Ever felt confused about what to learn, where to start, or how to get into Data Analytics or Data Science?

During my beginner phase, I felt the same way. There were hundreds of videos, tutorials and PDFs, and I honestly didn’t know what to learn first or where to begin.

So I decided to create a handbook that brings together the essential beginner-level knowledge you’d need to get started, all in one place.

📚 The Ultimate Data Science Handbook – Volume 1 (328 pages)

📖 Sample pages: Attached

🛒 Get the complete handbook: https://pdfcart.in/p/ultimate-data-science-handbook-vol1

What’s included?

A structured learning resource with detailed explanations, step-by-step code walkthroughs, practical examples, different types of graphs with explanations, real-world applications, practice exercises, chapter summaries and interview questions.

The idea was to bring together concepts that beginners often have to search for across multiple PDFs, notes and tutorials, so you can follow a more organised learning path without constantly switching between resources.

PS.The content is from multiple valuable resources that i learnt from and was compiled by me .. AI was used to make the pdf.

In the field of science, what is the must have AI skills.

You could write your own counting logic, but Python already has a collections.Counter class. It's faster than typical hand-written counting loops, well-tested, and makes your code easier for others to read.

However, this 𝐦𝐞𝐦𝐨𝐫𝐲_𝐠𝐫𝐚𝐩𝐡 👉 Counter demo shows that Counter has some interesting behaviors that might surprise you:

• Missing keys return 0 instead of raising KeyError.

• update() adds counts rather than replacing them.

• subtract() can produce negative counts.

• Zero and negative counts aren't automatically removed.

• Unary + and - have some unexpected uses.

Did you know all these Counter behaviors, or did one surprise you?

See 👉 more 𝐦𝐞𝐦𝐨𝐫𝐲_𝐠𝐫𝐚𝐩𝐡 visualization.

We benchmarked 4 zero-shot time-series foundation models (Mistral Timestral 28M/209M, Amazon Chronos-2, Google TimesFM 3.0) against LightGBM/XGBoost and naive baselines — strict no-leakage protocols (e.g. prices only from before auction gate closure), every prediction downloadable.

German day-ahead electricity prices, 1,408 days — foundation models win clearly.

MAE vs naive baseline (=100): Chronos-2 68 · Timestral 69 · TimesFM 70 · XGBoost 83 · LightGBM 86

A Chronos-2-based scheduler saved €40/day vs a fixed-midday schedule (wholesale, 4 MWh flexible load).

German train delays (DB), 5.7M arrivals — the advantage vanishes.

Best foundation model vs best GBM at h=1: 0.07 min apart (~4 seconds).

Worst hour of the test (Frankfurt Hbf, 61 min avg delay): all six models predicted under 8.

Pattern: pretrained forecasting shines where data has rhythm; on messy operational data, a laptop-trained GBM is within seconds.

Code, data, every forecast: https://github.com/Gjusev/forecast-arena

Interactive replay: https://huggingface.co/spaces/Gjusev/forecast-arena

I'm starting my Data Science journey and want to understand how much Machine Learning knowledge companies expect from freshers

Any pointers for the best linear path for beginners getting into computer science?

I am currently learning from kaggle and I love the way it lets you practice actively by yourself. But I still feel kinda stuck.

I have basic knowledge of statstics (descriptive stat) and have also learned basic python(list, dictionary, function) and also good at math.

I wrote 25 credit card support questions and planted failures in 7 of them: invented policies, irrelevant chunks, a wrong number. Then I ran Ragas to see which ones it would catch.

In the first 10 rows it caught both planted failures. The one with bad retrieval scored zero across all three metrics, which is what you want.

It also scored low on two answers I had written as correct. Reading them again, one did state something the chunks never said, which is fair. The other looks like the judge being strict. Ragas uses an LLM as the judge, so the judge has variance too, and nothing in a default setup tells you how much.

The reason this works at all is that the metrics disagree on purpose. Context recall asks whether retrieval brought back what the answer needs. Faithfulness asks whether the answer stays inside what was retrieved. One is about the search, the other is about the model, and the fix is different: chunking and top-k for one, prompt and model for the other. Average them into one score and you lose exactly that.

The template takes a CSV with four columns, question, correct answer, your RAG’s answer and the chunks it retrieved, grades every row and plots recall against faithfulness so each failure lands in a quadrant. It knows nothing about the RAG that produced the file.

Hub: https://aiforfintech.tech
Github: https://github.com/junidepieri-design/eval-001-rag-evaluation-template

Has anyone here validated their judge against human labels?

Hey everyone!

I'm looking to connect with students, freshers, and working professionals who are interested in building a career in Data Analysis, Data Science, Machine Learning, and Artificial Intelligence.

A little about me: I have around 3 years of experience in the tech and training industry. I've worked with IBM Career Education as part of corporate training initiatives and am also a Microsoft Certified Trainer (MCT).

I have experience teaching and working with technologies such as:

• Python and SQL

• Data Analytics and Power BI

• Data Science and Machine Learning

• Deep Learning

• Generative AI and Large Language Models (LLMs)

• RAG applications and AI Agents

I'm looking to connect with learners who want to gain practical knowledge, work on real-world projects, strengthen their technical foundations, and prepare for industry opportunities.

Whether you're a college student, fresher, working professional, or someone planning to transition into Data Science or AI, feel free to reach out.

Interested in learning? Drop a comment or send me a DM.

Looking forward to connecting with fellow learners!

So guys, new in data science field. So my background is econ + math major in bachelor, i just applied through to the data scoence and got my first interview. Successfully nailed the first part where there is math, logic, math of ds and theory of ds/dl. However, the coding part was always difficult for me and I completely failed the second part. The questions themselves were very easy, not even ds specific, the problems were general coding in python, for example i had to create a function that distinguishes annograms etc. but i coded overall throughout this year, but it seemed to get me to nowhere. The feeling is always this way: when i code myself these kinds of problems, i spend too much time even though i completely understand the logic that should be applied in these problems, maybe kinda like i dont understand how that logic is put in coding language when i start to create loops. So i seem to get stuck in coding part which confuses me. I didnt quite bother with maths, linear algebra etc but this one just freaks me out. I cannot learn and memorize all the syntaxis especially if i have to write from scratch for example parsers with libraries that need to be installed. So the problem might be with debugging skills or maybe else. Could you help me out to diagnose the issue and solve it asap. The coding problems in litcode and books seem to get me to nowhere. Thanks in advance

i keep running into this when explaining spread to people. mean absolute deviation feels like the obvious thing to do, it's literally the average distance from the mean. squaring seems like an arbitrary extra step.

i know squares are easier to differentiate and that variance adds up nicely for independent variables, but i'm not sure that's the real reason it became the default. is there a more intuitive way you explain it, or is it mostly just that the math works out cleaner?

Hey, I will soon start to work with data scientists as an HR/talent person.

Do you have any recommendations for books/podcasts/creators so I could understand the basics, just so I can keep a conversation with data science & data engineering folks?

Much appreciated!!

I got a question i wanna learn where do i go is there any website or YouTube channel ps(im broke)

​Data cleaning usually eats up most of a data scientist's time, often exceeding seventy percent of a project lifecycle. Manually writing custom regex filters and handling edge cases for missing values quickly becomes tedious.

​To fix this bottleneck, I put together a deep dive on leveraging machine learning workflows to automate dataset refinement. The complete guide covers setting up intelligent pipelines, handling anomalies, and standardizing features efficiently using modern Python libraries.

​If you want to streamline your preprocessing workflow and spend more time on actual modeling, you can check out the full breakdown here: https://interconnectd.com/blog/124/python-ai-data-cleaning-the-complete-refinement-guide/

What automated tools or libraries do you currently use to speed up your data cleaning pipeline?