peer-reviewed sources
Hands-On: Real datasets with end-to-end pipelines
Modern Toolchain: Scikit-Learn, Keras, TensorFlow 2.x

📖 References

Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer.
Boehmke, B. C., & Greenwell, B. M. (2020). Hands-On Machine Learning with R. CRC Press. https://bradleyboehmke.github.io/HOML/DT.html

Welcome to GRAD 695 Research Methodology & Writing

This course prepares students for analytics research through IEEE Access-format proposal development, research design fundamentals, and ethical research practices. Students learn to frame research questions, design methodological approaches, and communicate findings effectively in academic publication format.

Target Format: IEEE Access 2024 (dual-column) | Ethics: CITI Program Training Required

Author: Ziyuan Huang | Course: Research Methodology & Writing | Institution: Harrisburg University

View Repository on GitHub 📄 Course Syllabus (PDF)

📚 Course Content

Executive Session 01: Research Foundations NEW

Introduction to the research process, problem definition, analytics research methodology overview, and GRAD 695/699 two-course capstone sequence structure. Includes page count expectations and IEEE Access formatting requirements.

📄 View Tutorial 🐙 Source on GitHub

Week 02: Frameworks, Benchmarks & Literature Reviews NEW

Types of research frameworks (analytic, theoretical, methodological), benchmark datasets vs. metrics, conducting a literature search, and three methods for organizing a literature review (literature maps, subheadings, first sentences). Includes Zotero citation management and final guidance on writing a coherent literature review argument.

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Weeks 03–15 Coming Soon

Research question formulation, methodology design, data collection, analysis planning, results presentation, discussion frameworks, and APA 7th edition writing conventions.

🚧 Under Development

🎯 Key Features

Publication-Ready Format: IEEE Access 2024 dual-column template with LaTeX/Pandoc
Ethics First: CITI Program training required for IRB preparation
Two-Course Sequence: GRAD 695 (proposal ~5 dual-column pages) → GRAD 699 (full thesis ~10 dual-column pages)
Professional Writing: APA 7th edition citation and style requirements

📖 Key Resources

American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.). APA.
IEEE. (2024). IEEE Access Author Instructions. Available at ieeeaccess.ieee.org
CITI Program. Human Subjects Research Ethics Training. Available at about.citiprogram.org

Welcome to ANLY 699 Applied Project in Analytics

This capstone course involves the application of analytics methods and techniques to real-world problems. Students conduct comprehensive research projects demonstrating mastery of analytics principles, tools, and APA 7th edition scientific writing standards. The course emphasizes end-to-end project execution from problem definition through final presentation.

Writing Standard: APA 7th Edition | Format: Research Paper & Presentation

Author: Ziyuan Huang | Course: Applied Project in Analytics | Institution: Harrisburg University

View Repository on GitHub 📄 Course Syllabus (PDF)

📚 Research Writing Guide

Week 01: Research Foundation

Understanding the scientific research process, components of research papers, and APA 7 overview with basic formatting standards for analytics research.

📖 View Week Guide 📁 Week Folder

Week 02: Literature Review

Searching academic databases, evaluating source credibility, synthesizing research findings, and building theoretical frameworks for analytics projects.

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Week 03: Research Questions

Formulating research questions, developing testable hypotheses, and defining variables and constructs in analytics research contexts.

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Week 04: Methodology

Research design fundamentals, quantitative methodology, sampling strategies, validity and reliability, ethics and IRB considerations.

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Week 05: Data Collection

Data sources and acquisition, survey design and measurement, data quality assessment, and documentation requirements.

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Week 06: Data Analysis

Selecting appropriate statistical tests, checking assumptions and diagnostics, effect sizes and power analysis.

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Week 07: Results

Reporting statistical findings, organizing results logically, integrating tables and figures, what to include vs. exclude.

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Week 08: Discussion

Interpreting findings, linking results to literature, addressing limitations, implications and future research directions.

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Week 09: Abstract & Introduction

Writing effective abstracts, introduction structure (funnel approach), establishing research significance, stating purpose and hypotheses.

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Week 10: Citations & References

In-text citation formats, reference list formatting, managing citations in R Markdown, avoiding plagiarism.

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Week 11: Figures & Tables

APA-compliant figure formatting, table design and numbering, captions and notes, R code for publication-ready graphics.

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Week 12: Revision & Editing

Self-editing strategies, peer review process, common writing pitfalls, strengthening arguments and clarity.

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Week 13: Final Draft

Integrating all components, final formatting checks, preparing submission materials, quality assurance checklist.

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Week 14: Presentation

Creating effective research presentations, designing clear slides, delivering findings professionally, Q&A preparation.

📖 View Week Guide 📁 Week Folder

🎯 Key Features

APA 7th Edition: Complete guide to scientific writing standards for analytics research
14-Week Tutorial: Structured progression from foundation through final presentation
Applied Focus: Real-world analytics projects demonstrating end-to-end execution
Research Integration: Connecting data analysis with scientific methodology and reporting

📖 Key Resources

American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.). APA.
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. SAGE Publications.
14-Week Research Writing Guide: RESEARCH_WRITING_GUIDE.md