Course Summary and Next

MATH/COSC 3570 Introduction to Data Science

Dr. Cheng-Han Yu
Department of Mathematical and Statistical Sciences
Marquette University

Summary of 3570

  • You’ve learned a lot in this course!

  • Data importing > wrangling > visualization > statistical machine learning > reporting

Recommendation

If you want to learn advanced machine learning,

  • Probability (MATH 4700)
  • Statistical inference (MATH 4710)
  • Linear algebra (MATH 3100)
  • Optimization (MATH 4650)
  • Data structure (COSC 2100)
  • Algorithms (COSC 3100)

Recommendation

If you want to be a professional data scientist,

Stay positive and keep learning!

ADP in Applied Statistics

  • BA/BS and MS in 5 years
  • Advanced statistical and data analytics tools
  • No application fees
  • Courses include
    • Computational Probability
    • Statistical Simulation
    • Applied Linear Algebra
    • Design of Experiments
    • Machine Learning
    • Statistical Computing and more!

Presentation Order

team <- c("GitClub", "ggplot3", "Ctrl + Alt + Elite", 
          "Big Meta Watchers", "Win Rs", "Red Pandas", "Data Dawgs")
set.seed(2024)
(present_order <- sample(team, size = 7, replace = FALSE))
[1] "ggplot3"            "Win Rs"             "Red Pandas"        
[4] "Big Meta Watchers"  "Ctrl + Alt + Elite" "GitClub"           
[7] "Data Dawgs"

  • Every individual is welcome to ask questions to any group.

  • Every group asks as least one question about previous group’s project.

Project Proposals

Final Project

  • 14 - 15 min presentation
  • You evaluate all group projects except the one you work on based on
    • Project Content and Organization (8 pts)
    • Presentation Material (Slides) Quality (4 pts)
    • Oral Presentation Skill and Delivery (4 pts)
    • Interactions and Q&A (4 pts)
  • You choose one single person who you think contributes the most to your group project.

Evaluation Sheet

Evaluation Rubric

Thank you!