initial class resources

Author: ndanielsen

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+*~

README.md

@@ -1 +1,32 @@
-# socal-code-camp-python
+# Introduction to Python for Fun and Profit
+
+Socal Code Camp
+
+11:15 AM - Saturday, November 10, 2018
+
+## Set Up
+
+Please down this simple Python editor for beginner programmers.
+
+[https://codewith.mu/](https://codewith.mu/)
+
+[Recommended: Getting Starting with Mu] (https://codewith.mu/en/tutorials/1.0/start)
+
+## Why It's Great to Learn Python
+
+:snake: It's free. You can download it for free, there are ways to learn it for free, and there's a whole range of packages available for free.
+
+:snake: It's easy. Other languages make you remember strange commands or keep track of curly braces everywhere. Not so with Python. That makes it a great beginner programing language.
+
+:snake: It's everywhere. It's a powerful language that is used by a wide variety of companies and organizations to solve all sorts of problems.
+
+:snake: It's community-focused. Python exists solely because of volunteers and has a great community both online and off that connects people.
+
+
+## Recommended Resources for Your Python Journey After Mu
+
+[Automate the Boring Stuff](https://automatetheboringstuff.com/)
+Learn python through small practical projects that might reduce the tedium in your job! (Highly recommended)
+
+[How to Think Like a Computer Scientist](http://interactivepython.org/runestone/static/thinkcspy/index.html)
+An equally good book and allows you to use python right in your browser.

learning_resources.md

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+# Resources for Continued Learning
+
+
+## Blogs
+
+* [Simply Statistics](http://simplystatistics.org/): Written by the Biostatistics professors at Johns Hopkins University who also run Coursera's [Data Science Specialization](https://www.coursera.org/specialization/jhudatascience/1)
+* [yhat's blog](http://blog.yhathq.com/): Beginner-friendly content, usually in Python
+* [No Free Hunch](http://blog.kaggle.com/) (Kaggle's blog): Mostly interviews with competition winners, or updates on their competitions
+* [FastML](http://fastml.com/): Various machine learning content, often with code
+* [Edwin Chen](http://blog.echen.me/): Infrequently updated, but long and thoughtful pieces
+* [FiveThirtyEight](http://fivethirtyeight.com/): Tons of timely data-related content
+* [Machine Learning Mastery](http://machinelearningmastery.com/blog/): Frequent posts on machine learning, very accessible
+* [Data School](http://www.dataschool.io/): Kevin Markham's blog! Beginner-focused, with reference guides and videos
+* [MLWave](http://mlwave.com/): Detailed posts on Kaggle competitions, by a Kaggle Master
+* [Data Science 101](http://101.datascience.community/): Short, frequent content about all aspects of data science
+* [ML in the Valley](http://ml.posthaven.com/): Thoughtful pieces by the Director of Analytics at Codecademy
+
+
+## Aggregators
+
+* [DataTau](http://www.datatau.com/): Like [Hacker News](https://news.ycombinator.com/), but for data
+* [MachineLearning on reddit](http://www.reddit.com/r/MachineLearning/): Very active subreddit
+* [Quora's Machine Learning section](http://www.quora.com/Machine-Learning): Lots of interesting Q&A
+* [Quora's Data Science topic FAQ](https://www.quora.com/What-is-the-Data-Science-topic-FAQ)
+* [KDnuggets](http://www.kdnuggets.com/): Data mining news, jobs, classes and more
+
+## Online Classes
+
+* [Coursera's Data Science Specialization](https://www.coursera.org/specialization/jhudatascience/1): Nine courses (running every month) and a Capstone project, taught in R
+* [Stanford's Statistical Learning](http://online.stanford.edu/course/statistical-learning): By the authors of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) and [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/), taught in R, highly recommended (preview the [lecture videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/))
+* [Coursera's Machine Learning](https://www.coursera.org/learn/machine-learning/): Andrew Ng's acclaimed course, taught in MATLAB/Octave
+* [Caltech's Learning from Data](http://work.caltech.edu/telecourse.html): Widely praised, not language-specific
+* [Udacity's Data Analyst Nanodegree](https://www.udacity.com/course/nd002): Project-based curriculum using Python, R, MapReduce, MongoDB
+* [Coursera's Data Mining Specialization](https://www.coursera.org/specialization/datamining/20): New specialization that began February 2015
+* [Coursera's Natural Language Processing](https://www.coursera.org/course/nlp): No upcoming sessions, but [lectures](https://class.coursera.org/nlp/lecture) and [slides](http://web.stanford.edu/~jurafsky/NLPCourseraSlides.html) are available
+* [SlideRule's Data Analysis Learning Path](https://www.mysliderule.com/learning-paths/data-analysis): Curated content from various online classes
+* [Udacity's Intro to Artificial Intelligence](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271): Taught by Peter Norvig and Sebastian Thrun
+* [Coursera's Neural Networks for Machine Learning](https://www.coursera.org/course/neuralnets): Taught by Geoffrey Hinton, no upcoming sessions
+* [statistics.com](http://www.statistics.com/data-science/): Many online courses in data science
+* [CourseTalk](http://www.coursetalk.com/): Read reviews of online courses
+
+## Online Content from Offline Classes
+
+* [Harvard's CS109 Data Science](http://cs109.github.io/2014/): Similar topics as General Assembly's course
+* [Columbia's Data Mining Class](http://www2.research.att.com/~volinsky/DataMining/Columbia2011/Columbia2011.html): Excellent slides
+* [Harvard's CS171 Visualization](http://www.cs171.org/2015/index.html): Includes programming in D3
+
+
+## Face-to-Face Educational Programs
+
+* [Comparison of data science bootcamps](http://yet-another-data-blog.blogspot.com/2014/04/data-science-bootcamp-landscape-full.html): Up-to-date list maintained by a Zipfian Academy graduate
+* [The Complete List of Data Science Bootcamps & Fellowships](http://www.skilledup.com/articles/list-data-science-bootcamps/)
+* [Galvanize](http://www.galvanize.com/) (acquired [Zipfian Academy](http://www.zipfianacademy.com/)): Offers Data Science Immersive (Denver, Seattle, San Francisco)
+* [GalvanizeU](http://www.galvanizeu.com/): Offers Master of Engineering in Big Data (San Francisco)
+* [Data Science Retreat](http://datascienceretreat.com/): Primarily uses R (Berlin)
+* [Metis Data Science Bootcamp](http://www.thisismetis.com/data-science): Newer bootcamp (New York)
+* [Persontyle](http://www.persontyle.com/): Various course offerings (based in London)
+* [Software Carpentry](http://software-carpentry.org/): Two-day workshops, primarily for researchers and hosted by universities (worldwide)
+* [Colleges and Universities with Data Science Degrees](http://datascience.community/colleges)
+
+
+## Conferences
+
+* [Knowledge Discovery and Data Mining (KDD)](http://www.kdd.org/): Hosted by ACM
+* [O'Reilly Strata + Hadoop World](http://strataconf.com/): Big focus on "big data" (San Jose, London, New York)
+* [PyData](http://pydata.org/): For developers and users of Python data tools (worldwide)
+* [PyCon](https://us.pycon.org/): For developers and users of Python (Portland in 2016)
+
+
+## Books
+
+* [An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) (free PDF)
+* [Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) (free PDF)
+* [Think Stats](http://www.greenteapress.com/thinkstats/) (free PDF or HTML)
+* [Mining of Massive Datasets](http://www.mmds.org/) (free PDF)
+* [Python for Informatics](http://www.pythonlearn.com/book.php) (free PDF or HTML)
+* [Statistics: Methods and Applications](http://www.statsoft.com/Textbook) (free HTML)
+* [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)
+* [Data Smart: Using Data Science to Transform Information into Insight](http://www.amazon.com/gp/product/111866146X/)
+* [Sam's Teach Yourself SQL in 10 Minutes](http://www.amazon.com/Sams-Teach-Yourself-Minutes-Edition/dp/0672336073)
+
+
+## Other Resources
+
+* [Open Source Data Science Masters](https://github.com/datasciencemasters/go): Huge list of resources
+* [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science): Another list of resources
+* [The Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/): Online guide to understanding Python and getting good at it
+* [Python Reference](https://github.com/rasbt/python_reference): Python tips, tutorials, and more
+* [videolectures.net](http://videolectures.net/Top/Computer_Science/): Tons of academic videos
+* [Metacademy](http://www.metacademy.org/list): Quick summary of many machine learning terms, with links to resources for learning more
+* [Terms in data science defined in one paragraph](https://github.com/rasbt/pattern_classification/blob/master/resources/data_glossary.md)

part_1.py

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+## Learn More Python with Automate the Boring Stuff:
+## https://automatetheboringstuff.com/chapter1/
+
+# Your First Program
+#
+# print('hello world')
+#
+# # Comments in python use a '#'
+
+## WARM UP QUIZ
+
+# PART I
+a = 5
+b = 5.0
+c = a / 2
+d = b / 2
+
+# What is type(a)?
+
+
+# What is type(b)?
+
+
+# What is c?
+
+
+# What is d?
+
+
+
+# EXERCISES
+
+e = [a, b]
+f = range(0, 10)
+
+# What is type(e)?
+
+
+# What is len(e)?
+
+
+# What is type(f)?
+
+
+# What are the contents of f?
+
+
+# What is 'range' called?
+
+
+# How do I get help on 'range'?

part_2.py

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+# STRINGS
+# https://docs.python.org/3/tutorial/introduction.html#strings
+
+s = str(42)
+
+s  # convert another data type into a string (casting)
+
+s = 'I like you'
+
+# examine a string
+s[0]  # returns 'I'
+
+len(s)  # returns 10
+
+# string slicing like lists
+s[0:7]  # returns 'I like '
+
+s[6:]  # returns 'you'
+
+s[-1]  # returns 'u'
+
+
+# EXERCISE: Book Titles (Part 1)
+# 1) Extract the book title from the string
+# 2) Save each book title to a variable (ie book1_title)
+# 3) How many characters/elements are in each title?
+# Hint: {bookTitle} by {author}, {years}
+
+
+book1 = "Beyond the Door by Dick, Philip K., 1928-1982"
+book2 = "The Variable Man by Dick, Philip K., 1928-1982"
+book3 = "The Skull by Dick, Philip K., 1928-1982"
+
+
+# STRINGS - Part II
+
+# concatenate strings
+s3 = 'The meaning of life is'
+s4 = '42'
+s3 + ' ' + s4       # returns 'The meaning of life is 42'
+s3 + ' ' + str(42)  # same thing
+
+# split a string into a list of substrings separated by a delimiter
+
+s = 'I like you'
+s.split(' ')        # returns ['I','like','you']
+s.split()           # same thing
+
+
+## Learn more with Automate the Boring Stuff:
+## https://automatetheboringstuff.com/chapter1/

part_3.py

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+# LISTS
+# https://docs.python.org/3/tutorial/introduction.html#lists
+
+# list slicing [start:end:stride]
+
+weekdays = ['mon','tues','wed','thurs','fri']
+weekdays[0]         # element 0
+weekdays[0:3]       # elements 0, 1, 2
+weekdays[:3]        # elements 0, 1, 2
+weekdays[-1]        # last element
+
+test = weekdays[3:]        # elements 3, 4
+
+weekdays
+
+weekdays[-2]        # last element (element 4)
+weekdays[::2]       # every 2nd element (0, 2, 4)
+weekdays[::-1]      # backwards (4, 3, 2, 1, 0)
+
+days = weekdays + ['sat','sun']     # concatenate lists
+
+# Let's look at it another way
+days_list = ['mon','tues','wed','thurs','fri']
+days_list.append('sat') # append individual elements to list
+days_list.append('sun')
+
+#########
+# Exercise - Lists
+#########
+
+# How do I slice out 'wed'?
+# How do I check the type of 'mon'?
+# How do I slice out 'wed' through 'friday'?
+# What are two ways to slice out 'fri'?
+# What is the length of days and days_list?
+# How do I reverse the order of days? (hint: google it)

part_4.py

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+## FOR LOOPS
+
+# print 0 through 5 on a separate line
+nums = [0, 1, 3, 4, 5]
+
+for num in nums:
+    print(num)
+
+# for loop to create a list of cubes
+cubes = []
+for num in nums:
+    cubes.append(num**3)
+
+
+##### CONDITIONALS ######
+
+# We will use an if else to implement logic
+x = 15
+if x  > 10 :
+    print("x is more than 10!")
+    print("hooray")
+elif x > 100:
+    print("x is HUGE")
+
+# As soon as one conditional is satisfied, it stops checking!
+
+temperature = 20
+if temperature <= 32:
+    print("water is ice")
+elif temperature > 32 and temperature < 212:
+    print("water is liquid")
+else:   #implicity means else if temperature >=212
+    print("water will boil")
+
+
+#########
+# EXERCISE For Loops
+#########
+# Given that: letters = ['a','b','c']
+# Write a for-loop that returns: ['A','B','C']
+
+
+# Given that: nums = [0, 1, 3, 4, 5]
+# Write a for-loop that sums the total of all the numbers in the list
+
+
+# Given that: nums = [0, 1, 3, 4, 5]
+# Write a for-loop that prints out even numbers