Step into the world of data with this intermediate Python course, designed for confident Python programmers ready to explore core concepts in data analysis and machine learning using practical examples and real datasets.
Testimonials
Introduction to Data Analytics and Machine Learning with Python Course overview
Applicants must have successfully completed our Introduction to Programming with Python course, or have Python to a similar standard as a baseline.
Designed for established Python programmers, this online course is an introduction to machine learning and data analytics leveraging the most widely used Python libraries, developed and maintained by big companies like Google, Facebook and Twitter.
This course is taught through recorded lectures, with the lecturer present during class time to introduce topics and answer questions.
As both data analytics and machine learning fields are vast and fast expanding, we will focus our efforts on grasping the foundations. This 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
Put your foundation in Python programming to work making sense of big data.
The course will be taught using Python 3, the most recent iteration of the Python language.
Who is it for?
Aimed at established Python programmers, this online London-based course introduces the foundations of data analytics and machine learning to those new to the field.
Find out more about our Computer science and computing courses
Timetable
The Introduction to Data Analytics and Machine Learning with Python short course is offered in various formats to fit your availability:
- Weekly evening classes – taught one evening a week for 10 consecutive weeks, allowing you to continue in full-time employment
- Saturday classes – taught over five consecutive Saturdays, designed for weekend learners
- Summer School – taught intensively over one week in the summer, ideal for learning quickly or for staff training
Benefits
- Delivered by industry professionals
- Taught in small group sizes
- Awarded a City St George's, University of London certificate on completion
What will I learn?
Course Format
The course is delivered through pre-recorded videos of lectures by one of the course tutors that explain complex Machine Learning concepts, and Data Analytics tools and libraries in a simple way. Time is also allocated during classes for students to ask questions of the course tutor and clarify their understanding of the topics covered in the recorded content.
Students are encouraged to actively participate in the class in order to enhance their understanding of the course materials and use the provided video records to revisit the concepts discussed in the class when needed.
Please note: Applicants must have successfully completed the Introduction to Programming with Python or have Python to a similar standard, as a baseline.
Outline
- 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.
Assessment and certificates
You will be awarded an official City St George's, University of London certificate if you attend at least 70% of the classes. The course is not formally accredited.
Assessment
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.
Eligibility
Applicants must have successfully completed the Introduction to Programming with Python or have Python to a similar standard, as a baseline.
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.
(NB Candidates with strong programming skills in other languages - C++, Java etc. - may be able to convert as they go but should consult the syllabus linked above for guidance.)
English requirements
Applicants must have fluent written and spoken English.
Tutors
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Michal Grochmal (Mondays)
Michal Grochmal is a 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, Michal is also a computer security specialist. He is a great fan of applying 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.
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Cosmin Stamate (Tuesdays)
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 M.Sc. in Intelligent Technologies from Birkbeck, University of London where he took a particular interest 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 Ph.D. which bridges the Department of Computer Science and the Department of Psychological Sciences at Birkbeck; the Ph.D. is focused on developing novel deep learning algorithms that model certain cognitive and behavioural processes.
Cosmin is an active member of the Centre for Brain & Cognitive Development and Birkbeck Babylab where he applies state-of-the-art machine learning to electroencephalogram data.
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Spyridon Chouliaras
Spyridon Chouliaras holds a B.Sc. in Mathematics from University of Ioannina, Greece, and an M.Sc. in Data Science from Birkbeck, University of London. He has many years of experience in teaching machine learning, cloud computing, data analytics and database management courses on both undergraduate and postgraduate levels in a number of institutions including City, University of London.
Spyridon is currently working towards a Ph.D. in the Department of Computer Science and Information Systems, Birkbeck, University of London. His research focuses on cloud computing, auto-scaling, resource provisioning and anomaly detection, and his everyday tasks include leveraging machine learning and cloud computing to solve real-world problems.
