Python programming fundamental techniques such as lambdas, reading and manipulating csv files, and the numpy library.
Data manipulation and cleaning techniques using the popular python pandas data science library
How to use functions such as groupby, merge, and pivot tables effectively
Information visualization basics, with a focus on reporting and charting using the matplotlib library
How to realize design decisions in the framework
Variety of basic statistical charts
Structuring and visualizing data
How machine learning is different than descriptive statistics
learn toolkit through a tutorial
learn predictive modelling methods
Advanced techniques, such as building ensembles, and practical limitations of predictive models
Text mining and text manipulation basics
How text is handled by python, the structure of text both to the machine and to humans
Overview of the nltk framework for manipulating text
Regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processesnetwork analysis through tutorials using the NetworkX library
Basic natural language processing methods to text
Network analysis through tutorials using the NetworkX library
Concept of connectivity and network robustness
Importance of a node in a network
Evolution of networks over time and cover models of network generation and the link prediction problem.
