Whenever l have tried to solve a project l find it difficult, what should l do?
It’s good to kind of try to analyze what is it specifically that’s difficult. As you gain more experience, one big thing you will gain is a better intuition for how to gauge difficulty accurately.
Here are some sort of difficulties:
missing small reference information (could be a particular function/method, syntax, etc.). Sources to fix: documentation, forums, reference books.
missing small conceptual conceptual information: these are things like intuition of how the ideas you already know might fit together. Sources to fix: practice with your fundamental ideas a little more, try to plan it out on paper (pseudo-code or diagrams), youtube sometimes has some video overviews that are helpful.
missing important reference information: this could be like not knowing an entire algorithm or library that could solve your problem. Conceptually they might be understandable to you but you need to take a step back and learn them before really “conquering” the problem at hand. Sources to fix: documentation and reference material for those specific libraries/algos/data structures.
missing important conceptual information: this one is usually at least a few levels from where one might be. Sometimes requiring a mix of abilities (e.g.: some mathematical concepts and their implementation in code). Usually the best thing for this is to ask around and figure out what the core fundamentals are of this concept and strengthen those first.
Anything like the above 2 steps but add multiple levels of complexity: some problems take years of preparatory foundations to be able to tackle efficiently in a short time frame. But this shouldn’t dissuade you! Some of the cooler things out there take a lot of work to get to but the reward is not only the skill but the journey in building that skill. Sources to fix: specialized courses in the topic (sometimes you can find these for free and high-quality), and also it’s usually best to seek out advice from people who have covered that specific path successfully.
I hope this gives some idea! Feel free to ask more questions.
It’s also important to believe in yourself and remember that project completion doesn’t mean anything in the short term. The amount of projects I have left unfinished vastly outnumber the few projects I have completed in my life (I wouldn’t be surprised if it’s 90% unfinished projects). But the silver lining is that the collective experience of the unfinished projects helped the projects I did complete shine that much more.
I have another question, to be good at data analysis, do l have to try to solve a lot of projects and practice etc, cause that is what l actually want ultimately to gain
I recommend reinforcing probability and statistics and all the math concepts that support it. Particularly I recommend looking into linear algebra and the counting parts of discrete math as they help lay a lot of groundwork.
Here is a good discussion to bookmark:
I have another question, what kind of consistency do you need to be good in python, cause when l start, after some time other commitments come into my way
Good in python has many definitions… which depend on what type of skill-set you look to have. I think a good baseline though is to know the basics (strings manipulation, lists and dictionaries, looping, some basic file-management, maybe a few object-oriented principles), some light problem solving, and very importantly: improving debugging skills.
From the baseline you can then branch off into whatever else you might be interested in if you want to keep investing into the python skill tree (data analysis,data structures and algorithms/web-development/script writing for utility purposes/game development [more prototypes than finished products] etc).
For consistency my concept of it is: if you get a job/project, clearing out a week or so to review the material you learned before should be enough to bring you back up to speed. Anything less I would consider that it’s maybe a little rusty and needs practice.
Consistency l meant how many days of continuous learning is needed to be good at it
And how will l know if am ready to move on to data analysis
You need as many days as it takes, that’s going to be a very different number for everyone depending on your background and learning style.
For data analysis you could possibly start looking into intro material to get a flavor but you’ll want to look for which supplemental skills you need for that (like some study of stats, probability, maths).