These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
Cheatsheets for AI, only a handful are really for AI. Lots of it is relevant for data analysis. I printed off the pandas ones myself.
I use R primarily and have been wondering how to do similar data manipulation tasks in pandas. So I printed out one of the pandas cheatsheets just for that :smile: It looks veryyy similar to the dplyr (R package used for data manipulations) cheatsheet.
@bharath93m Your question is not really clear and I have some time not using PySpark but I still decided to investigate your request as I still found it interesting.
I found this link useful. The definition of window functions is:
At its core, a window function calculates a return value for every input row of a table based on a group of rows, called the Frame. Every input row can have a unique frame associated with it.
For what I can see, and complementing the recommendation by @erictleung, you can say that window functions resemble the
apply pandas methods. So I am sure you can implement a
count. What it is not clear from your question is what you want to do with the count.
Anyway - check the link I am suggesting to see if it is helpful? There is an example in Spark SQL but everything you can do in SQL can be probably done in Py too.
evaristoc sends brownie points to @mstellaluna :sparkles: :thumbsup: :sparkles:
Sorry for this but I am DELIGHTED with the Deep Learning training by Andrew Ng... Just look at this exercise to implement regularization from scratch (!!!!):
Problem Statement: You have just been hired as an AI expert by the French Football Corporation. They would like you to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head.
And then a soccer field figure as an illustration...
I am miles from what I was doing when I started practicing DS and python so I can finish this exercises rather quicker (three weekly sections of 2 different courses in just 3-4 days is nice...).
But the training might catch you if you don't have some math. A lot of people complaining. There are also those who are complaining because there is not enough math in the course but rather because in order to understand the math of the course they need more math. :) :) :) :)
Anyway - this course already promises to be as influential as his ML course for the years to come, IMO.
@mstellaluna I don't see why not to do that. Depends... SAP clients are not as many as those of Oracle, IBM and MS - her more visible competitors. SAP does have a niche market that is very faithful. SAP market share is mostly European if I am not wrong. But it is still a very important market, with Forbes 500 clients.
So: depends on your aspirations. If you do SAP, you might be securing a place within the SAP market share for sure.
But the nice thing is learning MANY things about NN implementations barely found in other courses.
And I haven't commented the invites: Andrew Ng interviews several people in the sector that are in the front-end of NN development and the insights are excellent. That also means that you can more easily associate those names to specific advances and specialties in the field.
I am taking the Specialization - 5 courses. If you are not still very confident, take just the first two.
Anyway: you are starting, so take it slowly. I was discussing with a friend here in fCC about how long average might take someone to get an average base for Data Analytics with a ML/DMining orientation and we concluded that 2 years average. Of course depends on how talented you are and when you started to study all the basis.
bharath93m sends brownie points to @erictleung :sparkles: :thumbsup: :sparkles: