EXCEPTIONAL TRAINING COURSES FOR IT PROFESSIONALS
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Our Python training course for Data Scientists covers the Python language both from the Object Oriented and Functional viewpoints, ultimately focusing on Big Data Analytics including how to best manipulate and visualize your data with Python's excellent library support.
The course is intensive and is intended for Data Scientists, Data Analysts, and Business Intelligence experts who want to understand how to use Python in their data-oriented environment.
Practical exercises and interactive walk-throughs are used throughout, so attendees have the opportunity to apply the proposed concepts on real Data Science applications, from exploratory data analysis to predictive analytics.
Installation packagaing and virtualization of Python such as Conda & Anaconda. Conda is a cross-platform tool for managing packages and environments. Anaconda is a free enterprise-ready Python distribution that includes 150 installed of the most popular python packages for science, math, engineering and data analysis. Anaconda comes with conda to manage libraries and environments.
Become familiar with the most common development environments for Python and discover how to develop, test and debug Python programs using modern best practice. We'll go through Jupyter, iPython, PyCharm and PyDev.
Introduction to Python's data structures, basic constructs and core components of the standard library. Overview of how Python is used in the field of Data Science, its benefits as fast prototyping tool as well as its production-ready capabilities.
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. Functional programming concepts open the way for parallelism and provide the intuitions used by many popular Big Data frameworks.
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.
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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