Allowing us to make sense of big data, Python is the future when it comes to data analytics.
The very popular Introduction to Data Analytics and Machine Learning with Python 3 short course has been designed to open the vast world of data analytics and machine learning to non-technical people without prior experience of the field, using the Python programming language.
Python 3 is the last iteration of the Python language, and so it will be useful to learn the tools and techniques we teach in this course in Python 3.
As this is an introductory data analytics course you are not expected to have any data analytics or machine learning experience. Pre-requisites are successful completion of Introduction to programming with Python or knowledge of topics therein and knowledge of mathematical concepts such as those presented in the website (http://www.gcseguide.co.uk/mathsgcseguide.htm)
The Introduction to Data Analytics and Machine Learning with Python short course is taught over 10 weeks in the evenings, allowing you to continue with full-time employment. Studying one of our short courses is a fantastic way to learn new skills and can be used as a great way to further your career.
The April start date is currently full.
For students who already have a sound working knowledge of Python
You will learn the state of the art in data analytics and machine learning by leveraging the most widely used Python libraries, which are 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:
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 in the website (http://www.gcseguide.co.uk/mathsgcseguide.htm) is essential.
Applicants must be proficient in written and spoken English.
Informal assessment through optional weekly assignments, which will build into a final project that will solve a real world problem using real world data, applying state of the art techniques taught during the course.
Michal Grochmal is a trained physicist and professional data architect, with a passion for data science. His everyday work entails passing data around, make it flow through pipelines, aggregate it into useful sets and ‘munging’ the data together to make something useful. An expert in machine learning, Michal is skilled at writing maths into code, profiling the resulting code and re-implementing the slow parts in a lower language (e.g. C).
With a strong background in programming, most prominently in Python language, Michal is also a computer security specialist. He is a great fan of applied mathematics to core computer science, notably the use of Machine Learning algorithms, to information security and convex optimization. He has a passion for topology, linear algebra and vectorial calculus.
Cosmin Stamate started programming on a ZX Spectrum clone when he was 8. Self taught, he has been consulting in several areas of software engineering, from programming to architecture, and web development for over 10 years. He has a MSc in Intelligent Technologies from Birkbeck, University of London where he took a particular interest in in artificial neural networks and evolutionary algorithms.
Some of Cosmin's industry roles include data analyst (on a consulting basis) for Tesco and Schroders. Currently he is working towards a hybrid PhD which bridges the Department of Computer Science and the Department of Psychological Sciences at Birkbeck; the PhD is focused on developing novel deep learning algorithms that model certain cognitive and behavioural processes.
Cosmin is also an active member of the Centre for Brain & Cognitive Development and Birkbeck Babylab where he applies state of the art machine learning on electroencephalogram data.