Reddit reviews Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

If you want to a job upon graduation, you need the following items:

• Work experience. No work experience, no job upon graduation. Sucks, right? But that's a fact. Try to get as many internships as possible.
  
• Languages: Python (fucking hot right now; NumPy, Pandas, TensorFlow), SQL (you need to know this as the back of your hand), R, and SAS (maybe, depends from the employer; from what I have heard, SAS is dying out).
  
• Now, let's talk about cherry on top. Few things that may really spice up your resume are TA and research opportunities. Additionally, it would be nice to have some independent projects, e.g. Time Series analysis of the Toronto housing market.
  
  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:
  
  
• Hands-On Machine Learning with ScikitLearn and TensorFlow
  
  
• Python for Data Analysis
  
  
• Python Data Science Handbook.
  
  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.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Honestly, if you have a lot of experience with R -- you might be better off working through these two books:

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/

https://www.amazon.com/Automate-Boring-Stuff-Python-Programming/

They should get you acquainted with data structures and the python language pretty easily.

I started by working through Python for Data Analysis.

There are also some free things online, including (but not limited to):

Think Python

Python Data Science Handbook

Computational and Inferential Thinking

How to Learn Pandas

Advanced NumPy Techniques

Jose Portilla on Udemy has some good python based courses (and also frequents this subreddit). There's regularly sales or some sort of coupon code available to get any of the courses for $10-$15, so it's very reasonable.

For books:

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=asap_bc?ie=UTF8 ... it's not out yet, but due any day. You can also get preview access on sites like Safari Online (which would also have all the books below).

https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X/ref=sr_1_1

For general python:

https://www.amazon.com/Python-Crash-Course-Hands-Project-Based/dp/1593276036/ref=sr_1_1

https://www.amazon.com/Automate-Boring-Stuff-Python-Programming/dp/1593275994/ref=sr_1_1

No Starch Press, OReilly, APress and Manning generally have pretty good quality publications. I'd usually skip anything from Packt, unless it's specifically received good reviews.

I'm in about the same position as you and a lot of this post resonated with me. I have the PDF of this book (Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython https://www.amazon.com/dp/1491957662/ref=cm_sw_r_cp_api_6wx.BbRBY7X94) and I really like it a lot.

Just a heads up, Python for Data Analysis now has a second edition :)

Oh god, yes. Use Python. Use Python. USE PYTHON. Seriously - someone on Stack Overflow once wanted help on a project like this because he wanted to use Python and the client insisted on VBA. One of the CREATORS of VBA replied, saying he'd prefer to do it in Python. Someone else said to print that out and show it to the client and that would be all he needed. :-)

https://www.amazon.com/Data-Wrangling-Python-Tools-Easier/dp/1491948817

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=pd_cp_14_1/144-1708349-4929050?_encoding=UTF8&pd_rd_i=1491957662&pd_rd_r=3012d8cc-053a-4575-abf2-94039719a3d4&pd_rd_w=ODfdR&pd_rd_wg=CpS9q&pf_rd_p=0e5324e1-c848-4872-bbd5-5be6baedf80e&pf_rd_r=2DG2M7K5VYQ25NSN8N3P&psc=1&refRID=2DG2M7K5VYQ25NSN8N3P

What resources would you recommend for newbies? I'm currently reading Data Science from Scratch(Grus) and Python for Data Analysis(McKinney). Anything else I should check out?

Love the blog!

I may need to buy an actual book.

• Clean code (mentioned elsewhere)
  
• The Mythical Man Month
  
• Code: The Hidden Language of Computer Hardware and Software
  
  Docker or Kubernetes:
  
  
• Docker Deep Dive
  
• The Kubernetes Book
  
  ​
  
  Data Science, Machine Learning:
  
  Hands-On Machine Learning with Scikit-Learn and TensorFlow
  
  
• Now that you understand Python and the applicable libraries, you can actually use it for ML
  
  Python for Data Analysis
  
  
• Fantastic for learning Python and growing familiar with the libraries you’ll use in data analysis. It is from the creator of the Pandas framework.
  
  Python Data Science Handbook
  
  
• Great overviews of Juypter notebooks, NumPy, Pandas, Matplotlib, Scikit-learn
  
• Free lite version & code from the author here.
  
  Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
  
  
• So helpful for explaining the business use case for ML and data science to non-technical individuals
  
• Also helps boil down problems into data science terms, and realize if it really is an ML problem
  
  I put together a list of resources on my blog.

READ THE "PYTHON FOR DATA ANALYSIS" BOOK.

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=asc_df_1491957662/?tag=hyprod-20&linkCode=df0&hvadid=312140868236&hvpos=1o1&hvnetw=g&hvrand=3538389747429099525&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9011445&hvtargid=pla-396828636441&psc=1

Yes, I shouted. It's that important.