PWD: Add data science resources from @Arundel2017

modules/level_5/cm_2015_programming_with_data/README.md

@@ -1,8 +1,7 @@
 [Go back to the main page](../../../README.md)
 
-# Table of contents
+### Table of contents
 
-- [Table of contents](#table-of-contents)
 - [Programming with Data](#programming-with-data)
 - [Topics covered](#topics-covered)
 - [Assessment](#assessment)
@@ -23,7 +22,7 @@
 
 ---
 
-# Programming with Data
+## Programming with Data
 
 This module will show you how to work with data: getting data from a
 variety of sources, visualising data in compelling, informative ways,
@@ -32,7 +31,7 @@ data to test hypotheses and make parameterised predictions. The module
 will also introduce you to a new language and programming environment
 that is well-adapted to languages for these applications.
 
-# Topics covered
+## Topics covered
 
 - Setting up the programming environment
 - Control structures, functions and comprehensions
@@ -45,37 +44,43 @@ that is well-adapted to languages for these applications.
 - Bayes' theorem and naïve Bayes classification
 - Clustering
 
-# Assessment
+## Assessment
 
 One two hour unseen written examination and coursework (Type I)
 
-# Mock exam
+## Mock exam
 
 [See the `binary-assets` repository](https://github.com/world-class/binary-assets/tree/master/modules/cm2015_pwd).
 
-# Module overview
+## Module overview
 
 [See the `binary-assets` repository](https://github.com/world-class/binary-assets/tree/master/modules/cm2015_pwd).
 
-# Module specification
+## Module specification
 
 - [Module specification (September 2020)](https://github.com/world-class/binary-assets/blob/master/modules/module_specification/CM2015_PWD-Module-Spec.pdf)
 
-# Syllabus
+## Syllabus
 
 - [Syllabus PDF (September 2020)](https://github.com/world-class/binary-assets/blob/master/modules/syllabi/Syllabus_CM2015_PWD.pdf)
 
-# Resources
+## Resources
 
-## Complementary learning
+### Complementary learning
 
-### Data Science
+#### Data Science
 
-- [Foundations of Data Science: K-Means Clustering in Python](https://www.coursera.org/learn/data-science-k-means-clustering-python) - Coursera, by Dr Matthew Yee-King +3 more instructors.
+- [Applied Data Science with Python Specialization](https://www.coursera.org/specializations/data-science-python) - _"University of Michigan, Coursera."_
+- [CS 88: Computational Structures in Data Science](https://cs88-website.github.io/sp21/) - _"Spring 2021. Instructors: Gerald Friedland, Michael Ball"_
+- [Data 8: The Foundations of Data Science](http://data8.org/) - _"The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance."_
 - [Data Science playlist](https://www.youtube.com/watch?v=GjKQ6V_ViQE&list=PLFCB5Dp81iNVmuoGIqcT5oF4K-7kTI5vp) - Youtube, by Keith Galli: web scraping, numpy, pandas, plotting, NLP, sklearn.
+- [Data Science: University of Cambridge](https://www.cl.cam.ac.uk/teaching/2021/DataSci/materials.html) - _"Department of Computer Science and Technology."_
+- [Foundations of Data Science: K-Means Clustering in Python](https://www.coursera.org/learn/data-science-k-means-clustering-python) - Coursera, by Dr Matthew Yee-King +3 more instructors.
 - [Machine Learning & Data Science playlist](https://www.youtube.com/watch?v=sEte4hXEgJ8&list=PLGLfVvz_LVvQy4mkmEvtFwZGg1S38MUmn) - Youtube, by Derek Banas: probability, statistics, numpy, pandas, plotting, time series
+- [Statistics with Python Specialization](https://www.coursera.org/specializations/statistics-with-python) - _"University of Michigan, Coursera."_
+- [The Data Science Design Manual](https://data-manual.com/) - _"Steven Skiena - The Data Science Design Manual serves as an introduction to data science, focusing on the skills and principles needed to build systems for collection, analyzing, and interpreting data."_
 
-### Python
+#### Python
 
 - [Courses (free)](https://github.com/world-class/REPL/tree/master/online_courses/free#python) - REPL
 - [Learn Python, Data Viz, Pandas & More on Kaggle](https://www.kaggle.com/learn/overview) - Kaggle
@@ -84,29 +89,29 @@ One two hour unseen written examination and coursework (Type I)
 - [Videos](https://github.com/world-class/REPL/tree/master/youtube#python) - REPL/YouTube
 - Websites: [references](https://github.com/world-class/REPL/tree/master/websites#python-1) - [working with data](https://github.com/world-class/REPL/tree/master/websites#working-with-data-pandas-numpy-matplotlib-ipython-scikit-learn) - REPL
 
-#### Sentiment analysis
+##### Sentiment analysis
 
 - [Example using NLTK for preprocessing text](https://colab.research.google.com/github/gal-a/blog/blob/master/docs/notebooks/nlp/nltk_preprocess.ipynb)
 - [Sentiment Analysis with NLTK](https://www.kaggle.com/kakiac/sentiment-analysis-with-nltk)
 
-#### Libraries
+##### Libraries
 
-##### Matplotlib
+###### Matplotlib
 
 - [Matplotlib](https://matplotlib.org/) - _"Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python."_
 
-##### Numpy
+###### Numpy
 
 - [NumPy](https://numpy.org/) - _"The fundamental package for scientific computing with Python."_
 
-##### Pandas
+###### Pandas
 
 - :3rd_place_medal: [Pandas Tutorial Playlist - Corey Schafer](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS) - YouTube
 - [10 minutes to pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html) - pydata.org
 - [Brandon Rhodes - Pandas From The Ground Up - PyCon 2015](https://www.youtube.com/watch?v=5JnMutdy6Fw)
 - [Learn Pandas](https://www.kaggle.com/learn/pandas) - Kaggle
 - [Vincent D. Warmerdam - PyData Eindhoven 2019](https://www.youtube.com/watch?v=yXGCKqo5cEY) - YouTube
 
-## :heart: Notes
+### :heart: Notes
 
 - Visit [this page of world-class/notes](https://github.com/world-class/notes/tree/master/level_5/programming-with-data/).