What is Python?
Python is an easy to learn, general purpose programming language that has developed into one of the most popular programming languages since its first release in 1991 and is now widely used by companies such as Google, Facebook, Instagram, Spotify, Reddit, Netflix, Amazon and Uber.
Python is not only easy to learn, but also simple to use and easy to read which enables you to write code faster and collaborate more effortlessly. It is one of the prime choices for data science and artificial intelligence as it features a rich pool of libraries to transform, visualise and analyse data and is used in technology, finance, healthcare, retail, and ecommerce for those purposes.
Python vs R: Which is right for you?
R is another very popular programme language for data science and both have their pros and cons without there being a clear winner. Python is easier to integrate with other software and is more versatile. Python is the better choice when it comes to web applications, software development, task automation and integration of analysis with web applications and production databases. The R analysis ecosystem is superior and provides a much larger number of libraries specialised in different types of statistical analysis and its ggplot library is still the gold standard when it comes to data visualisation.
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Programming experience: Python and R are both easy to learn with many online resources available to continue to learn independently. If you already know another object-oriented programming language Python might feel more natural to you.
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Your environment: Which programming language do your peers, fellow students or teachers use? What is more common in your field of study? Do future employers or job sectors that you might target after graduation have a preference?
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Your goals: Some models and visualisations might be better supported in R than Python. Also consider your long-term career goals. If you aspire to become a software developer, you might prefer Python.
Python Workshops
The DSL Python training is split into two workshops. Both workshops have an open format where you will work on materials from a specific topic, i.e. Python Fundamentals, Data Wrangling or Data Visualization.
Join the Python Introduction workshops to learn about the most fundamental aspects of programming in Python. These workshops provide an opportunity for you to explore what programming in Python is like before moving on to more complex techniques and concepts in the Python for Data Science workshops. You will be working on the first four lessons from the Python Fundamentals materials.
Once you complete the first four lessons from the Python Fundamentals materials, you can move on to the Python for Data Science workshops which teach you the fundamental Python skills required to work with libraries designed for data transformation and visualization. You will continue to learn more about the foundational Python skills from the Python Fundamentals materials and then progress to Data Wrangling, where you will learn how to use pandas to import, transform and explore data. Finally, the Python Data Visualization materials will introduce you to various ways you can create charts with Python.
Location
These workshops take place in LBR.R.08 located on the lower ground floor of the library. Computers are provided.
Technical Requirements
All software is provided on lab computers. If you are using your own laptop during the workshops please ensure you have the required software installed as below.
- Anaconda Navigator (Mac / Windows) Note: You will have to install Anaconda Navigator to be able to use Python and Jupyter Notebooks. Run the installer. Do NOT change any of the default settings. For help on installing Anaconda, please see this video.
Click on the link below to check availability and book your place:
Python Introduction
Python for Data Science
The Python training series is a meaningful task based introduction to Python coding language. The instructors provide guidance when needed throughout the independent partnered learning experience. Very grateful for this resource!
Laura Stahl, Department of Anthropology