Vilvik is a cloud-based platform for solving optimization problems using evolutionary algorithms. Currently it supports the genetic algorithm. Try it at https://vilvik.com

It is very interactive and offers a lot of demos and problems to quickly get started with. It is flexible and offers a lot of features.

Hope you can try it.

I've been an operations analyst for 5 years, mostly Power BI and a lot of SQL I picked up on the job. My team lead keeps asking for things our dashboards can't do, like cleaning vendor files that change layout every month and break my Power Query steps, so I end up doing it by hand in Excel every week. I've decided to pay for a data analyst course that starts at my level instead of explaining what a bar chart is, and I'm comparing Boot.dev's data analyst path and Pluralsight. Which would you pick for an analyst who needs useful Python?

Hey, people working in the IT sector as data scientists!

I’m currently studying Data Science with AI/ML, and I genuinely want to make a career out of it. However, people around me keep saying that data science has no future and that I should reconsider my decision and switch to another field. Honestly, I feel like it’s too late to change paths, and I genuinely don’t want to give up on something I chose for myself. I want to prove that I can make it work.

I’m open to suggestions and advice from experienced data scientists. What should I focus on, and what should I avoid while learning? Which projects would help me build a strong portfolio and improve my chances of landing my first job as a fresher?

I’d also appreciate recommendations for free online courses that I can take alongside my current course to strengthen my skills and help me build a successful, long-term career in this field.

Any advice, suggestions, or personal experiences would be greatly appreciated. Thank you!

Pandas makes working with data look simple. But some of its most powerful features can also produce surprising results. 😲

For example:❔ - What's the difference between loc and iloc, and why does slicing behave differently? - Why can adding two Series produce NaN values, even when neither contains missing data? - When does assigning a Series to a DataFrame column align by labels rather than position? - How do Boolean masks, where(), and mask() differ? - How do you select and manipulate data with a MultiIndex?

Pandas is much more than rows and columns. Understanding its indexing, alignment, selection, and assignment rules can save you from some very subtle bugs. 🪳

🔍 This interactive 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 example explores these concepts and many more, with short explanations for each operation. 👣 Step through the Python code, see what changes, and experiment with your own modifications: 👉 Pandas operations

💬 Which Pandas operation took you the longest to fully understand?

See more 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 visualizations.

Ever wondered why PySpark runs out of memory, even when you're working with a powerful cluster? 🤔

PySpark is widely used for large-scale data processing in Machine Learning and AI.

But distributed computing doesn't automatically eliminate memory bottlenecks.

I explore:

✅ Memory Architecture - How JVM heap, Executors, and Python workers consume memory.

✅ Shuffle & Dependencies - Why data redistribution matters for memory consumption.

✅ Common OOM Causes - Data skew, high cardinality, and concurrent tasks.

✅ Diagnosing Bottlenecks - Using Spark UI, physical execution plans, and spill metrics.

✅ When to use PySpark - When is distributed computing actually worth it?

📄👇 Read the full article here!

When I first started diving into Machine Learning, almost every course drowned me in math and dry code on day one. I’m a visual learner, so I started building animations to explain these concepts simply before writing any code.

In my first video, we answer the core question: How do machines actually learn?

In traditional programming, we give the computer Rules + Data to get Answers. Machine Learning flips this upside down. We give the computer the Data + Answers, and it mathematically figures out the Rules.

I animated this entire 4-minute breakdown on my channel, VisualForge AI. If you are a visual learner transitioning into tech, I’d love your feedback!

🔗 Full 4-Minute Video: https://youtu.be/iq4bypuvMKE?si=XIVqY7gpdhjzkX7t

If you found this visual breakdown helpful, please drop a like, share it with anyone learning ML, and subscribe to the channel! It really helps support the project.

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

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

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.