Introduction to Data Analytics and Machine Learning with Python

City University London
Course summary
Professional Training
10 weeks
560 GBP
Online courses
Course Dates
Online courses
560 GBP
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Course description

Introduction to Data Analytics and Machine Learning with Python

Understand the key principles of analysis and machine learning, perfect for experienced Python programmers.

You will learn the state of the art in data analytics and machine learning by leveraging the most widely used Python libraries - developed and maintained by big companies like Google, Facebook and Twitter.

As both data analytics and machine learning fields are vast and fast expanding, we will focus our efforts on grasping the foundations. The foundations which we will go through could enable you to get a junior position as a data analyst and/or machine learning engineer.

Libraries that will be taught in this course:

  • Jupyter Notebook
  • NumPy
  • SciPy
  • matplotlib
  • pandas
  • Scikit-learn

Suitability - Who should attend?

This course is designed to open the vast world of data analytics and machine learning to those without prior experience of the field, using Python. Knowledge of mathematical concepts is beneficial, and delegates must have Python already.


Applicants must have successfully completed the Introduction to programming with Python or have Python to a similar standard.

As this is an introductory data analytics course you are not expected to have any data analytics or machine learning experience.

Knowledge of mathematical concepts such as those presented here is essential.

English requirements:

Applicants must be proficient in written and spoken English.

Training Course Content

  • 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.

Course delivery details

Introduction to Data Analytics and Machine Learning with Python is taught over 10 lessons, once a week in the evening from18:30 to 20:30 on every Monday, allowing you to continue with full-time employment.

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City University London

City, University of London is a special place. With skill and dedication, we have been using education, research and enterprise to transform the lives of our students, our community and the world for a hundred years. We are proud of...

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Northampton Square
EC1V 0HB London

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