ONLINE COURSE – Introduction to Python and Programming in Python (PYIN02) This course will be delivered live
7 April 2021 - 8 April 2021£275.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – UK local time (GMT+0) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email firstname.lastname@example.org for full details or to discuss how we can accommodate you).
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. In order to use Python confidently and competently for these applications, it is necessary to have a solid foundation in the fundamentals of general purpose Python. This two day course provides a general introduction to the Python environment, the Python language, and general purpose programming in Python. We cover how to install and set up a Python computing environment, describing how to set virtual environments, how to use Python package installers, and overview some Python integrated development environments (IDE) and Python Jupyter notebooks. We then provide a comprehensive introduction to programming in Python, covering all the following major topics: data types and data container types, conditionals, iterations, functional programming, object oriented programming, modules, packages, and imports. Note that in this course, we will not be covering numerical and scientific programming in Python directly. That is provided in a subsequent two-day course, for which the topics covered in this course are a necessary prerequisite.
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This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here, but are covered in a subsequent course, the fundamentals taught here are vital for master data science and scientific applications of Python.
Venue – Delivered remotely
Time zone – GMT
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com. Failure to attend will result in the full cost of the course being charged.
Dr. Mark Andrews
Works at – Senior Lecturer, Psychology Department, Nottingham Trent University, England
Teaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R; Generalised Linear, Nonlinear and General Additive Models; Python for data science, machine learning, and scientific computing
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
This course will be hands-on and workshop based. Throughout each day, there will be some brief introductory remarks for each new topic, introducing and explaining key concepts. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time (GMT+0) at:
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining live sessions attendees will be able to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.
Assumed quantitative knowledge
No particular knowledge or understanding of Python is required.
Assumed computer background
No prior experience with Python or any other programming language is required. Of course, any familiarity with any other programming will be helpful, but is not required.
Equipment and software requirements
Attendees of the course must use a computer with Python (version 3) installed. All the required software, including Python itself, the development and programming environment tools, and the Python packages, are free and open source and are available on Windows, MacOs, and Linux. Instructions on how to install and configure all the software will be provided before the start of the course. We will also provide time during the workshops to ensure that all
software is installed and configured properly.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Wednesday 7th – Classes from 12:00 to 20:00
• Topic 1: Installing and setting up Python. There are many ways to write and execute code in Python. Which to use depends on personal preference and the type of programming that is being done. Here, we will explore some of the commonly used Integrated Development Environments (IDE) for Python, which include Spyder and PyCharm. Here,
we will also introduce Jupyter notebooks, which are widely used for scientific applications of Python, and are an
excellent tool for doing reproducible interactive work. Also as part of this topic, we will describe how to use virtual environments and package installers such as pip and conda.
• Topic 2: Data Structures. We will begin our coverage of programming with Python by introducing its different data structures.and operations on data structures This will begin with the elementary data types such as integers,
floats, Booleans, and strings, and the common operations that can be applied to these data types. We will then
proceed to the so-called collection data structures, which primarily include lists, dictionaries, tuples, and sets.
• Topic 3: Programming I. Having introduced Python’s data types, we will now turn to how to program in Python. We
will begin with iteration, such as the for and while loops. Here, we also cover some of Python’s functional
programming features, specifically list, dictionary, and set comprehensions.
Thursday 8th – Classes from 12:00 to 20:00
• Topic 4: Programming II. Having covered iterations, we now turn to other major programming features in Python,
specifically, conditionals, functions, and exceptions.
• Topic 5: Object Oriented Programming. Python is an object oriented language and object oriented programming in
Python is extensively used in anything beyond the very simplest types of programs. Moreover, compared to other
languages, object oriented programming in Python is relatively easy to learn. Here, we provide a comprehensive
introduction to object oriented programming in Python.
• Topic 6: Modules, packages, and imports. Python is extended by hundreds of thousands of additional packages.
Here, we will cover how to install and import these packages, but more importantly, we will show how to write our
own modules and packages, which is remarkably easy in Python relative to some programming languages.