r/askdatascience

my friend 3+ yoe passed test assessment+technical round+technomanager round apex round+ HC round and now got link for documentation upload....but no salary discussion yet so will they do doc verification first and then discuss package or they are collecting candidate and choosing best among them? it is for senior datascientist role, how much hike he can expect as his base was 13.5 in the previous org and what is their joining bonus

I’ve been learning Machine Learning for the past few months and I’m comfortable with the theory side of things now. I understand statistics, calculus, and the working of most ML algorithms.

I’ve also learned libraries like Pandas, NumPy, Matplotlib, and Seaborn, but the problem is that I still can’t confidently use them on real-world datasets. Either I get confused about what to do next, or I feel like my knowledge is too insufficient for practical projects.

I recently realized that in real-world Machine Learning, a huge amount of the work (probably 60%+) is actually:

- data cleaning

- preprocessing

- EDA

- feature engineering

- visualization

And this is exactly where I’m struggling badly.

When I get a messy real-world dataset, I often feel completely stuck:

- how to clean it properly

- what visualizations to create

- " I can't remember the syntax of any function "

- just feel stuck by looking at the data

At this point I honestly feel helpless and stuck because I don’t know how to bridge the gap between “understanding ML theory” and actually working with messy datasets confidently.

Has anyone else faced this stage before?

What resources, projects, courses, or practice methods helped you improve in data cleaning, EDA, and visualization?

Even small suggestions or personal experiences would really help.

I’m coming from a bio-ish undergrad and planning to apply to DS/stats master’s programs for fall 2026, and the closer graduation gets, the more I realize how uneven my background is compared to people who already did CS/math-heavy degrees.

I basically started coding this semester. I’m in intro Python right now and I can get through assignments, but it still feels slow and clunky. Math-wise I’ve only done basic stats and Calc I so far. Calc II is next term and I still haven’t touched linear algebra.

What’s stressing me out is that every “how to prepare for a DS master’s” post says vague stuff like “learn Python” or “brush up on math.” Ok… but how much is enough before day 1? I’m trying not to walk into a program and immediately drown.

Right now my plan is pretty simple: finish Calc II, learn linear algebra somehow, get way more comfortable with probability/stats, and keep building up Python beyond beginner stuff.

I also want to do a couple small projects so I’m not showing up having only done homework problems. Probably some messy dataset cleaning + visualization stuff and maybe one tiny dashboard/report project.

I’m also still trying to figure out whether I even want “data science” specifically or something more stats/analytics focused. I even took the coached career test because I kept second-guessing whether I actually liked this field or just liked the idea of it. Made me realize I lean way more toward analytical/problem-solving work than wet lab stuff.

Main thing I’m trying to understand from people who already survived one of these programs: what did you actually know before starting? Like realistically. Did you come in already comfortable coding, or were you learning as you went? And were there any topics the program clearly expected you to know already that caught you off guard?

Hello, I am 18 years old and I am in my first semester of a Statistics and Data Science program, but something that has been bothering me is not knowing how the use of AI is in the daily life of data scientists/data analysts/statisticians. I find myself wondering whether it is worth spending the next few years dealing with pure mathematics and coding from scratch, only to end up needing to deliver work as quickly as possible using AI. Because of this, my question is: how are data scientists (and the other careers I mentioned above) dealing with this? How is AI being used in their daily work? And for juniors, how is the experience?

Has anyone done the ANU masters of computing and have any feedback?

I am from a non-cognate background, looking to incorporate software/computing skills into my current field and hopefully build cool stuff that currently only exists in my imagination.

I’ve also always been interested in founding a startup or working for one and this would be helpful.

I’ve done some very amateur coding with the help of AI in the past but definitely don’t understand the fundamentals. Also interested in machine learning.

Was also considering doing a data science masters online or at the ANU, or a bachelors of engineering at ANU or UC, (or even just studying a few undergrad programming subjects at UC) but benefits of this degree would be:

- more mature age students with non-tech backgrounds like myself

- better grounding in the actual technical stuff compared to data science

- shorter than a bachelors

- have always wanted to do a masters and while unlikely, might be a pathway to PhD

- in person means I’ll probably be more engaged

because I can sometimes struggle with self directed learning

Interested in anyone’s experience including how big the cohort is/whether there’s support among students, startup culture at the ANU, quality of the course, assumed knowledge/whether I should do a bridging asthmatics course beforehand

Thank you so much!!

hi, i just finished my second year of university and was looking to get a new laptop for data science, computer science, and machine learning stuff

my budget for a new laptop is $2k max, thanks in advance for the recommendations

Hey zusammen!
Ich möchte ein Generalized linear mixed effects model mit einer logic link Funktion aufstellen, um meine Daten auszuwerten.
Ich habe folgendes Modell aufgestellt und in MATLAB eingegeben:

fitglme(Data, [y ~ x1 * x2 * x3 + (1+ x1 + x2 + x3 | u)], Distribution, binomial, Link, logit, FitMethod, Laplace);

Y ist meine binäre AV,
X1, x2 und x3 Faktoren (x1 kontinuierlich und die anderen kategorial),
U ist mein Random Effekt (Personen).
Zudem wurden die Daten mit within Subjekt und mit messwiederholung erhoben.
Jeder Proband hat jede Bedingung 10 mal gemacht.

Dieses Modell ist das komplexeste welches hier noch konvergiert und es ist auch signifikant besser als ein einfacheres Modell mit nur einem random slope für x1.
Also eigentlich passt es gut zu meinen Daten.

Ich bin allerdings neu mit LMEM und bin mir nun unsicher, was genau mein Modell nun schätzt:
Zum einen die fixen Effekte der Faktoren x1, x2 und x3 auf mein Kriterium y. Zudem alle Interaktionseffekte dieser fixen Faktoren.

Außerdem noch zufällige intercepts für jede Untergruppe von u.
und dann auch noch zufällige slopes in jedem der fixen Effekte für jedes u.

Also kann jedes U nun einen eigenen intercept haben und auch einen eigenen slope für jeden der fixen Effekte.

Aber was ist mit den interaktionseffekten?
Habe ich hier auch Interaktionen zwischen den random slopes, sodass eine höhere Steigung von u1 in Haupteffekt x1 dazu führen kann dass die Steigung von u1 in x2 geringer wird??

Und gibt es jetzt auch random slopes für die Interaktionseffekte der fixen Effekte, weil ich diese ja auch geschätzt hatte: so dass u1 auch einen eigenen slope für die Interaktion von x1 und x2 hat?

Bitte entschuldigt wenn das total wirr klingt! Ich bin da gerade ein wenig überfordert 🙈

Danke für jegliche Hilfe!

Hello, I am conducting a survey for my AP stats final. I’m looking into the potential correlation between caffeinated beverage consumption and generation.

1. The survey data is completely anonymous. Information will NOT be shared with anyone.

2. The survey is being conducted by myself reddit name: “EducationalAnt4071”

3. The survey takes less than 30 seconds to fully complete

I am trying to get a good understanding of the data science interview process. What types of questions are being asked. Probability? statistics? SQL? ML?

How’s the work life balance for the people already in the field ?

So I’m in my last year of a Data Science degree and I’ve started noticing that nobody really seems to agree on what a “Data Science degree” even means.

A couple hiring managers have basically said “wait, so is this more stats or more CS?” and honestly fair question.

My program isn’t bad. We did calc, linear algebra, probability, regression, time series, ML, databases, data mining, all the expected stuff. But a lot of it feels weirdly shallow. Like we touched 12 ML models in one semester and barely implemented anything beyond toy examples. Our databases course spent more time on theory than actually wrestling with ugly SQL tables. Software engineering was basically “here’s how to write scripts that work on your laptop.”

Meanwhile I look at alumni who landed the stronger DS jobs and a ton of them came from CS, math, or stats backgrounds.

So now I’m sitting here wondering if I need to “fix” the signal before I graduate. Not because I think I learned nothing, but because I’m starting to understand how the degree gets read by recruiters.

Part of me is considering a CS post-bacc just so nobody questions whether I can code. Another part of me thinks a stats master’s would fit better since I’m more interested in analytics/experimentation than hardcore ML engineering.

Then there’s the third option where I stop obsessing over credentials and just get better at the stuff I already know I’m weak at. Better SQL. Better Python. Less Kaggle-y projects, more stuff that actually looks like something a company would use.

I already rewrote my resume because the first version sounded like a syllabus exploded onto a PDF. I ran it through resumeworded mostly to trim the fluff and make the projects sound less academic. It helped a bit, but I still feel like the bigger issue is proving I can do real work and not just pass classes.

Honestly the thing messing with my head is that I can’t tell if I’m overthinking this or seeing the market clearly for the first time. Like… is “B.S. Data Science” actually viewed differently from CS/stats once you’re applying, or does nobody care after the first internship?

Im shit at coding but i know the principles and basics
Im vibe coding everyth in work, ik how to vibe code how to prompt how to catch errors and how fix them, working pretty good so far.
But i once sat with a senior dev and he said the code is shit etc etc and i was amazed how good he was at coding
Is it worth taking the time to strengthen my coding skills? Or is it just a waste of time with ai?

Instagram switches off end-to-end encryption: What it means for users' privacy

Will the data be used for AI and ML model training?

What will happen would like to know your idea?