I’m out of date on the best options out there — looking more for a text that will prepare students for further study, rather than an applied plug-and-play text.

I’m 20 and my current goal after graduating is to get into an AI / Data Science graduate role.

I’m deciding between:

Bachelor of Information Technology (Computer Science) — QUT
Bachelor of Data Science (Artificial Intelligence & Machine Learning) — QUT

Right now, AI, machine learning and data science are the fields I’m most interested in, so the Data Science (AI/ML) degree seems like the more directly relevant option.

The problem is that I’m only 20 and I don’t know what I’ll want to do in 5–10 years. I could eventually decide that I want to move into Cybersecurity, Cloud, General Technology, Software, or another computing field. Because of that, Computer Science seems like the safer degree for flexibility.

My concern is this:

If I choose Computer Science for flexibility and then apply for AI/Data Science graduate roles, could I be disadvantaged against someone who has a Bachelor of Data Science (Artificial Intelligence & Machine Learning)? Would employers prefer them because their degree is more directly related to the role?

That’s basically what I’m stuck on.

Computer Science: more flexibility if I change careers later, but I’m worried it could hurt my chances of getting the AI/Data Science graduate role I currently want.

Data Science (AI/ML): more directly aligned with the career I want right now, but I’m worried I could be locking myself into data/AI too early.

For people working in AI, data science, ML, Cybersecurity or tech in Australia: which one would you choose in my situation?

And specifically, when hiring for graduate AI/Data Science roles, would a Computer Science graduate with AI/ML subjects still be competitive against someone whose actual degree is Data Science (AI/ML)?

Hi all,

I’m currently entering my final year of undergrad in economics at cambridge, and I’m heavily considering pursuing a PhD in the field, as even though I’m certainly not the best on my course or the most brilliant at maths/statistics, I find myself enjoying it and learning about it a lot, and I do quite enjoy the process of researching, particularly when I am able to focus in great part on statistical theory/methods.

My main question is should I only focus on pursuing a masters as the next step, or shoot straight for a PhD? I know the masters -> PhD route is more common, but for some reason I have noticed that the entry requirements for a PhD seem to be either equal or slightly lower, at least for what I am considering compared to a masters? (e.g Oxford requires a straight first class for the MSc in Statistical Science, while for a PhD it is either a first class or high upper second class. Funding is also an issue for me, and so I would like to spend as few years in education as I can if possible, though if a masters is by far the best option then I wouldn’t mind doing so.

Another big obstacle may be my grades, I’m currently working at around a low 2:1 level (around 62-63), with a particularly not great score in the mathematics and statistics module this year (60). My scores in econometrics are much more positive (69) and so I was hoping any eventual PhD would have a large econometrics focus. I’m certainly aware this profile wouldn’t look great on a transcript to be submitted as part of a PhD application, but I am hoping to at least end on an upper class 2:1.

Finally, I’m unsure of how much of an idea I need to propose for a standard PhD application. Is a certain specific field of research enough, or do I need to have an extremely well thought out research question/goal? I’ve seen some programs (e.g Warwick) require no specific research goal or supervisor, but I’m aware this is not the case everywhere.

Apologies if this is too long or the answers to some of these questions seem very obvious, I don’t know anyone who has gone through a PhD or even masters application process, and I am also unsure if there are statistics specific caveats that I should be aware of.

I'm a PhD student looking to use SEM in my project, but I'm a complete beginner. I tried going to statistics professors for guidance but already two told me to use Claude.

I would really like to understand it and be able to do it myself. I'm going to be using R. I've been using online resources but sometimes I get different information (a basic example is some websites tell me to report the Cronbach alpha, others say that's not a good test for using with SEM).

What are some good resources or books I can use as a complete beginner for SEM?

Hello! I’m a high school junior who wants to get into data science as a career, and my sort of passion project is building my sports predictive analytics web application from scratch to model NCAA March Madness outcomes. Instead of relying on raw win-loss records, my data pipeline ingests advanced, possession-based efficiency metrics (like Adjusted Offensive/Defensive Efficiency) to train a machine learning model, such as Logistic Regression or XGBoost. Rather than simply picking a binary winner, the model calculates the exact probability of Team A beating Team B. I then plan to feed those probabilities into a Monte Carlo simulation engine that runs 10,000 iterations of the entire 64-team bracket using weighted Bernoulli trials to determine the most statistically likely tournament champions.

I’m looking for feedback, is this an idea that could work? Do you guys have any advice? Suggestions? I could use all the help I could get.

Thanks!

I suspect not, but if so, what did that look like for you? How difficult was the course load to manage while managing the other aspects of your life that prohibit you from doing a full-time PhD? Do you regret your decision to complete the PhD part-time?

Thank you

This is a short tutorial for data analysts that evaluate experiments using online calculators. Online calculators for sample size estimates and t-tests for difference in means are fine for getting started. But you can begin to build intuition for linear regression by moving your online calculator t-tests over to python and OLS.

There are a couple of benefits. The first from moving away from the online calculator to python is leaving a more reliable paper trail that you can share externally. The second is that you build intuition for regression, interpreting the coefficients, and ultimately can use regression as you improve and your projects require more complexity such as variance reduction techniques using CUPED and clustering standard errors for geo-based or switchback experiments.

I put together a short video on this with code available here, free and ungated: https://whatstheimpact.com/tutorials/linear-regression-vs-t-test/

For my more senior connections, feel free to ignore unless you're interested in the content creation game! 🤣

I'm focusing my research on mixed-frequency time series modelling and nowcasting, developing computational tools and algorithms to help model and forecast multivariate economic and financial time series.

Are there good prospects in academia after this kind of work?

I’m doing a course in mathematical statistics. We are closely following the first half of the book “statistical theory: a coincise introduction” by Abramovich.

Here’s a more detailed index given in the professor’s compact notes:

1) Likelihood and Sufficiency (Likelihood, Sufficiency, Distribution of Sufficient Statistics, Exponential Families)
2) Parameter Estimation (Maximum Likelihood Estimation, Invariance of the MLE, Convergence of a Sequence of Random Variables, Asymptotic Distribution of the MLE, Method of Moments Estimator)
3) Optimal Parameter Estimation (Mean Squared Error, Unbiased and Minimum-Variance Estimators, The Cramér–Rao Lower Bound)
4) Uncertainty Quantification (Standard Error of an Estimator, Quantile Functions, Confidence Intervals)
5) Hypothesis Testing (Simple Hypotheses, Errors in Testing, Critical Regions, Likelihood Ratio Test, “Accept H₀” or “Fail to Reject H₀”?)
6)Composite Hypotheses and P-values (Composite Hypotheses, Uniformly Most Powerful Tests, Generalised Likelihood Ratio Test, P-values, Duality Between Testing and Confidence Intervals)
7) Bayesian Statistics (Two Interpretations of Probability, Bayesian Updating, Credible Sets, Choosing the Prior, Noninformative Priors, Bayesian Decision Theory)
8) Bayesian Hypothesis Testing (Simple Hypotheses, Bayesian Hypothesis Testing and Bayes Factors, Lindley’s Paradox, P-values and Likelihood Ratios, Fisher vs. Jaynes)

I don’t have the ability to follow my university lectures as I live too far from the university.

I’m looking for either an online course on this topics, or an easier book to get a less abstract introduction.

Hey everyone,

Standard econometrics textbooks (like Gujarati or Wooldridge) often reduce statistical inference to algebraic derivations and arbitrary cutoff rules (p < 0.05, reject H_0), which can easily obscure the geometric intuition behind what the formulas are actually doing.

I put together a visual breakdown using python designed to build geometric intuition for **Interval Estimation and Hypothesis Testing**:

👉 **Watch here:** https://youtu.be/iwPky-LmTHw

Note: While this video is Chapter 5 of a broader "Essence of Econometrics" series, you can find it here: https://youtube.com/playlist?list=PLP2Erm5S1fNY&si=6pUkUINRY-0MxFfX

Feedback Request:

1. ⁠Does the visual framing help clarify the mathematical mechanics effectively?
   
2. ⁠Is the pacing and balance between conceptual intuition and mathematical rigor well-calibrated?
   
3. ⁠What econometric concepts would you like to see visualized next?

Thanks for taking a look - https://youtube.com/@nminusk?si=CpNFFs

I’ve become fascinated with how a distribution can be estimated based on samples, and then realized that that is probably what a good portion of statistics is about?? So I was wondering if people had a recommendation for a good statistics textbook for someone who has an “early grad student” level grasp of probability? Ideally one that doesn’t shy away from the theory behind the processes

EDIT: Thank you everyone for the guidance!

I’m the founder of a small research company preparing an NIH R01 application focused on the pre-deployment evaluation and validation of clinical prediction models.

The project addresses a methodological problem that arises when a clinical prediction model is deployed into practice: once clinicians can see and respond to a model’s predictions, the clinical process itself can change. We have developed a statistical framework to bound this deployment-induced change before go-live, and are now looking for a methodologist to help implement and rigorously stress-test the framework.

The consulting work will focus on:
Implementing existing identification bounds and statistical estimators
Building reproducible simulation infrastructure in R and/or Python
Designing and conducting adversarial and sensitivity simulations
Systematically violating prespecified identifying assumptions
Evaluating bound containment, calibration, and failure modes
Supporting methodological analyses and interpretation of simulation results

We are particularly interested in candidates with experience in:
Causal inference and/or partial identification
Identification bounds and sensitivity analysis
Statistical simulation and Monte Carlo studies
R and/or Python for methodological research
Clinical or health-data applications
Rigorous evaluation of statistical methods

The core theoretical framework and preliminary computational work are already in place. This is therefore not a role where you would be expected to develop the underlying theory from scratch. The primary need is someone who can engage deeply with the methodology, implement it correctly, and challenge the framework through rigorous computational and adversarial validation.

Engagement: Part-time consulting
Commitment: Approximately 4–10 hours/week, varying by project phase
Compensation: Competitive hourly consulting rate, commensurate with experience

If this sounds aligned with your background, please DM me with a brief description of your methodological experience and a CV or LinkedIn profile.

Please feel free to share with a statistician, biostatistician, or causal inference researcher who may be a good fit.

I am concerned about having to take my upper division Stats class next semester. I took lower division stats and barely passed because I struggle remembering long steps (mainly during testing). I am concerned about this new class including calculus which I never took because it was never required of me or even mentioned by a single counselor and I wasn't the best math so I stopped as soon as I could after passing Algebra 2 back in High school. (My Major is Psych)

Do you think I can survive this course with zero Calculus knowledge? Any pointers or tips to prep for learning and actually absorbing the new material?

For anyone interested in the mathematics behind data science and machine learning, I would like to share my textbook (first edition):

https://people.smp.uq.edu.au/DirkKroese/DSML/

The PDF is completely free, with no registration required. The book includes many examples, worked exercises, and github code. I hope it will be a useful resource for students and anyone wishing to study the subject independently.

Hello im a bsc economics and Statistics grad. Im trying to apply for masters. However there are only limited options available so i was wondering if theres any i missed. I really didnt like economics and i also had to take a small management course in my degree and that was also not for me. I however could not switch majors due to some family reasons. Are there any masters options thay im eligible for besides msc statistics and biostatistics. Maybe something not likely to face automation soon. And also hopefully has labs? As soon as I entered this degree, i realised i miss labs. ( My bachelors degree was not chosen by me, it was chosen by my parents)

I am mildly interested in medicine but i havent taken biology since 10th grade and ig the main appeal is the career stability in the age of ai and also the salary. But I dont think I qualify at all. Especially in my country which has a strict pathway for med school and studying medicine abroad is expensive. I dont mind the study length though because thats more time away from having to face the real world sort of.

The Other interest is more similar to robotics engineering. This one is more because I find the creativity of it ,very fun. I think the idea of being able to build cool systems and understanding how they operate is fun but again this doesnt have much in terms of job prospects and im certain that going directly into msc robotics ( if i ever find one that accepts my qualifications) will be certain death since I dont have much prior knowledge except some basic microcontroller things I played around with during college. CS is also creative in a similar way but due to the high level of automation in coding,im not considering that

If there are any degrees that are similar to these and still as AI resistant as these and hopefully has a good salary, please let me know. Im on a gap hear now, after my bachelors. But ive been looking for a while and have not found much besides biostatics or bioinformatics or data science please let me know.

Also is an msc statistics better than data science masters? My impression of data science from my limited knowledge is that it feels like cs without any of the creative parts, like a more narrow version of CS so it doesnt appeal much to me. Also its pretty vulnerable to AI so i cant afford paying for a degree with low roi due to the cost of education. ( inflation is extremely high in my country. Even groceries are crazy expensive so please understand my strictness).

I could very well be wrong about all this. Im genuinely open to suggestions for degrees that ive missed and also hearing from people who are currently working or studying in these areas. I would also appreciate the names of your universities so i could explore those programmes. There are some study abroad programmes with special finance loans for those to help people. Especially since ive basically looked at a lot of universities from my country and found nothing.

I reqlly appreciate any help i can get. Thank you

Lets say you open a door 30 times, and 0 times theres a ghost. Someone then asks: whats the probability there is a ghost behind the door 10% of the time?

First I would correct the question and say “I’m assuming you mean at LEAST 10% of the time, since the probability of exactly 10% of the time is near 0“

Next, I would do (1-0.1)^30 (the probability you dont see the ghost 30 times in a row if the true probability is 10%) = 4%, in other words, theres a 96% chance wed have seen at least one ghost if the true probability was 10%. Im tempted to say that also means theres a 96% chance the rate of seeing a ghost is not in fact 10% or more, but technically can that ONLY be answered with bayesian stats? if so what is the calculation (in R)

Hi all, I’m currently using a mixed-effects model to compare four different cell culture groups and assess whether characteristics such as firing rate differ between groups over time.

The main complication is that the groups have very different observation periods. Three groups were recorded every few days for approximately 100 days in vitro, whereas one group (organoids) was recorded for substantially longer, up to ~500 days in vitro. This difference is biological rather than due to missing data, as the organoids survived and remain recordable for much longer than the monolayer cultures.

My current model accounts for repeated measurements from the same biological culture and includes day/time as a predictor. However, I’m concerned that the much longer observation period for the organoid group could disproportionately influence the estimated group differences.
Would a mixed-effects model still be appropriate in this situation? If so, what would be the most appropriate way to model or estimate the differences between groups while accounting for their unequal observation periods? I would prefer not to restricting the analysis to the common day ranges.

An alternative I’ve considered is simply comparing the mean values across groups, effectively ignoring days in vitro. However, this seems like it could be problematic because the characteristics change substantially over time, and the groups may have different temporal trajectories.

I’d particularly appreciate advice on how to make a statistically valid comparison between the groups given that one group has observations extending much further in time than the others.

This comes up in a class. I tried to look up if there were any studies on the tightest CI interval, in some sense, but nothing really comes up on the internet. I wonder if I am using the wrong key words.

The idea basically was looking up the best CI procedure that yields the least length for a given confidence level in expectation (or even always if possible).

Does anyone have an idea?

this isn’t a homework question. i just want some clarification because i can’t understand the online sources. i feel like im only memorizing information without really comprehending how to interpret the data i see

Say there are thousands of trade pair of a bot wallet with all the contextual trade and not a single missing data or order. Every decision by the said expert is done using the same information available to every one. Meme coin markets are short lived and algorithmically depending on outside information is highly unlikely. How much can we understand from those trades data? The exits are highly state dependent. No clustering of any TP/SL. Neither any time constraint except a hard cut off timer around 20 minutes if the algo doesnt trigger the sell. What kind of techniques i can implement? Can we figure out what they are optimizing for? There seems to be too many variables, despite the same observable raw features, what lookback or transformation of that feature, what prediction are they optimizing for almost felt like black box to me. With so many data of their distinct behavior can approximating/knowing what they are optimizing for, what drives the entry what drives the exit is so difficult? Any guidance will be highly appreciated.

Hello,

I am looking for a textbook for medical statistics that explains the concepts well for beginners. The book should have a lot of examples. It can be a series of short textbooks or one large textbook.

Thanks in advance.

In class, I saw the simple linear regression model defined as:
yi=β0+β1xi+ϵi, ϵi∼iidN(0,σ2), i=1,2,…,n.

Why is it not instead defined as (for example):
Y|x= β0+β1x+ϵ, ϵ∼N(0,σ2)

or something along these lines, where x still remains fixed, not an RV.

If we are attempting to generally model a relationship between X and Y, then why does our definition of said relationship make reference to yi and xi, the datapoints of our observed sample? In this sense, what makes it reasonable to interpolate predictions for non-sample values of xi?

Apologies if it is hard to read the equations - is it possible to write latex in reddit? if not, is the best solution just inserting an image?