This training course provides an introduction to the core concepts of the Python language, ultimately focusing on Big Data Analytics and Machine Learning applications.
The first three days of the course introduce the delegates to Python tools for Data Science, including topics like how to best manipulate and visualise your data with Python's excellent library support.
The last two days of the course move one step forward, providing an overview to Artificial Intelligence and Machine Learning with the purpose of implementing predictive analytics applications.
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.
Software developers and software engineers with a basic knowledge of Python. Data Scientists, Data analysts and Business Intelligence professionals who are new to Python.
Python Core Concepts and Best Practices
Introduction to Python basic concepts, data structures and control flow structures. Overview of how Python is used for Data Science and Data Analytics projects.
Environment set-up using Anaconda, a free and enterprise-ready distribution of Python. We'll discuss how to set up virtual environments and install Python packages. We'll also set up Jupyter, a web-based interactive environment where users can organise, write and run their Python code in notebooks.
Notions of Object-Oriented Programming and Functional Programming, applied to the design of Python applications and analysis pipelines using best practices.
Python Data Science Tools
We'll explore the most important Python tools for Data Science.
NumPy, short for Numerical Python, is one of the main building blocks for scientific computing in Python. It provides high speed manipulation of multi-dimensional arrays and it's used by higher level libraries (like pandas) to support sophisticated analytics with high speed computation.
Pandas is a highly performant library for data manipulation and data analysis in Python. It's built on top of NumPy and optimised for performance, while offering a high-level interface.
We'll discuss how to create and manipulate Series and DataFrame objects in pandas, accessing data from multiple sources, cleaning and transforming data sets to get them in the right shape for advanced analysis.
Accessing & Preparing Data
Data can come in multiple formats and from multiple sources. We'll examine how to read and write data from local files in different formats, and how to access data from remote source.
Data cleaning and data preparation are the first steps in a data analysis project, so we'll discuss how to perform data transformation to get ready for further analysis.
With our data in the right shape, we're ready to analyse them in order to extract useful insights.
We'll perform the computation of summary information and basic statistics from data sets. We'll approach split-apply-combine operations with Data Frames, in order to perform advanced transformations and reshaping our data with pandas.
We'll query our Data Frames using the powerful group-by method.
Data analysis benefits from the visualisation of data. If a picture if worth a thousand words, complex data structures can be easier to understand and analyse using effective visualisation techniques.
Communicating the results with non-technical users is also a challenge that visualisation techniques help to overcome. We'll showcase how to easily produce beautiful visualisations with matplotlib.
Artificial Intelligence, Machine Learning and Data Science
What is AI? What is ML? What's up with the hype? We'll discuss Machine Learning problems and applications. How to translate a business problem into a Machine Learning task? We'll discuss the overall process to solve these tasks and then introduce specific algorithms implemented using scikit-learn, the core Machine Learning library for Python.
ML and AI Overview
We'll discuss the overall process of learning from data in order to make predictions, introducing core notions of Machine Learning like feature engineering, training data and test data, evaluation and cross-validation.
Supervised Learning Problems
Predicting a quantity: modelling the numeric relationship between variables using regression techniques. Predicting a category: assigning pre-defined labels to new items using classification techniques. Examples of applications include house price prediction (regression) and spam detection (classification).
Unsupervised Learning Problems
Clustering: the task of grouping similar items together. Without labelled data, learning algorithms can be used to detect patterns and similarity in complex data sets.
Dimensionality Reduction: the task of reducing the number of variables serves a double purpose: visualising complex data sets and performing downstream tasks more efficiently with smaller inputs.
Neural Networks and Deep Learning
Introduction to Artificial Neural Networks, a family of algorithms applicable to many Machine Learning problems, and relevant mathematical concepts. Discussion on Neural Network concepts like gradient descend, activation functions, loss functions and hyper-parameters.
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