Reddit reviews Python Data Science Handbook: Essential Tools for Working with Data
We found 11 Reddit comments about Python Data Science Handbook: Essential Tools for Working with Data. Here are the top ones, ranked by their Reddit score.
O\'Reilly Media
La scelta del linguaggio di programmazione dipende molto dal contesto e dalla applicazione specifica. R é ottimo per l'analisi statistica, ma appunto si adatta solo a quello.
Per iniziare, mantenendo una forte connessione con quello che desideri studiare, ti suggerisco python.
Leggi "Python Data Science Handbook: Essential Tools for Working with Data" e "Learning Python"
Impara a manipolare e filtrare dati con Pandas, Numpy, Scipy,
Impara a mostrare dati preacquisiti con matplotlib, seaborn, bokeh,
o dataset che possono evolvere rapidamente con pyqtgraph
Mi stavo quasi dimenticando, per analisi statistiche uso Statsmodels
Quanto tempo serve? Non lo so. Io imparo linguaggi e librerie nuove ogni giorno perchè ogni mese disegno qualcosa di nuovo, diverso, migliorato.
Edit: aggiungo un commento finale. Arrivati ad un certo livello di affinità con la programmazione, il linguaggio di programmazione scelto non importa quanto la disponibilità di librerie affidabili, ben documentate, e ben mantenute. Dal mio punto di vista avere buone librerie è essenziale perché creare ex novo una libreria ha un costo {temporale, di skills, di focus, etc..} che spesso preferisco allocare diversamente.
You can buy direct from the publisher: http://shop.oreilly.com/product/0636920034919.do
But it's a bit cheaper on Amazon
If you want to a job upon graduation, you need the following items:
The problem with the Stats degree is that it is heavily theoretical. So, in order to balance it out, you need to get experience. Overall, I liked my experience with Stats, although I wish I spend more time on internships.
To summarize: work experience, programming, research.
Also, Machine Learning is hot right now. Pick up some books such as:
Lastly, you gotta network like your life depends on it. Meetup.com and eventbrite.come have some pretty good Data Science/ML/Programming networking events where you can make connections and learn about the industry demands. Additionally, leverage the power of LinkedIn; create your profile and start asking people out for coffee in order to learn what they do, how they do it, what tools they use and for you to gain insight into the market demands and what you can expect upon graduation.
May Central Limit Theorem work with all your distributions.
Also, another thing that seems to be hot in financial markets is Risk Management. I would suggest you speaking with the Stats profs or Risk Management profs from Rotman in order to understand how you can leverage your Stats degree in Risk Management. Fantastic, here is one of the first things you can do for networking. Fuck, I wish I was back in uni.
Sorry, just remembered. Hadoop is also pretty important as is Tableau (for data visualization).
Ah, yes, experience. I don't know whether you spent the last part of 2017 and early part of 2018 on searching for internships. If not, keep searching you still have a slight chance to find some for this summer. Indeed and LinkedIn are pretty good sources. Lastly, try reaching out to recruiters from various organizations in order to learn if they have anything available. Now, if you don't find anything at all, like AT ALL, I would suggest either you take summer school and start looking for internships during either the Fall or Summer semesters OR contact the temp agencies to see what opportunities they have. Some opportunities may not be related to what you studied, but at least they will give you some work experience and your resume will not look as empty as it does now. Also, if I am correct, then U of T should have an alumni database. Try going through that database, find the alumni of interest, reach out to them, and ask them out for coffee to learn more about what they do and if they have anything available. Tick tock, tick tock.
After some googling, indeed
How am I doing? I am depressed man, I am fucking depressed. But, TensorFlow is keeping me awake.
I doubt any courses you take would spend more than a day on the basics of a language. That's something you need to learn on your own. What's your background like? It sounds like you don't have much programming experience, so perhaps start with this. Then maybe this for learning numpy, pandas, and matplotlib.
EDIT: Didn't realize you were still in high school. I don't believe there's a specific data science undergrad program anywhere, but any STEM undergrad program will probably include an introductory programming course.
Python Data Science Handbook is awesome. Doesn't cover Scikit-Lean, but it covers Pandas (which inherently means Numpy), and some visualization stuff too.
After you finish that and are comfortable in python check out Python Data Science Handbook. I am not a data analyst, I am a PhD student doing research in fields that generate/require a lot of data.
The handbook goes over pythons numpy package and then gets into pandas. Pandas should be the tool you want to learn. Under the hood it uses numpy a lot so don’t skip the first half. Numpy implements a lot of matrix operations in FORTRAN/C if you use it properly (avoid loops when possible) it is incredibly efficient on large datasets.
While you are learning python I highly reccomend using jupyter lab.
Good luck!
I don't love the Dummies books for technical subjects; O'Reilly books are far superior. Their Python Data Science Handbook by Jake VanderPlas is worth its weight in gold, IMO.
Although I am not a statistician myself and given your background, some of my recommendations would be:
This should probably be enough for now but if you need more recommendations just say so :)
Regarding the time series question, it's not my area of expertise but since time series analysis ends up employing many statistical methods, I think it can be considered an area of statistics (Statisticians around here correct me if I am wrong :P)
As far as Python books, you should get these 2:
Python Data Science Handbook and Python Machine Learning.
There is That book coming out in 10 days by Jake VanderPlas. I haven't read it yet (obviously) but his youtube lectures are great.
Docker or Kubernetes:
​
Data Science, Machine Learning:
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Python for Data Analysis
Python Data Science Handbook
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
I put together a list of resources on my blog.