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Old 02-02-18, 08:27   #41
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Duncan F*****g Stewart
 
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Originally Posted by Elshambles View Post
Thinking of making python part of my time doing courses

What would you advise to someone coming at it from a fado fado coding viewpoint not a financial one?

Ta
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Step 1.1: Have finished this coursera course: Learn to Program: The Fundamentals.

This was a brill course. Really well delivered. Went for the certified version (46) as have seen on CVs a lot of people now including Coursera certified courses and those courses being accepted.

You start the course with nothing and end with great basic knowledge of how python works.
Definitely the above one, its an excellent introductory course thats taught very well.
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Old 02-02-18, 11:14   #42
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Old 26-02-18, 22:18   #43
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Duncan F*****g Stewart
 
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Doing a course today on deep learning (where you create hidden layers after your main data to account for unknown interactions between variables). Have covered all the basics of deep learning before but it's genuinely mind-blowing the scope of the expansions in this area. It's almost like being given magic keys to understand things that no-one else knows.

Big plan for next month is to develop a Eurovision gambling model using a variety of machine learning techniques. Have wife involved too as she has tonnes of Python skills from her job and was surprisingly of the 'lets gamble everything' view. Will post all the key betting takeaways here and we'll get rich together.

Just about finished the 20-course Python specialism on DataCamp so a formal progress update to follow in a day or so.
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Old 27-02-18, 21:46   #44
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So I finished the 20-course monster on data science on DataCamp today. Data Scientist with Python. It was excellent really, although I think some of those coursera academic courses are a good idea to do beforehand as most of it is just explaining how to do things, rather than the deeper meaning behind why you do things. (the latter being a natural focus of academics vs practitioners, and I think a better way of engraining real learning).

At this point its hard to see much more in core non-machine-learning data science that needs to be learned, and thats a big thing. There's a lot more in actual machine learning as thats an incredibly complex area.

The plan therefore:

March: spend 80 hours working on three projects to build good practical knowledge:

1. Have a project ongoing involving a survey that needs all UK professor email addresses. So am going to do something simple enough to automate the collection of these through a web scraping tool in Python. I'd imagine this will work out as quite a painful data gathering task, but good learning for future data gathering.

2. Have a work project involving standard finance data that I'd normally test in Stata (the go-to academic statistical testing software), but because of a few data manipulation difficulties am going to run everything through Python and build up a bit of parallel knowledge about how to do what I normally do in Stata in Python, plus benefit from actually being able to put the data in a more usable way.

3. The third project was mentioned yesterday and is a bit more fun. Going to use a range of machine learning techniques to develop a betting model for Eurovision. The initial plan is to wager around 2k on whatever is developed as long as the model looks reasonably competent. This is going to be a multi-stage ML model - voting hierarchical clustering to model voting tendancies, probably something like lasso regressions or deep learning to model songs, and maybe a bit of natural language processing of news coverage depending on availability. Time to get a bit of skin in the game!


April will then return to learning. First up is a few more coursera courses on the breadth of machine learning, going quite in-depth, then May will see a descent into the maths behind the modelling. This could easily take June too (and is probably an endless task). The maths seems vital as the actual testing of these models is becoming easier and easier, but very few of the newer people will have the slightest clue what the models they are applying actually mean. Will finish focused learning in July/August with some advanced machine learning courses. Any courses after that will be shortish courses to catch up on smaller additional learning needs.

That's 28 courses done so far, of varying sizes. Maybe another up-to-12 to round out the learning and its then time to consider things done really.
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Old 08-03-18, 06:13   #45
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Google have made their machine learning course that most starting Googlers have to take available for free. I'd imagine it's quite good.
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Old 08-03-18, 07:52   #46
Denny Crane
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What path would you recommend?

Fundamentals, crafting quality code, datacamp, and then Michigan?
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Old 08-03-18, 08:17   #47
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You might find the fundamentals course a bit too basic, but I thought it set up the whole learning very nice. Crafting quality code isn't a great course, but it goes together with the first course.

Yep, DataCamp would be a decent next step then, as its short sharp courses that are very practical.

Not sure about the third step. Maybe Michigan, which are very good applied courses, but I'd say there might be a better third step that just am not aware of yet!
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Old 29-08-18, 07:40   #48
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Went to an into lecture for a data analytics course

*Tools will be pen paper calculator, Excel, SPSS and R(main lecturer doesn't know much Python)
*Highest marked project last year was a dashboard built in Excel
*There were a bunch of annoying older students asking a barrage of pointless questions


Strongly reconsidering my plan to give up several evenings a week to do it
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Old 29-08-18, 08:27   #49
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Google have made their machine learning course that most starting Googlers have to take available for free. I'd imagine it's quite good.
ugh...I was busy today as well...
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