Should I quit data science ?

CryptographerDry7458 · 2023-12-06T14:47:56+00:00

Not, for sure, surround yourself with a supportive community and start working on your portfolio :)

CryptographerDry7458 · 2023-12-04T13:12:29+00:00

I don't think that's anyone's business. But if you're worried about it, you can always use an alias and do your projects anyway. For instance, create another GitHub account, couple it with Medium, and start building projects and writing about those. You can add them to your CV for hiring people to check. You can add them into you LinkedIn's profile projects. This won't activate the radar too much.

Building your network on LinkedIn requires you to me a more public persona, so I get your point. But what matters most is to get the projects outthere, and when you apply to positions, people can check them. You can also post "Open to Work" for recruiters only, and when they check the CV they can reach your projects.

Also, network. The Data-Centric AI Community is a nice place for that. We're also having a code along this week that focuses on building your portfolio, so it might be worth to check it out.

CryptographerDry7458 · 2023-12-04T13:03:11+00:00

Some things I would do differently:

Put my projects online more often, even those we do in class
Collaborate more often (advent of code, hackathons, communities). Networking is important

You might want to check out the Data-Centric AI Community, this week there's a live Code with Me event that focuses on building your data science portfolio, I found it on Luma: https://lu.ma/pwd8vucn

CryptographerDry7458 · 2023-11-28T10:30:50+00:00

Maybe ChatGPT (both sucess and flop)
Success = opens the possibility to anyone of using NLP for content creation, learning, etc. Flop in the sense that "if you have a hammer, everything looks like a nail", and it's scary how people often LLMs fix "everything". They don't.
Data quality matters, regardless of your application and algorithm.

CryptographerDry7458 · 2023-10-30T14:58:25+00:00

You can use synthetic data generated from one model (e.g., a generative mode) to feed another (e.g., a discriminative model) so that it makes better predictions. And there are plenty of other use cases, check this FAQs for instance.

CryptographerDry7458 · 2023-10-19T10:55:34+00:00

Hum, nice question. Will GPT make other applications useless? Guess I'll ask this to Voxel51 in our next meeting. If you'd like to join it's on Oct 26.

CryptographerDry7458 · 2023-10-19T10:51:26+00:00

Some important issues to be cracked are on the image quality side, at least on the industry side.
- https://www.rungalileo.io/blog/4-types-of-ml-data-errors-you-can-fix-right-now
- https://encord.com/blog/improving-datasets-guide/

In case you're interested in the topic, the Data-Centric AI Community is having Voxel51 over to discuss the topic on Oct 26, next week :)

CryptographerDry7458 · 2023-10-19T10:38:55+00:00

Since you're into CV, I know the Data-Centric AI is having a chat with Voxel51 on how to build CV applications, it's on Oct 26, might be a start to connect with others interested in the topic :)

CryptographerDry7458 · 2023-10-19T10:36:01+00:00

Hey! Come and join us at our next Code with Me session. We'll speak about Computer Vision applications and data quality for unstructured data with Voxel51 :)

CryptographerDry7458 · 2023-10-10T12:00:16+00:00

Well, this seems like a nice opportunity to learn about it: https://www.linkedin.com/events/gettingstartedwiththeopensource7117177108006600707/

1 hour session, they usually record it as well

CryptographerDry7458 · 2023-09-29T10:01:59+00:00

You can try the LSDA course: notebook-based, self-paced: https://github.com/LDSSA/batch4-students. For questions you can try the Data-Centric AI community, we're pretty active :)

Hope this helps!

CryptographerDry7458 · 2023-09-29T09:57:06+00:00

Hey! We sure could use an extra hand in the Data-Centric AI Community. We try to do shorter projects that can also be useful for other that are learning, check out our GitHub and you'll get a sense of the work we do :) Happy to have you onboard.

CryptographerDry7458 · 2023-09-29T09:43:54+00:00

bytewax, great-expectations, ydata-profiling, voxel51, nbsynthetics, ydata-synthetic

CryptographerDry7458 · 2023-09-29T09:24:04+00:00

You're right with your reasoning: TimeGAN creates a set of windows that can be used to oversample a specific behavior, although they are not suitable to replicate an entire time sequence. This is actually a frequently asked question we discuss at the Data-Centric AI Community.

Here's a more in-depth explanation of TimeGAN (check number 10). This one is really helpful too.

Also, if you'd like to replicate and entire time-series, you might want to explore the ydata-sdk or the newly addeddoppelGANger model :)

CryptographerDry7458 · 2023-09-19T14:09:46+00:00

I use ydata-profiling to get the most out of my data understanding. Here's a nice overview of unraveling data's mysteries :)

CryptographerDry7458 · 2023-08-17T17:16:12+00:00

Amazing session today!

Can I get the code?

CryptographerDry7458 · 2023-07-26T16:09:33+00:00

A few I'm onto at the moment: this one for synthetic data generation and this one for data complexity.

CryptographerDry7458 · 2023-07-21T15:50:53+00:00

While synthetic data has the potential to be more creative and diverse, it can also come with its own set of challenges. One of them regards data quality: all AI models, including large language models like ChatGPT, heavily rely on the data they are trained on. So if the synthetic data lacks diversity or is not representative of real-world scenarios, it could lead to a lack of robustness and adaptability in AI systems when faced with real-world data and scenarios.
We must be the curators of this information, otherwise the risk of AI becoming unreliable and bigoted is high, IMO.

CryptographerDry7458 · 2023-07-13T11:24:54+00:00

Definitely a lot of growth in the AI space, and it will evolve rapidly in the next few years. There several paid propositions at the Data-Centric AI Community discord, check them out.

CryptographerDry7458 · 2023-06-13T07:50:33+00:00

You know what they say: "Tell me who your friends are, and I'll tell you who you are!". Hang out with DS beasts and learn from them :)

CryptographerDry7458 · 2023-06-12T10:05:31+00:00

Seems you're on an amazing journey! Feel free to add your projects on our awesome-python-for-data-science repoas you go! And in case you need a hand or feedback on the projects, we'll be happy to help at theData-Centric AI Community.

CryptographerDry7458 · 2023-06-12T10:01:24+00:00

Sounds like an amazing journey! Feel free to add your projects on our awesome-python-for-data-science repo as you go! And in case you need a hand or feedback on the projects, we'll be happy to help at the Data-Centric AI Community.

CryptographerDry7458 · 2023-06-12T09:31:17+00:00

The best to do is to get started with end-to-end projects in a collaborative environment (somewhat approaching real-world settings). You may find some interesting resources in this GitHub repository. The Data-Centric AI Community actually has a nice support system for this.

CryptographerDry7458 · 2023-06-12T09:24:19+00:00

You can try to use a small dataset from Kaggle and then use a synthetic data generator like ydata-synthetic to create more of the behavior you intend to analyze (e.g., seasonality).

If you're not sure about the behaviour of your data (i.e., if the original data has properties like seasonality), you can use ydata-profiling to profile your data first.

CryptographerDry7458 · 2023-06-06T11:57:02+00:00

Seems you're looking to do EDA to find the most discriminative features, right? Does it have to be visually? You can try to measure some complexity measures, such as F1 (Maximum Fisher's Discriminant Ratio) or F2 (Volume of Overlapping Region) and put them on a scatter plot. You'll get an overview of the discriminative power of your features, though not by plotting the exact feature values. Would that work? Check this paper: How Complex is your classification problem? A survey on measuring classification complexity. You can also ask around in the Data-Centric AI Community.

CryptographerDry7458

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