A place to share the personal projects you're doing
Like any other ML algorithm, KMeans clustering also has its own set of pros and cons. Here we're going to discuss a specific kind of disadvantage of this algorithm which is the fact that what appears to be a 'natural cluster' for a human eye is not necessarily what appears to be a cluster from a machine's perspective. #ml #clustering #kmeans #machinelearning #datascience #unsupervisedlearninghttps://www.linkedin.com/posts/fazil-mohammed-4062711b2_understanding-cluster-formation-in-kmeans-activity-6938104670510809088-YGhl?utm_source=linkedin_share&utm_medium=member_desktop_webI couldn’t find an option to upload the pdf. Hence I shared the above link of my post in linkedin
Hi,I wish to launch services for class 12th passing out students.i see many students are confused or unsure which stream they should follow after 12th class. They don’t what exact skill set they have and what they require to enroll into the subject of choice.Can anyone guide me how to kich start in this direction? What all is required out of “Big Data” to make it tailor made?Respect and Regards Rohit Bhatnagar
Hi all! Peak is hosting a hackathon for Manchester University Data Science Society (MUDSS) next weekend. It should be a really great day, with the option for students to take part in a beginners project (minimal coding and data science knowledge required) or a more advanced project (moderate python and data science knowledge required). For some of the students attending, especially those on the beginners project, it’ll be their first hackathon. I’m therefore looking for some advice from you please:How can Peak make the event as great as possible? What have you found works really well (or not so well) at previous hackathons that you’ve attended? What advice can you give to the students to make the most of the day? Thanks so much! If you’re a student at the University of Manchester and this sounds interesting, please get in touch with MUDSS (email@example.com) and sign up 🙂
I had quite an unusual start with programming as the first language I ever learnt was C++. I was only 14 at the time and I had no idea what coding was, so I was thrown in the 🤐 deep (or at least it felt like it). It actually took me quite a while to learn to enjoy it as in the beginning I was doing it out of pure stubbornness after my teacher told me programming is not for me. I used C++ for my first data science related project. Later I taught myself Python and recently I’ve started using R as well, which is quite exciting! No need to say I prefer Python over C++, but I’m glad it gave me a good base when I was so young. What was the programming language you used for your first data science project?
There is no doubt that doing a personal data science project (however small it may be) is a great way to learn something new or improve the skills you already have. That being said, sometimes it might be difficult doing it completely on your own, especially if you’re used to working in a team. What are some best practises to follow when doing a personal project? What should one be mindful of? Tips, anyone? 😅
Not all personal data science projects need to be elaborate ML models built over weeks/months, some can be small day-to-day problems that data science can help you solve more efficiently. This topic is to start a chat around everyday activities that can be aided with a little bit of data science stardust
We’d like to hear some of the cool projects you’ve all done recently.If you’ve done one leave a reply down below 👇 with what the project was, what techniques you used, and what was/will be the end result of it? Cheers everyone!
As a little starter to the conversation I thought I’d share my first personal data science project: I just started learning basic coding principles at uni and through a few online courses but always felt like missing an opportunity to put all the newly learned functions, packages etc. together to craft what would feel like one coherent solution. At the same time I had bought my fist fitness watch but wasn’t entirely happy with the summary stats and visualisations that came with the manufacturer’s app. So I pinged them an email, got an API in response (that I didn’t really know what it was at the time) and started playing around with my data a bit. Let’d be honest, learning data science is a difficult process. I found that working on a problem I’m genuinely interested in represented a great way to maintain enough motivation to push through these obstacles while putting learned skills into practice and further engraining them.The end result was by far nothing to write home about but I’d
My first hackathon experience was back in Jan 2021 when I just started my masters in data science.We found a website that keeps showing a new image every time we refresh the website - and some of them were leaked images including personal information (passport, credit card, etc)! We thought it’d be cool if we could develop a model that decides whether the image includes personal information or not. Also, we wanted to introduce ways to let them know the fact their information has leaked so that they can take actions 👀Although it was quite challenging to crack our project in a limited time as we were all beginners in data science, it was great fun trying to solve a problem together and was a good opportunity to learn new concepts/skills I’ve never known!How was your hackathon experience? Were there any interesting ideas you had worked on?
Welcome to the Personal projects: questions and ideas part of the Community 👋Some ideas for discussion for this section:Ideas you have for personal projects, e.g. if you have heard about a public data set that could be interesting to explore Suggestions for how to approach personal projects for others, e.g. if you have any advice on how to get started on a project Questions on getting started with a personal project, e.g. if you need to get some data using an API and aren’t sure how to approach it
Welcome to the Personal projects part of the Community 👋The purpose of this section is to post about project you’ve been working on in your own time (or maybe at university). When writing about your project think about:Why did you decide to do this project? What data did you use? How did you get the data? What did you do to understand the data? What kinds of methods/analysis/modelling did you apply to the data? What was the outcome? Is there anything you’d like advice from others on for next time?These projects do not have to be super complicated data science projects, they can be anything you like as long as it involves data of some kind.
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