I'm about to start my final year as a Stats and Econ undergrad, I also plan to pursue Master's in statistics, hopefully in the same university as I am now, I checked and I can take a graduated level class this year and it will count to the necessary credits needed for the Masters program if i'll indeed continue in the the same uni

I've checked with 2 professors about their graduated level classes, they said that it seems that I have the necessary background for the their class, so i'm considering taking one of these 2 courses:

Casuel Inference - according to the syllabus it will cover: Causal Parameters, Randomization, confounding, selection bias, Usage of DAGs for checking assumptions and method and variable selections, ML algorithms in casual inference for the estimations of heterogeneous effects, Propensity score, matching, IPW and Instrumental variables.

Optimization under uncertainty - the syllabus doesn't really say as much but it says it will cover 3 main topics: Online approximation algorithms, Stochastic optimization and Onilne machine learning

So i'd love to hear some opinions on those subjects and which course sounds better to you strangers

I'm a former math major who's now majoring in philosophy and doing a minor in statistics. I'm taking 3 statistics courses next semester and have room for one more math course. I'm debating between an intro to proofs course or an applied biomath course. Below are the course descriptions.

MATH 141W: Intro to Mathematical Proofs and Combinatorics

• An important part of mathematics research is investigating and discovering patterns, then proving the patterns hold in all cases. This course focuses on solidifying these important skills. By studying mathematical logic, methods of argument, and a wide variety of topics, students will better understand what it means to prove things and how to do so rigorously. By working to discover the underlying structure and truth in various mathematical topics, students can expect their problem-solving skills to improve, as well as their ability to form and communicate persuasive, logical arguments.
• Students planning to take MATH242: Introduction to Analysis I are strongly encouraged to take MATH141.

MATH 360: Intro to Biomathematics

• In this course you will learn core concepts and modelling techniques in mathematical biology across a range of topics, from competition and predator-prey interactions to infectious diseases and evolution. These topics will be taught in the context of real-world scenarios where mathematical models are used to inform decision-making, such as invasive species, pest control, and pandemics.
  
• Even relatively simple mathematical models can give powerful insights into many biological phenomena, from patterns of biodiversity to population crashes, and can help inform policymakers about how best to manage human interactions with the natural world. You will learn how to construct models, analyse them algebraically, geometrically, and numerically, and then interpret the results biologically.
  
• Examples of the types of questions we will answer include:
  • How do species respond to human-driven pressures such as climate change and overfishing?
  • When are competing species able to coexist?
  • How best to respond to an epidemic?
    
• Throughout the course, we will explore case studies in class where we work through longer problems together, learning not only how to analyse models, but also how to construct them wisely and communicate the results. In addition to developing core analytical and computational skills, you will gain transferable skills in critical thinking, problem solving, and communication.

Which one would you recommend I guys take? I'm someone who is extremely weak at proofs which was why I switched out of the math program to begin with. However, I know real analysis is an important part of statistics, so I figure the intro to proofs course will be of great assistance.

The biomathematics course sounds interesting and will probably be easier for me since I'm strong at computational math. However, the content looks like it will utilize a lot of differential equations and I'm unsure how practical this will be for statistics. Any advice is greatly appreciated!

I'm sharing this new tabular data diagnostic tool (Entropic Scree). It can be used to estimate these properties of your high-d, real-world, dirty dataset:

• The informational volume of the signal (i.e., helps you assess whether the signal is strong enough to survive the dataset's idiosyncratic volume).
• The overall signal-to-idiosyncratic volume ratio (SNR).
• The intrinsic rank.
• Provides an exploratory map that allows for the identification of decoupled sub-networks of variables.
• The linear sufficiency (i.e., does the dataset align with the linear assumptions of standard PCA?).

Instead of evaluating linear variance, rank order, or Euclidean distance like traditional PCA variants, this new method evaluates a transformed mutual information metric. Relative to these baselines, it is less reliant on strong parametric or distance assumptions, making it appropriate to apply more broadly.

It also serves as a practical diagnostic of the theory explored in the From Garbage to Gold framework, which describes when and why uncurated, error-prone data can be used directly to create accurate prediction models.

There is a preprint that presents the full technical details, and Python and R packages will be released soon. Right now though, the original function is already available in R (see Quick Start R Function Code below).

• Entropic Scree Preprint: https://doi.org/10.5281/zenodo.22028087
• Entropic Scree GitHub: https://github.com/tjleestjohn/entropic-scree
• From Garbage to Gold Preprint: https://arxiv.org/abs/2603.12288

Let me know how it goes if you give it a try... or if you have any questions or comments of course.

############ 
# Quick Start R Function Code.
# To load the function, copy and paste the following into your R console, then hit enter. 
############

# 1. Define the direct URL to the raw function script on GitHub
url <- "https://raw.githubusercontent.com/tjleestjohn/entropic-scree/main/Entropic.Scree.v1.0.0%20-%20ENLI.R"

# 2. Define what you want to name the file on your computer
file_name <- "Entropic.Scree.v1.0.0 - ENLI.R"

# 3. Download the script to your current working directory
download.file(url, destfile = file_name)

# 4. Source the core function into your R environment
source(file_name)

# 5. Ex. To run the function and extract bipolar modules:
# results <- Entropic.Scree(dt 
#                         , extract_bipolar_modules = TRUE)
#
# View the extracted structural sub-networks for the primary axes:
# results$bipolar_modules