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Introduction to Python's data structures and control flow. Overview of how Python is used in the field of Data Science.
iPython, PyCharm and PyDev
Become familiar with the most common development environments for Python and discover how to develop, test and debug Python programs using modern best practice.
Understanding of Object Orientation is key to using many of Python's excellent Data Analytics libraries and is the most popular programming paradigm in use today. Object Orientation introduces concepts such as objects, classes, inheritance and polymorphism and focuses on the manipulation of mutable data.
In addition to Python's object oriented features, Python also fully supports the Function Programming paradigm. Functional programming concentrates on transformation of immutable data using functions. Topics include comprehensions, iterators, generators, lambdas and decorators.
Numpy, SciPy and SymPy
These libraries are the building blocks for the more advanced Data Analytical libraries. Numpy provides high speed manipulation of multi-dimensional data. This core Python library is written in C and hence is extremely fast and can work with very large data sets.
MatplotLib is an outstanding plotting library, essential for visualizing 'Big Data'. It is very mature and can perform almost any plotting you could possibly need including interactive plots and simulations.
Pandas and Spreadsheet software
Most real data these days is provided in the form of spreadsheets and Pandas and OpenPyXl are two libraries that make analysis of such data as simple as possible. Pandas is particularly good at handling large data sets with missing or faulty rows and columns.
SciKitLearn provides simple and efficient tools for data classification, mining and analysis. SciKitLearn is a sophisticated data modelling package.
Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.
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