Introduction to Data Analytics and Machine Learning with Python

• Jupyter notebook: a quick tour of the data engineers' IDE of choice.
• Introduction to numpy: N-dimensional arrays, broadcasting functions, linear algebra abstractions and random number generators.
• Exploratory data analysis with pandas: manipulating data: loading, storing, cleaning, transforming, merging, reshaping.
• Visualising and plotting with matplotlib: generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots. Visualize and understand different types of data.
• Introduction to scipy with statistics, is mainly focused at providing a quick introduction to the scipy.stats package. We will be looking at distributions, fitting distributions and random numbers.
• Introduction to machine learning concepts with scikit-learn, training and evaluating learning algorithms. We will be looking at: decision trees, perceptrons, support vector machines, and neural networks.
• Scikit-learn delving deeper: using data validation and cross-validation. Also some other methods to improve the accuracy of your learning algorithms

Information about the libraries taught in this course

• Jupyter Notebook: a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more
• NumPy: the fundamental package for scientific computing with Python, which contains useful things like: a powerful N-dimensional array object; sophisticated (broadcasting) functions; useful linear algebra, Fourier transform, and random number capabilities. We will also be using it as an efficient multi-dimensional container of generic data.
• SciPy: provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. This library builds on top of NumPy and makes heavy use of all the features that we will be learning in NumPy.
• matplotlib: a plotting library which produces publication quality figures and can also be used to do image manipulation. You can generate plots, histograms, power spectra, bar charts, error charts, scatter plots, with just a few lines of code.
• pandas: is an easy to use data structuring and data analysis library which we will be using. It has advanced data manipulation capabilities and can use data objects in the same way we use databases. It can also import and export data from a vast number of formats.
• Scikit-learn: built on top of NumPy, SciPy, and matplotlib this is one of the most widely used machine learning libraries in industry and research. It covers a truly impressive number of machine learning techniques and methods, some of which include: classification, regression, clustering, dimensionality reduction, model selection, data pre-processing, etc.