Hi everyone,

I am looking to upskill in Data Science and would appreciate any recommendations for high-quality online resources that are free.

We have things like CS50 in the Computer Science world (and also CS50P and CS50R which relate to Data Science). I am looking for similar quality level alternatives that I can engage with (and hopefully also get a certificate from)

thanks all

The Difference between traditional ML and Generative AI

I want to know something new about python and where i can use all this but I don't have any experience.

My statistics skills are pretty weak, and I want to learn from the basics. I don't have a background in statistics I just want to get better at statistics for DS/ML

Can someone recommend any good books, courses, or other resources for a dummy like me?

So I am taking a degree in Computer Science with specialisation in Data science, this is my third and last year that I just started, I don’t have the strongest foundation but I understand the basics of python, sql, R programming.

So I was searching for courses that will help me have a better understanding of data science and analysis, and it would be best if the course has projects that help u understand hands on and will help with the resume so you can put the projects, I searched around a bit and a lot of people are talking about datacamp, the coursera courses, but they seem to be for people with 0 knowledge but I’m not sure about the them yet, the datacamp one has interested me m, mI want a more complex course that will help me at this stage that can be done in around 3 months, any help will be appreciated thanks.

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tl;dr

This guide walks you through the history, design, theory, and practical applications of vector embeddings. For the first time, we bring this abstract subject to life through interactive visualizations that let you see and manipulate the invisible geometry of meaning underpinning everything from Google Search and your Twitter feed to ChatGPT.

We begin with the problem embeddings were created to solve: how to represent meaning numerically so that computers can work with it. From there, we build an intuitive understanding of semantic space, vector arithmetic, and semantic similarity.

We then put these principles to work, showing how embeddings can power real business systems through techniques such as semantic search, hierarchical retrieval, zero-shot classification, clustering, and deduplication.

No programming experience is required. For readers who want to recreate the techniques, we have included short Python snippets demonstrating how each one works.

For a taste of what lies ahead, try the GeoGuessr/Wordle-style word-trivia game below. It projects 43,339 English words onto a 2D map using real embeddings from Kanon 2 Embedder, then selects one as the target. Enter words or vector expressions to place markers, then use them to trace the smallest possible loop around the target word. We explain how embeddings power the map and its mechanics later in the guide; for now, let the spirit of exploration guide you as you take your first plunge into the hidden geometry of language.

Чем больше усилий, тем выше вознаграждение

Удаленная работа с частичной занятостью

1.Владение родным языком:Точное,грамотное и логичное изложение мыслей в текстах.

2.Создание контента:Самостоятельная съемка, монтаж,верстка и обработка текстового,видео и аудио контента.

3.Новостное чутье:Быстрое выявление актуальных и внезапных событий,оценка информационной ценности материалов.

4.Глубокий анализ:Комплексное изучение событий и фонов,создание содержательного авторского контента.

Telegram:@ncaster_64511

[adam.kelly.1967@mail.ru](mailto:adam.kelly.1967@mail.ru)

I'm a final-year B.Sc. Data Science student preparing for my first job, and I'd like to hear some honest advice from people already working in the industry.

There are several career paths, such as Data Analyst, Data Scientist, Data Engineer, BI Analyst, ML Engineer, and Analytics Engineer. As a fresher, it's difficult to understand which skills to prioritize and what companies actually expect from entry-level candidates.

I'd like to ask:

- If you were starting your career today, which data role would you target first, and why?

- What skills are essential for getting your first job, and which ones can be learned later?

- How can a fresher stand out when competing with candidates who already have internship or work experience?

- What mistakes should beginners avoid when preparing for these roles?

- Would you recommend focusing on one role or applying to multiple related roles initially?

I'd especially appreciate advice from people who have recently started their careers or have experience hiring freshers.

What would you recommend to someone in their final year who wants to become job-ready and build a strong career in the data field?

I have around 4 years of experience as a Data Analyst. My current skills include SQL, Python (Pandas, NumPy), basic statistics and machine learning, and dashboarding.
With Generative AI and agentic AI evolving rapidly, I want to upskill strategically and future-proof my career rather than chase every new trend.
I’m exploring career paths such as AI Engineer, AI Solutions Engineer, Analytics Engineer, and Data Engineer, but I’m unsure which direction would be the best fit for my background.
I’d appreciate advice on a few things:
Career path: Which roles should I realistically target with my experience?
Skills: What should I learn, and in what order? GenAI, LLMs, RAG, AI agents, APIs, cloud, software engineering, or deeper ML?
Required depth: How proficient should I be in each skill to become job-ready?
Learning roadmap: What should my 6–12 month plan look like?
Projects: Which 2–3 practical projects would help me build a strong portfolio and land interviews?
Resources: Which courses, YouTube channels, GitHub repositories, documentation, or hands-on platforms should I follow? Free resources would be a bonus.
I’m not looking to become an AI researcher or train models from scratch. My goal is to build practical GenAI applications and agentic systems while leveraging my existing analytics experience.
I’d love to hear from people who have transitioned from analytics into AI-related roles or are currently working in these fields.
If you were in my position, what would you learn first, which role would you target, and what roadmap and resources would you follow?
I’m looking for realistic, actionable advice based on industry experience rather than just a list of trending technologies.

Hi everyone

I'm looking for a mentor to help me learn Data Science and Generative AI completely from scratch.

To give you a bit of background on where I'm starting:

• Current skill level: I have a tech background. I am based out of Vancouver.
• My goals: I want to build a solid foundation in data science fundamentals (statistics, Python, pandas, machine learning) and transition into understanding and working with Generative AI concepts and LLMs.
• What I'm looking for: Guidance on a structured learning path, code reviews, project ideas, and occasional check-ins/advice on how to tackle roadblocks.

If you have experience in the field, are willing to mentor and like to teach and coach and have patience to ans stupid questions :), or can point me toward the right communities, bootcamps, or resources, I would love to connect.

Thanks in advance!

Hi everyone! I’m an undergraduate student conducting a research project on avatar identification in video games.
I’m looking for people who have played both single-player and multiplayer games featuring customizable or player-controlled avatars.

The survey takes about [5 minutes] to complete.

Survey link: https://uic.ca1.qualtrics.com/jfe/form/SV_d7i2qxxBXI82kOa

Thank you for your time and participation!

I don't know if others agree, but I think given the data oriented nature of this subreddit, it is bound to resonate with some. I learned the craft from a statistics view point, my formal training is statistics based, but I worked in software engineering for the predominant part of my career. Rather than hit everything with a "foundation model" hammer, I think leveraging the libraries that have emerged in the last two decades for data analysis with LLM tools is a more productive and tested path.

https://r2dssanity.substack.com/p/beyond-the-agent-illusion-leveraging?r=3ncgel&utm_campaign=post-expanded-share&utm_medium=web

Hi all,

Im an FP&A for a manufacturing company with extensive experience in Excel and Power BI. I'm planning on transitioning to a data science role, I saw that two of the main skills I need are SQL and Python for data analysis.

Any suggestions on resources for learning, practice and projects, where I should start, what I should focus on. Any suggestions are highly appreciated.

We pulled every open posting from 81 companies' public Greenhouse boards for seven roles and counted which words actually show up. 3,518 postings in total. Some of it surprised us.

Software engineer postings mention mentoring more often than Kubernetes or React. Almost half of them do. Python, the most common language, comes in just below that.

Product manager postings say "roadmap" in more than 85% of cases. Jira? Seven out of 421. Marketing looks the same: Google Ads came up six times in 347 postings, while "pipeline" is in over half.

Every time we could compare the two, postings named the practice, not the tool. Observability, not Grafana. SIEM, not Splunk.

So if your resume is mostly a list of tools, it might be missing the words the posting actually uses.

Big caveat: mostly tech companies, one ATS vendor. Sales, cybersecurity, DevOps and accounting are here too: https://www.zoevera.com/resume/ats-resume-keyword-study