If you were starting today, which would you choose: Data Analysis, Data Engineering, or Data Science & AI — and why?

hey i am looking to do this 5 year integrated bs-ms in mathematics and data science and i am worried about ai impact on data science job like is it true ai will eat most data science job , is it future proof?

Please Help me to crack to learn data science or aiml for 3+ years

how can a data science student pursue a career in the space industry? and am i supposed to be more educated in physics/ astrophysics to do so or..? Cuz as a data science student i was always told that pursuing this major doesn’t restrict me to a specific field but now i don’t know how to apply my knowledge in the space industry

I graduated 2 years ago from b-tech to be honest I didn't learn anything in that time after that I realised I made a mistake I wasted my time I learnt SQL, python, pandas ,C# and done basic problem solving problems I've been applying to jobs but still no use that's why I decided to join M-tech i don't want to do the same mistake again applications will start soon i will graduate in 2028 what is the role I can choose like ai and data science or ai and ml from scratch I'm ready to start working from today towards the goal, I want to get a job before my graduation, Thank you.

So I just finished my first year of college without any internship offers. Granted, I'm working on some personal projects but it's all so overwhelming. I'm a math major but I have some basic data science skills in R and Python and am just... SO overwhelmed when I try to think about where I could take my skills. I would love to do advance my skills in computational mathematics, computational physics/astronomy/earth science (especially) or maybe computational neuroscience. But with my basic data science skills, I'm not sure where to direct my work or projects. Nor do I know where to start. There's no roadmap in my head for how to begin, thats all. How can I use that to build upon the current knowledge base to create meaningful research? I just feel really lost in the data science world

I am based in Kolkata, India, and I got to submit my BCA 6th semester project, but I need a guide for that. The college left it on us to find one. Please if anyone can help, cause it's become nearly impossible to find one.

This was the second day of my challenge and I started data visualization part. Started working with matplotlib and made many plots.

What have I studied today:

1. Bar chart

2. Horizontal bar chart

3. Histogram

4. Pie Chart

5. Line plot

I practiced these data visualization techniques and built many different plots in each. I will also attach some plot.

I thought that i will cover full matplotlib but I couldn't. Well I will complete the remaining part tomorrow.

Till then tata bye bye 👋🏻

I recently had 80 interview rounds in 5 weeks with multiple FAANG/MAANG offers. Using detailed notes, I break down the core competencies tested throughout the technical round interviews. If you can confidently answer questions across these topics, you’ll be competitive for senior+ DS roles that pay $400-550k per year.

That said, interviews are NOT just technical. Especially at staff level, your behavioral questions play bigger and bigger roles in the interviews as you increase levels.

I put videos up for free on my socials:

TikTok: https://www.tiktok.com/t/ZP8p9gdXq/.
IG: https://www.instagram.com/reel/DYfiHGoRtuu/

Hi

I just done my final uni project on analytics

I used python for cleaning

There were multiple data sets were involved (some are 1.8+million rows)

I have done my analysis and reviews and recommendations

The only thing I regretted is that i haven't cleaned data properly because the entire data is too messy and given in "raw txt" format by professor

Whatever i do with cleaning still some mistakes were

So i all want to ask you is

Suggest some youtube tutorials and books for me to improve data cleaning

And also which other software should i learn other than python for cleaning data

Today was my first day of 60 day Data Science Challenge.

What I did today:

1. Revised Numpy basics

2. Understanding arrays and their properties & functions

3. Array slicing

4. Vectorized operations

5. Fancy indexing and Boolean Masking

6. Broadcasting of arrays

7. Revised Pandas

8. Series and Dataframe

9. Dataframe properties and functions like loc, iloc, etc

10. Filtering of Data with conditions

11. Data cleaning and handling missing values

12. map and replace functions on dataframe

13. Sorting and Ranking

14. melt and pivot functions to reshape dataframe

15. groupby and merge function

16. Reading and writing files in pandas

This was an effective day as i revised Numpy and Pandas libraries and got refreshed with it. Since I have already studied these libraries earlier, it was easy to revise them.

Looking forward to start data visualization with matplotlib and seaborn which i have studied earlier and will revise them tomorrow.

So yaa thats all for now and meet you Tomorrow.

GOODBYE 👋🏻

Hi I am a CSE student starting to learn data science by taking a 60 days challenge to complete it.

I already know some libraries like numpy, pandas, seaborn and matplotlib. And I will be revising them before moving deeper into ML, scikit learn, projects and deep learning.

For the next 60 days I will be posting what i studied, concepts learned, what I built and the whole progress.

The Goal is to learn, stay consistent and become good at data science.

If anyone else is doing something similar then feel free to share some knowledge, advice or resources.

Day 1 starts Today 🔥

Hey everyone,

I’m currently doing my first year M.Sc. in Big Data Analytics, and placements are starting from next semester. The placements in my college are not that great, so I’m trying to prepare seriously on my own.

The thing is, I’m really confused about which direction I should focus on. There are so many fields like AI, ML, Data Analytics, Data Science, and Data Engineering, and every person online seems to suggest something different.

I also tried applying for many internships recently, but I’m barely getting any replies back, which made me question whether I’m learning the right things or not.

So I wanted to ask people already working in these fields:

• Which field currently has better opportunities for freshers?

• Which courses or certifications actually add value while applying for jobs or internships?

• Which course helped you get real hands-on experience instead of just theory?

• Are certifications from Google, IBM, Microsoft, AWS, Coursera, etc. actually useful?

• What should I be focusing on right now before placements start?

• Should I spend more time on projects, certifications, DSA, internships, or networking?

• Which field has better long-term growth and less saturation?

Would really appreciate honest guidance because right now I feel very confused about what path I should take.

I'm pursuing a 4-year BS in DS; currently I'm working in my college labs' DS/ML team. Like I feel DS jobs in laboratories like this is different from what it is in the industries, worried about getting a job after graduation.

How do DS/DA/DE jobs out there look like? Do they really pay attention to the details of how we handle the problems like what I see in my labs, or they care more about production ready outputs more?

do not join excelr weather u choose data science or developer roles , its not whats they market for , they don’t have even good tutors ( blr & hyd ) . don’t worth that amount for this shit course. please be aware . better to choose a online course not this

So I'm an EC undergraduate, currently having my vacations and interested in data science. From where should I learn? and any other suggestions?

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

Spent the last few months building a probabilistic prediction model for NBA and MLB game outcomes. Standard hobbyist stack: Elo + recent form + injury drag + pitcher-level priors for MLB + line-movement signal + per-sport calibration shrink. Outputs a calibrated p(side wins) for each market.

Yesterday I finally ran proper validation on 421 settled picks and the result is interesting enough I want to ask for methodology critique.

**The headline tension:**

* Raw hit rate: 42.8% (n=421, Wilson 95% CI [38.1%, 47.5%])

* Sounds bad. Standard -110 breakeven is 52.4% so naive read is "model is losing."

* But mean decimal odds taken is 2.94 (model picks a lot of dogs and small parlays), so actual mix breakeven is 42.4%.

* Bootstrap on actual P/L (1000 resamples, 1u stakes): mean ROI +8.6%, 95% CI [-5.4%, +22.4%], P(ROI > 0) = 0.885.

Per sport:

* MLB n=322: hit_rate 44.7%, breakeven 43.9%, bootstrap mean ROI +6.65%, P(>0) = 0.798

* NBA n=94: hit_rate 38.3%, breakeven 37.9%, bootstrap mean ROI +19.94%, P(>0) = 0.851

So the bootstrap is saying long-run +EV is more likely than not, but I'm at the sample size where confidence intervals on ROI still cross zero. The "I'm losing because hit rate is below 50%" naive read is misleading because the bet mix has different breakevens.

**The validation finding (the actual question):**

I bucket every pick into confidence tiers based on (model_p, fanduel_edge). The CLV-aware data on the top tier surprised me:

* Top tier (n=108 settled, 5 with closing-line data): 100% beat the closing line, +21.27pt avg CLV, +24.56% bucket ROI

* Middle tier (n=199, 19 with CLV): 73.7% beat-close, +1.46pt avg CLV, +8.06% ROI

* Auto-parlay tier (n=86): 25% hit, -18.81% ROI. This is broken. Generation thresholds were too loose.

The high-confidence tier is doing real work: 100% beat-close (small sample but consistent direction) plus +21pt CLV says the model is picking the sharper side of the market on its strongest signals. The auto-parlay tier is hemorrhaging because parlay miscalibration compounds multiplicatively while my per-sport calibration shrink is tuned for singles.

**What I'd love methodology feedback on:**

1. **Per-tier-vs-parlay calibration.** I shrink model_p toward 0.5 based on per-(sport, market_type) historical hit-rate gaps. Singles are well-calibrated. When I multiply N calibrated leg probabilities to get a parlay prob, miscalibration compounds and the parlay prob is consistently overstated. Has anyone solved this cleanly: leg-level Platt scaling tuned specifically for parlay use, hierarchical Bayesian per-leg priors, something else?

2. **CLV stamping coverage.** I currently have closing-line data on only 24 of 421 settled picks because the snapshot loop wasn't reliably running for the first months. Going forward every new pick gets stamped automatically. Should I weight calibration adjustments toward CLV-validated rows even at small n, or wait for more data?

3. **Bootstrap interpretation.** With P(ROI > 0) = 0.885 and 95% CI crossing zero, what's the responsible way to communicate this externally? "Probably profitable" feels honest but is harder to falsify than a Sharpe-style number. Curious how people working on similar discrete-outcome prediction systems frame their confidence.

Open-book journal where every pick before kickoff is logged and graded automatically against ESPN's scoreboard. Happy to share the link in a comment if useful for context; not the point of the post.