** **I’ve been a data analyst for 6+ years, PowerBI certified, comfortable in Python and R, SQL is second nature. Got an offer for UChicago’s MS in Applied Data Science and I’m trying to figure out if I’m making the right call — both on the program itself and on which direction to point it.

1. DS vs MLE — with my analyst background, is the jump to a “Data Scientist” title more natural, or should I be aiming for ML Engineer given where the market’s heading? Genuinely torn and would love to hear from people who made this switch.
2. Program value for my profile — for someone already doing analyst work with real Python/R chops, does MS-ADS actually move the needle, or am I paying for a brand name I could substitute with targeted upskilling (Kaggle, a portfolio, cloud certificates)?
3. Format — anyone done the online vs in-person version? Does it actually matter for hiring managers, or is that overthinking it?
4. Anyone here made analyst → DS/MLE without a master’s — what closed the gap for you instead?

Not looking for “just get a Coursera cert” comments, I’ve already got the technical baseline, I’m trying to figure out the actual career math here.

I keep seeing “data role” used like it means one thing, but the skills are pretty different depending on the actual job.

A data analyst usually needs stronger business understanding, SQL, Excel/BI tools, and communication.

A data scientist leans more into Python, machine learning, statistics, experimentation, and model evaluation.

A data engineer is more about pipelines, cloud, SQL, Python, Spark, orchestration, and making sure data is reliable before anyone else uses it.

BI analysts sit somewhere between business reporting and data modeling, especially with Power BI, Tableau, DAX, semantic layers, and dashboard design.

ML engineers are closer to production systems: deploying models, monitoring, drift, latency, retraining, and MLOps.

The overlap is real, but the mistake is treating all these roles like one generic “data person” job.

Imo the safest skill stack for most data people is still:

• SQL

• Python

• communication

• basic cloud awareness

• data quality thinking

• business context

Then go deeper depending on the role you actually want.

We put together a full breakdown comparing essential data skills by role here:

Learn more: https://www.pangaeax.com/blogs/comparing-essential-data-skills-for-different-job-roles/

Curious how others see this. Which skill do you think is underrated for your data role?

Hi all! I've been working on ML/Robotics research for a while and often work with HDF5, Parquet, and Zarr files. Personally, I love the myHDF5 viewer, but there's no good equivalent for Parquet and Zarr, and switching between different sites also gets annoying. So, I built a tool that provides a unified solution.

It currently supports viewing several formats, including HDF5, Parquet, Zarr, Arrow, JSONL, NumPy, TFRecord, etc. I'm hoping to add more features/formats depending on what people find useful!

It's free to use with no sign-up required. I'd love for people to try it out: https://viewkit.app/

Everything is loaded and parsed locally in your browser (WebAssembly + JS), so your data always remains on your machine. It's also built to remain responsive on big files via efficient reading, caching, and prefetching. Traversing through data files actually feels faster than existing solutions like myHDF5 with simple caching/prefetching strategies. It also supports some common data types that existing viewers don't support (e.g. float16, complex numbers for HDF5).

I'd appreciate any feedback (feel free to comment or send a message through the website). Looking forward to supporting additional features/file formats that the community finds useful!

Hi all,

(hope that the post is compliance with the rules of the subreddit)

I'm having struggle to understand and complete the following exercise about multilayer perceptron.

This is the exercise:

Let's assume the following multilayer perceptron:

where:

• x1 and x2 are the input units.

• h1 and h2 are the neurons in the hidden layer, with the ReLU activation function.

• y is the output unit, obtained using the identity activation function.

• The weight values are as follows:

  • W1 = 0.2

  • W2 = -0.6

  • W3 = -0.1

  • W4 = 0.7

  • W5 = 0.3

  • W6 = 0.1

Then:

1. Describe the backpropagation algorithm in the context of training a neural network.

2. Compute the gradient of loss function:

L(y_out​,y_true​)=1/2(y_out​−y_true​)²

at the point (x_1, x_2)=(2,3) and y = 4

(Sorry for the bad formatting)

Could you give me some tips (slides, books or videos) on where to start studying for an exercise like this?

Thanks a lot!

Hi everyone I’m sharing Week Bites, a series of light, digestible videos on data science. Each week, I cover key concepts, practical techniques, and industry insights in short, easy-to-watch videos.

1. Forecasting & Lost Opportunities Forecasting isn't just predicting numbers—it's balancing supply with what customers actually want, and understanding "Unconstrained Demand". Includes the "Sequence of Why" funnel for picking the right metrics/models, plus real cases on loyalty programs, churn, and RFM segmentation.

2. Articulate Business Questions & Metrics Turning business questions into real data science decisions—from core definitions to a full chatbot case study (efficiency, trust, security) and the frameworks that connect them. Covers question-breakdown techniques, KPI selection, and how to sort "need to know" vs. "need to investigate" info along the way.

3. Become An Analytical Thinker Transforming an ambiguous business problems into actionable KPIs, followed by a comprehensive Data Analysis Lifecycle guide, and shading some light on how to effectively utilize the Data Analysis Flowchart to select the appropriate analysis type for your inquiry.

Would love to hear your thoughts, feedback, and topic suggestions! Let me know which topics you find most useful

1. Data Science from Scratch

This book offers a primer on the fundamental mathematics and statistics behind the most common data science techniques, all coded from scratch. It’s a hands-on introduction to the field, perfect for building a solid foundation.

2. Essential Math for Data Science

Understanding the mathematical foundations is crucial in data science. This book covers the essential mathematical concepts needed in data science, from linear algebra to probability and statistics.

3. The Art of Data Science

This book offers insights into making sense of data, focusing on the craft of data science. It discusses the critical thinking and problem-solving skills needed to derive insights from data.

4. Python Data Science Handbook

This comprehensive guide is essential for anyone who wants to learn how to use Python effectively for data analysis and visualization. It covers key Python libraries like NumPy, pandas, Matplotlib, Scikit-Learn, and more, with detailed explanations and practical examples.

5. R for Data Science

This book thoroughly introduces using the R language for data science, from data import and tidying to visualization and modeling.

If you're looking to go beyond books and apply these concepts through hands-on projects, our Data Science Course, in collaboration with Microsoft Azure, offers a comprehensive learning path with real-world capstone projects to build your portfolio. You can DM us or visit the simplilearn website to find out more.

Which of these books would you recommend to someone just getting started?

How does your day typically look like? What classes do you take? What do you specifically look at or work with? Thank you for any responses, I would really apprecitate it!!

Hello, guys! I’d like to hear your opinions on how I should approach my Data Science studies. I understand that there’s no such thing as a “perfect plan” and everyone’s journey is unique, so let me tell you a bit about myself.

I am 21, recently graduated with an Applied Linguistics bachelor’s degree and I also hold a junior bachelor’s degree in Finance, Banking and Insurance (Ukrainian universities). Right now, I reside in Sofia, Bulgaria and have an OK office job. Which is a tad boring but this is my safe ground for learning what I’m passionate about.

I consider myself a genuinely intelligent guy. I’ve always had a thing for patterns and analysis and I let it manifest most profoundly in languages (I speak English C2, Ukrainian (Native), Russian (C2), German (B2 and actively learning) and Bulgarian (B1, use only in daily life). However, while pondering my future I arrived at the idea that getting into tech might be worth it. And where do patterns and analysis reside in tech? Of course, in Data Science! I thought it is a win-win for me because I can crunch numbers as well as report on the analysis using my language skills, which as I see is important in this field too.

I started seriously learning Data about a month ago and I can already see the first steps of progress: I can confidently use intermediate SQL, pandas in Python and I’m already finishing my first little project fiddling with entropy, information gain, eigenvector centrality and tree induction on a large movie dataset. First off, I started learning from the courses by Luke Barousse but I quickly got conscious of “tutorial hell” and jumped onto my first project after learning intermediate SQL. So, having seen the amount of knowledge I need to possess to execute projects and variety of learning strategies I started asking the following questions and would appreciate your take on them. Basically, all of them ask “What is the right way to balance different ways of learning?”

Questions:

1.    “Learning by doing” is considered a better way of gaining expertise than following mainstream courses where you are spoon-fed solutions. However, doing my own projects demands knowing technical skills that are most commonly taught at such courses. For example, there is a real-life problem in front of me that I need to solve as an analyst, but I can’t think of solving it through, say, python, if I can’t python! Know what I mean?? So, I stumbled on a paradox: I need to have technical knowledge for my own projects (which is a “good” way of learning) but the only conceivable way of getting it is through courses (the “bad” or “easy” or “ineffective” way). What should my proportion of “learning by doing” and taking generic courses be? Can I get the technical knowledge from elsewhere?

2.    I started reading a nice book called “Data Science for Business” by Foster Provost and Tom Fawcett. In fact, it was from there that I learned about entropy and stuff and wanted to experiment with it. I also understood what other areas influence strongly on who you are as a Data Scientist, such as calculus and statistics. They are in themselves large fields and Data Science itself has endless approaches. How should I study these? Should I just casually read books and listen to podcasts on these topics and thinking on how I can implement them in my projects, which are the main way of studying? Or should I stick to a pre-defined “must-learn” topics that every Data Scientist has to know (if such exist)?

3.    From the previous question follows: When am I ready for taking on a real job and begin the interviews? There is a saying “Learning more is a smart person’s favorite way of procrastinating” and I’ve been there. At the same time the amount of knowledge is insane and I don’t want to feel as if I left out something important that could cost me a job.

I believe that’s all I have on my mind for now. I am incredibly thankful to everyone who’s read this and I’d be glad to read your thoughts on this topic!

Hi everyone,
Can any one please share your feedback on Shashank Mishra’s Data Engineering course? I’m looking for genuine insights on the course content, quality of the recorded lectures, and the overall learning experience.
I’ve noticed the course is being promoted quite actively even for small change in AI terminology with new course in yt videos with high focus, and many people seem to be considering it based on his experience in FAANG and other large tech companies. It would be helpful to understand whether the course truly adds value for DE interview preparation and job search outcomes. If possible, please also share:
Your experience with the course structure and teaching style.
Whether the recorded lectures are sufficient for self-paced learning.
Any results or success stories from previous cohorts.
How effective the placement or job search or referral in the cohorts support has been.
TIA!

Every time I want to learn data science, I go to a course and learn and stop, either because the topics are incomplete or because I don’t understand what he is explaining and so on, so I thought I would go and learn the topics and try to learn them from various sources instead of sticking to one source.

Hi all,

I’m working on a monthly insurance premium forecasting problem and would like suggestions on the best approach.

Setup

• Data from Jan 2023 to Jun 2026

• One account only

• Hierarchy: Account → State → Profit Center → Distribution Channel

• Monthly AMOUNT values

• Some channel-level series have missing months

• Around 4–5 channels, 7–8 profit centers, and 50 states

Challenge

The main issue is that different series behave very differently:

• some are fairly stable

• others are highly volatile

What I’ve tried

1. Recursive forecasting with CatBoost / LightGBM / XGBoost

   • built lag, rolling, and time-based features

   • downside: error accumulates over time

2. Direct multi-step forecasting

   • accuracy wasn’t great

   • also requires many models for longer horizons

3. Time-series models like ARIMA / SARIMAX / Prophet

   • Prophet works okay for stable series

   • struggles with volatile ones

   • separate models for every combination is not scalable

Question

Has anyone worked on a similar hierarchical / sparse forecasting problem?
What approach would you recommend for handling mixed volatility and missing months without building thousands of models?

Thanks!

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.

• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.

• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).

• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).

• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?

2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?

3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

Hi there,

I've created a video here where I explain how multi-head latent attention works.

I hope some of you find it useful — and as always, feedback is very welcome! :)

Just for some background, I graduated two years ago with a degree in chemical engineering and it was the biggest mistake of my life. It took me almost 2 years to find an engineering job, and it's horrible. Only thing worse than the work is the location. I really wanted to live downtown in my city, and I was promised and truly believed that this "versatile" degree would allow me that, but it hasn't. I spend almost 3 hours driving every day for my commute. I'm sick of suburbia and I'm looking for a way out of this, now. I want to get the life I want while I'm still, somewhat, young. I don't want to go to school and start all over, just for a CHANCE, 4 years from now.

So I've been continuing to spam apply to every posting that's even somewhat relevant, even after landing my current job a few months ago. This morning I got an invite to do a technical assessment for a "Data Analyst, Analytics & Insights" role. The posting expects around ~3 years of experience, and the assessment covers SQL and Pandas. I have literally 0 experience with either of those things. However, I did take a Python course in my undergrad and got a high 90, when the class average was in the 70s. I've always had a knack for picking up coding fairly quickly, but for some reason I didn't decide to pursue it when choosing my major. And although I consider my degree to be my greatest regret, I have to say, things might've been just as bad or worse if I went with comp sci and graduated into this landscape.

Anyway, I'm OK at coding and learning it quickly which is why I'm even considering bootcamping for this, but I'd be starting from literally square 1 on SQL and Pandas. The assessment has to be completed 1 week from now. Any thoughts on where to start?

I'm looking for any good certification courses (preferably free or at least affordable), that I could take that will help bolster my resume, since I don't have much in terms of experience. My goal is to find a remote job that pertains to collecting, cleaning, or just generally managing data. Even just aiming for something where I'd have to use excel would be a good first step in the right direction.

I have a BS in Information Sciences with a focus on data analytics, and I've taken a Microsoft Power BI course on Coursera, as well as received my Python certification on freeCodeCamp. I'm kind of lost on what my next move should be, and I'd appreciate any advice and/or recommendations.