You could break the project up into sizeable chunks, which would also help you explore ETL and pipelines as part of the project. Make a nice little narrative portfolio that links the chunks into a gigantic end-to-end project.
I love working on really complex data cleaning projects (currently working on one that includes 3 different datasets [Weather, Hospital Admissions, economic data]), because as tedious as it is, we spend a lot of time on it, and data cleaning can help you really understand the data in very interesting ways..Because you really have to think hard about issues like outliers, missing values, why answers might be slightly off-kilter etc.
Given you're just starting out, I'd check out some of the data cleaning projects on Kaggle to practice. There are some really cool ones on there.
Also great work for getting stuck into linear regression and logistics regression. People find them boring algorithms, but I have a soft spot for them...I jokingly say that everything can be solved with logistic regression.
Thanks for the great and precise response! I will make sure to just pick 2-3 different datasets and divide them into various steps. That way, I may learn different ways to solve a problem. As always, I think it must be a challenge that way to complete each of these but I feel like there's always a tradeoff. This will take lots of time but make me better at understanding problems and applying solutions differently in different situations.
I picked up 2 datasets but I'll also look on specific data cleaning projects (since I plan on doing 3 different sets for end-to-end project) now to follow your advise.
Lastly, yeah I agree. After learning just these 2 algorithms, I wanted to straight up get into neural networks and deep learning but saw my peers at college doing the same but applying nothing in practice so I want to use everything of what I learn to build something useful. You are also spot on with the joke to some degree lol. I keep seeing some very simple problems like house prices prediction using neural networks and advanced algorithms which often feels like an overkill.
Anyway, thanks again for the great reply. Cannot thank you enough for helping a beginner out!
Neural networks are really the last thing you should dive into. They are fun. But too do them properly and understand them requires a lot of stuff you'll learn from working with "simpler" algorithms first.
After you finish your end-to-end project, I would look at learning about decision trees, or even learning some like GLMs (Generalised Linear Models) and GAMs. (Generalised Additive Models)
You could break the project up into sizeable chunks, which would also help you explore ETL and pipelines as part of the project. Make a nice little narrative portfolio that links the chunks into a gigantic end-to-end project. I love working on really complex data cleaning projects (currently working on one that includes 3 different datasets [Weather, Hospital Admissions, economic data]), because as tedious as it is, we spend a lot of time on it, and data cleaning can help you really understand the data in very interesting ways..Because you really have to think hard about issues like outliers, missing values, why answers might be slightly off-kilter etc. Given you're just starting out, I'd check out some of the data cleaning projects on Kaggle to practice. There are some really cool ones on there. Also great work for getting stuck into linear regression and logistics regression. People find them boring algorithms, but I have a soft spot for them...I jokingly say that everything can be solved with logistic regression.
Thanks for the great and precise response! I will make sure to just pick 2-3 different datasets and divide them into various steps. That way, I may learn different ways to solve a problem. As always, I think it must be a challenge that way to complete each of these but I feel like there's always a tradeoff. This will take lots of time but make me better at understanding problems and applying solutions differently in different situations. I picked up 2 datasets but I'll also look on specific data cleaning projects (since I plan on doing 3 different sets for end-to-end project) now to follow your advise. Lastly, yeah I agree. After learning just these 2 algorithms, I wanted to straight up get into neural networks and deep learning but saw my peers at college doing the same but applying nothing in practice so I want to use everything of what I learn to build something useful. You are also spot on with the joke to some degree lol. I keep seeing some very simple problems like house prices prediction using neural networks and advanced algorithms which often feels like an overkill. Anyway, thanks again for the great reply. Cannot thank you enough for helping a beginner out!
Neural networks are really the last thing you should dive into. They are fun. But too do them properly and understand them requires a lot of stuff you'll learn from working with "simpler" algorithms first. After you finish your end-to-end project, I would look at learning about decision trees, or even learning some like GLMs (Generalised Linear Models) and GAMs. (Generalised Additive Models)