Introduction

Welcome to the Data Analysis II Course!

Data Analysis II builds on foundational concepts from Data Analysis I, focusing on advanced statistical modeling, machine learning techniques, and big data tools. Students will develop proficiency in predictive analytics through hands-on projects using Python, SQL, and distributed computing frameworks. The course emphasizes ethical considerations in modern data workflows and prepares students for real-world analytical challenges.

📋 Course Highlights:

Advanced Data Wrangling: Implement advanced data wrangling techniques for complex, multi-source datasets.
Advanced Visualization & Simulation: Master complex data visualizations (Seaborn, plotly…) and probabilistic simulation exercises.
Predictive & Statistical Modeling: Build, interpret, and diagnose models including Multiple Regression, Generalized Linear Models (GLMs), Time Series (ARIMA), and Classification algorithms (kNN, SVM…).
Dimensionality Reduction & Resampling: Apply PCA, t-SNE, bootstrapping, and resampling methods to optimize model performance.
SQL & Big Data Frameworks: Execute complex SQL queries and big data operations using frameworks like PySpark and Dask.
Ethics in Advanced Analytics: Evaluate ethical implications in modern analytical workflows using GDPR and bias frameworks.

By the end of this course, you will have a deep mastery of advanced predictive analytics, empowering you to execute complex data science workflows and make data-driven decisions.

Course Criteria

`Criteria`	`Percentage`
In-class activities & quiz	15%
Labs	25%
Midterm Exam	30%
Final Project & Presentation / Practical labs	30%

Programming:

You are free you use your favorite programming language Python or .

Course progress

Note: The following table of contents will be progressively updated according to the course advancement. To save PDF version of the slides, simply press E on your keyboard to render it to PDF and save the wanted slide.

Topic	Lab	Solution	Remark
1. Review & Advanced Data Wrangling	Lab1	…Loading	Completed ✅
2. Advanced Data Visualization	Lab2	…Loading	…Loading

Midterms, Exams and Projects

In this section, you will find all the information related to the midterms, exams and projects including instructions, starting dates and the deadlines.

Midterm & Exam

Midterm Exam: Check Canvas for schedule.
Final Exam: Check Canvas for schedule.

Project & Capstone:

Deadline for the report: Check Canvas.
Where to submit: Canvas
Your report should be in PDF format (prepared using Overleaf or Quarto/RMarkdown) and include the following criteria:
- Members’ names & contributions:
  - Clearly state each member’s contribution to the project and report.
- Introduction & Purpose:
  - Clearly state the objectives and analytical scope for multi-source datasets.
- Advanced Wrangling & Preprocessing:
  - Detail steps to clean, transform, and handle complex datasets, missing values, and outliers.
- Exploratory Data Analysis & Visualization:
  - Include advanced visualizations (ggplot2/Seaborn) and statistical diagnostics.
- Model Development & Resampling:
  - Explain choice of advanced models (GLM, ARIMA, Classification, PCA).
  - Provide details on validation, bootstrapping, and model optimization steps.
- Results, Evaluation & Ethics:
  - Present model performance metrics.
  - Evaluate ethical implications, privacy frameworks (GDPR), or potential algorithmic bias.
- Conclusion & References:
  - Summarize key insights and future work.
  - Cite all sources and tools in APA format.
Presentation:
- Capstone presentations will take place during Sessions 29 and 30.

Resources and Further Reading

Here, you will find additional resources, including books, research papers, and online courses, to further your understanding of Data Analysis.

📚 You will find these books/links helpful…