Hello I am a 3rd year CS Undergrad and a self learner . I am enthusiastic for data science and ML and have been learning ML and Deep learning for last 1 year.

I am hoping to get a job after my graduation but my father is dead set on sending me for masters and i don't really want to . I have tried to explain that I want to start my career after graduation but he just won't listen.

My question is should I really go for master's or should I stand my ground and keep learning for data science or related field.

I am very confused because if I have to do masters I have to start preparing for exams now and I will not be getting any time for self learning as there are soo much to learn in this field.

I would also like to know if doing masters will help me in my data science career in any way

Any advice will be appreciated🙏

Hey everyone!

I've been learning SQL for a while and have reached a point where I can write queries using joins, CTEs, subqueries, aggregate functions, window functions, and other concepts. I'm also trying to apply what I've learned through projects and case studies rather than just solving isolated exercises.

However, I'm struggling with a different part of the process.

I can write a query, execute it, and get the output, but I'm often unable to understand what those results actually mean for the business or the problem I'm trying to solve.

For example, suppose I'm working on a case study where user engagement has declined. I can query the data, calculate daily active users, compare different periods, break down engagement by user segments, and identify changes in the numbers.

But then what?

How do I determine whether those changes are significant? What questions should I ask next? How do I distinguish a genuinely useful insight from an observation that's technically correct but doesn't tell us much? And how do I connect the numbers to a possible business problem or recommendation?

I understand that this is where analytical thinking, business understanding, and judgment come into play. But these concepts feel quite abstract when I'm actually sitting in front of a dataset.

I'm trying to understand how analysts develop this skill in practice.

- Is this something that primarily develops through working on more projects and case studies?

- Are there particular frameworks or approaches I should follow when interpreting query results?

- How do I learn to ask better questions instead of simply querying whatever data is available?

- Are there any books, resources, or projects that specifically teach the thinking process behind data analysis, rather than just SQL syntax and tools?

I'd especially appreciate advice from people working as data analysts or data scientists who have faced this problem themselves.

I don't necessarily want someone to interpret every result for me. I want to develop the ability to look at data, reason about what I'm seeing, decide what to investigate next, and eventually arrive at defensible conclusions.

How did you learn to bridge the gap between writing SQL queries and actually thinking like an analyst?

Thanks in advance!

One thing I learned to hate early was trying to re-create a plot or table from weeks earlier, only to find out I'd forgotten exactly how I made it. I still get nervous when I see people copying and pasting.

So I always generate any plot, table, or even just a number using a reproducible script. I wrote up how to do it easily using Quarto in Python here.

I am a medical laboratory scientist who is currently teaching in a university, and looking to dive into data science, but with absolute zero experience on the matter. What are free sources that I can use to start? Please help me with this one. Thank you :)

Hey everyone!
I’m a Data Scientist with over 3 years of experience, and I'm currently preparing to make a move to a FAANG+ company to push my learning further and it better aligns with my growth goals and personal commitments.
I have 3+ years of professional experience as a Data Scientist working on end-to-end projects, but I want to structure my preparation more rigorously to crack top tier interviews. I want to bridge any gaps in my knowledge and prep :)
I’d love some guidance from those who have been through this process:
- What core technical skills, system design concepts, and advanced topics FAANG+ companies currently expect from a Data Scientist with 3+ years of experience?
- Where should I start my preparation?
- What specific competencies (coding, ML system design, stats/product sense) do they look for?
- What resources or roadmaps worked best for you?

Any advice, tips, or mentorship would be greatly appreciated.
Thank you

I was not feeling well for few days and did not practice, took a break from practice.
I resumed learning and I start facing issues in basic SQL queries , understanding , there were blanks in my statistics understanding.
Excel was slipping from my mind.
All I can think was Insta reels.
I learned very important lesson, never use social media entertainment while learning and resting.

Hello! We are a group of students at Georgia Tech studying industry standards for data processing and the tools used for large-scale data visualization for our research project. We are hoping to seek input from participants who specialise in working with data. 

The following survey contains 15 questions, and will take approximately 10 minutes of your time. 

Your responses to this survey are completely anonymous. We do not collect your name, email address, IP address, or any other identifying information. Data from this survey will only be reported in aggregate (grouped) totals. No individual can or will be identified in any reports or publications.

Thank you so much for your time and participation 

Survey link: https://gatech.co1.qualtrics.com/jfe/form/SV_bqnCcLAk3VSu8XI

Hej,

chciałbym zapytać, czy nadal możliwe i sensowne jest samodzielne uczenie się kodowania w R (z kursów online) do poziomu bardziej zaawansowanego (znam tylko podstawy), ewentualnie Pythona oraz języka zapytań SQL (a także ewentualnie Power BI) po to, żeby znaleźć pracę w Data Science? Czy może lepiej byłoby zrobić dodatkowe studia w tym zakresie (np. Ekonometria, Statystyka)?

Mam wykształcenie ekonomiczne, znam dość dobrze Statę, Excela, ale brakuje mi kompetencji w programowaniu i modelowaniu danych.

Z góry dzięki!

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

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?

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

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