View on GitHub

INF 428/528: Analysis, Visualization, and Prediction in Analytics

Semester: Fall 2022 
Day/Time: Mondays Wednesdays 18:00 - 19:20	
Location: Physics 224
Instructor: Chen Zhao
Office Location and Hours: By appointment
Course Website: 

Course Description:

Principles of data analysis, emphasizing modern statistical and machine-learning based approaches. Also, the important role of simple analyses and visualization to gain an overall understanding of data sets, regardless of size. The role of analytics in creating predictive models of phenomena. The importance of understanding the nature of the data and other methodological considerations. Prerequisites: Some statistics and database experience.

Student Learning Objectives:

Course Outline:

Lecture Topic Notes
1 Introduction and Overview  
2 Intro/Review of Python I (Variables, If/Else, Loop )  
3 Intro/Review of Python II (List, Dictionary)  
4 Intro/Review of Python III (Class) Quiz1
5 Machine Learning and Predictive Models I (Linear/ Logistic regression, K-mean) HW1
6 Machine Learning and Predictive Models II (Decision tree, SVM)  
7 Machine Learning and Predictive Models III (Neural Networks)  
8 Data Wrangling with Pandas I (Missing values, Outliers, Feature selection) Quiz2,HW2
9 Data Wrangling with Pandas II (Imbalance, Standardization)  
10 Machine Learning with Python I (Loss functions, Optimizers, Model Training)  
11 Machine Learning with Python II (Implementation, Evaluation, Interpretation)  
12 Visualization with Matplotlib I  
13 Visualization with Matplotlib II  
14 Introduction to Dash Plotly  
15 Invited Talk  
16 Special topic and projects (TBD)  

Examinations and Grading:

If a student feels they will miss a course obligation for any reason, they must reach out to the course instructor for guidance.


Homework Assignments

Late Homework

Disability Policy:

Reasonable accommodations will be provided for students with documented physical, sensory, systemic, medical, cognitive, learning, and mental health (psychiatric) disabilities. If you believe you have a disability requiring accommodation in this class, please notify the Disability Resource Center (518-442-5490; Upon verification and after the registration process is complete, the DRC will provide you with a letter that informs the course instructor that you are a student with a disability registered with the DRC and list the recommended reasonable accommodations. You can review the Equity and Compliance website as well for additional information.

Academic Integrity:

It is every student’s responsibility to become familiar with the standards of academic integrity at the University. Claims of ignorance, of unintentional error, or of academic or personal pressures are not sufficient reasons for violations of academic integrity. See

Course work and examinations are considered individual exercises. Copying the work of others is a violation of university rules on academic integrity. Individual course work is also key to your being prepared and performing well on tests and exams. Forming study groups and discussing assignments and techniques in general terms is encouraged, but the final work must be your own work. For example, two or more people may not create an assignment together and submit it for credit. If you have specific questions about this or any other policy, please ask.

The following is a list of the types of behaviors that are defined as examples of academic dishonesty and are therefore unacceptable. Attempts to commit such acts also fall under the term academic dishonesty and are subject to penalty. No set of guideline scan, of course, define all possible types or degrees of academic dishonesty; thus, the following descriptions should be understood as examples of infractions rather than an exhaustive list.

Any incident of academic dishonesty in this course, no matter how “minor” will result in