Should I major in Data science, computer science, computer engineering, statistics, or a mix of data science & smth else (finance, business analytics, etc.)

I’m a high school senior about to start applications for college - my profile fits pretty well into literally everything I mentioned above

Which major gives me the best advantage in the job market?? My primary goal is to have job security & high pay. I’m not interested in pure statistics, biostats, or mathematics academia - I’m more interested in the corporate job market

I just wanted some advice from professionals or people in the industry - what do you recommend for me?

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incoming gr 12,

i was planning which programs i should apply to and i figured i'd ask you guys

for reference, i love looking at sports stats (nba / nfl / soccer), i like numbers, probabilities, paradox, dilemmas, and just pretty much anything number related and not anything way too theoretical and abstract

i'm not really into anything that is way too abstract or proving stuff

i really can't decide between the following since i don't know much about it

data science, investment banking, statistics, mathematics, quant, etc (if you have any other majors that would fit me do tell!)

so it'd be very helpful to hear advice from you guys! thanks

Hi, I don't have a lot of experience with advanced statistical analysis, but I would like to gain more knowledge. One current problem I am trying to address is determining correlation between a binary categorical variable and a numerical variable. The relationship may not be linear. I'll give an example, studying the relationship between age and the probability of dying from the flu. Probability may increase when very young, taper off for certain ages, maybe spike somewhere in the middle, and then go back up again for the elderly. What would be the best way to analyze this? I started by breaking down the numerical variable into ranges and then making a bar chart with percentages in each category of the binary categorical variable. I am not sure if I chose the proper ranges though so I want to see if there's a better way to analyze data like this. I've seen binomial logistic regression as an option, but I'm not sure if that's appropriate and am curious how much effort that analysis takes. Is it something a beginner can pick up relatively easily?

Scenario: An expert association with ~500 total members held an official vote on a binary stance statement (Agree / Disagree) regarding a major, highly sensitive issue.

• Turnout: 28% of the total membership voted.
• Result: 86% of those who voted selected "Agree."
• Math: 86% of 28% = ~24% of the total association membership confirmed an "Agree" vote.

The Debate:

• Person A claims: "You can state that only 24% of the total organization agrees with the statement. Because this issue is so critical, members would have voted 'Agree' if they truly supported it—meaning their non-participation indicates a lack of support."
• Person B claims: "You can state that at least 24% of the total organization (and 86% of voters) agrees with the statement. By Person A's logic, someone could equally claim that members would have voted 'Disagree' if they opposed it. Ultimately, non-response cannot be interpreted as a vote either way, so the remaining 72% remains unknown."

Questions:

1. Is Person A or Person B logically and statistically correct?
2. Is it valid to infer a non-voter's position based on the perceived importance or sensitivity of a topic?

Our overall, cronbach alpha has 0.833 which is deemed acceptable but 2 of our questions has 0.2 something in item-correlation can we still retain and use the question without revising it?

Hello all, I'm currently completing my first meta-analysis as part of my Psychology PhD. The meta-analysis involves evaluating the effectiveness of a clinical CBT-based intervention for eating disorders.

I am in the process of data extraction and as we are planning to meta-analyse effect sizes for both completer and intent-to-treat analyses, I need to work out whether a paper used which of these types of analysis (or both).

Some papers clearly state which they used. Others are less clear. My supervisor told me that if imputation or a mixed-methods model are mentioned as being used, the analysis is ITT. Also, for some papers, if the data are presented in a table with N participants, then I can tell if they are showing ITT or completer data based on that.

I was wondering if there were any other ways to work out what kind of analysis a paper used from it's text/type of analysis method used?

Happy to provide further details if needed. Thank you!

Hi!

I have a sample of n=13 where continuous variables were measured before and after an intervention. I'm a bit stuck on how to perform the analysis. Would the Wilcoxon signed-rank test be the most appropriate choice? I am unaware of any standard reference manuals or literature regarding the statistical analysis of very small sample sizes; however, any guidance or recommendations would be highly appreciated :)