Learning Python For Data Science
For those of you who wish to begin learning Python for Data Science, here is a list of various resources that will get you up and running. Included are things like online tutorials and short interactive course, MOOCs, newsletters, books, useful tools and more.
Python Tips decided to put this together so that you can begin learning Data Science with Python right of the bat, without having to spend hours surfing the web in search of resources:
Tutorials
- Intro to Python for Data Science by DataCamp
- Python Programming by Codecademy
- A Byte of Python
- http://www.learnpython.org/
- Code Mentor Python Tutorials
- Learn Data Science with Python – Dataquest
- Intermediate Python for Data Science by DataCamp
Massive Open Online Courses (MOOCs)
- Introduction to Python for Data Science by Microsoft
- Python for Everybody by University of Michigan
- Data Analysis and Interpretation Specialization
- Data Analysis with Python and Pandas on Udemy
- Data Analysis with Python and Matplotlib on Udemy
- Intro to Data Science on Udacity
Resources and Newsletters
- Bite Python
- PyData,org
- Python.org/doc/
- http://planetpython.org/
- Python Weekly
- Pycoder’s Weekly
- DataScienceMaster.org
Books
- Intermediate Python
- Learn Python The Hard Way
- Practical Data Analysis with Python
- Python for Data Analysis
- Data Science from Scratch: First Principles with Python
Work Space
You can find the full article, here. For more articles about Python, click here.
DSC Resources
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Additional Reading
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Comment
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Comment by Krishnaswami Rajagopalan on July 17, 2017 at 5:48pm
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Ma'am thank you for taking time out and doing this blog in such detail.
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Comment by Dheeraj Tommandru on March 16, 2017 at 6:57am
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Thank you sir
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Comment by Maksymilian Piechanowski on August 4, 2016 at 11:29pm
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Thank you!
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Historically, programming languages have evolved to be further from the machine (think octal !) and closer to the way people naturally think (think Java).
In the last decade or so this progress seems to have stalled.
But here's a next step -- Executable English -- -- emerging self-service self-explaining database apps, live online at www.executable-english.com .
Here's a slide:
www.executable-english.com/internet_business_logic_in_a_nutshell.pdf
and a short paper:
www.astd.org/Publications/Magazines/The-Public-Manager/Archives/201...
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Excellent list of sources. I have recently "discovered" George Boole's PROJECT PYTHON on YOUTUBE. It may not be for everyone, but I am finding it uniquely helpful for myself. The biggest change I can point to is his narration through each line of the program, vs simply listing the steps.
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