Introduction to Python
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Our 3 week evening Introduction to Python course is aimed at beginners, preparing you for a Python-powered career.
Classroom Teaching or Remote Learning
You can choose to join us at our location in Monument or remote into our classroom via our Live Video Feed. Remote students participate fully in each class, including screen-sharing with the instructor and communicating via audio and live chat functionality.
A webcam and headphones or headset will be required for remote learning.
Learn Python in a classroom environment or from the comfort of your home.
2 COURSE FORMATS:
CLASSROOM – 3 Weeks @ Vauxhall, London, Mon to Fri (15 sessions), 6:30pm – 8:30pm
£795 + VAT
9th March 2020
27th April 2020
REMOTE – 3 Weeks, via Live Video Feed, Mon to Fri (15 sessions), 6:30pm – 8:30pm
£550 + VAT
9th March 2020
27th April 2020
Or try our 1 Day Python For Beginners Course
8 Hour Saturday Course – from £225 + VAT
By the end of this course:
You will be able to write a complex several hundred line program that works as intended (and is also readable!). Writing a program of this length is good practice for ensuring that you can split programs into multiple files using constructs such as modules, classes and functions. You’ll leave with a substantive project that can be demonstrated to potential future or current employers.
Our Teaching:
We firmly believe in classroom-based study versus online, pre-recorded content, because it ensures your teacher is always on-hand to help you with the challenges you encounter.
Furthermore, we teach through projects selected by you, allowing you to choose the direction of your learning at a pace that suits you.
Our Teachers:
Our teachers are Data Scientists and Python programmers.
They come with years of commercial experience –
Meet Priyesh Kapadia
Priyesh is a Step Function Python teacher and works full-time as a Senior Data Scientist at Collinson Group. Priyesh has a Nuclear Engineering Doctorate form Imperial College, where he also worked as a Post-Doc developing Python based modelling tools.
Priyesh was a student on the Step Function Data Science Professional course in the summer of 2017.
Priyesh has taught students up to PhD level and enjoys sharing his knowledge.
Student Reviews:
“Priyesh has been very professional during the entire course, and very knowledgeable. He has always provided help, hints and been open to discuss suggestions and/or ways to improve the code efficiency. The course has been very well structured. Overall course has been a source of inspiration for me, and I would strongly recommend it!”
ENRICO – September 2019 Course
“The teacher was very knowledgeable with the potential to take students far. I enjoyed the exercises and projects, which gave a tangible feeling of what one can do with Python in the real world.”
IOANA COMSA – February 2018 Course
Introduction to Python Course Syllabus
Module 1 – Pythonic Way
How to use the basic python constructs: lists, dictionaries, functions and objects, while adhering to pythonic principals and good software design guidelines.
We focus on learning these concepts more thoroughly than other introductory python courses by working through multiple exercises.
Module 2 – Logic and Control Flow
Covering Boolean values and expressions, operators, conditional statements and loops. All learning is reinforced through exercises.
Module 3 – Exceptions
Exception handling, raising errors and user-defined errors.
Module 4 – Core Libraries and Process Automation
Master the core libraries of Python starting from scratch, e.g. datetime, csv, json, unittest.
Learn how to read from and write to csv files, facilitating automation of manual processes in the workplace.
Module 5 – Software Management Principles
Solid foundation in software management principles, how to design, manage and run software projects.
Including version control systems like Github, development environments in MacOS and windows and ipython.
We also introduce concepts related to using Amazon Web Services as a coding environment, which is an effective introduction to the principles of cloud computing.
Module 6 – Automation Project
A common application of Python is process automation. With our IBM Watson project we teach you to write a fully automated process using Python: write the script, run it, and sit back while Python makes complicated tasks look quick and simple.
Module 7 – Running Python Scripts in the Cloud
This module focuses on how to automate and schedule the execution of Python scripts in the cloud. For example, you can save your company time and money by automatically running key reports at set times of the day.
Module 8 – 3rd Party Data Services and APIs
Using 3rd party data services is a crucial skill not covered by most introductory python courses, yet it’s something you are expected to understand from day one in most python related roles.
We provide a hands-on demonstration and tutorial on using important API services such as Google Maps and Twitter.
Project Choice 1 – Google Maps API
Write a python application that is able to give you directions between two addresses, or is able to give you information about a particular address.
The application makes use of Google Maps API and is an excellent example of writing python applications that make use of 3rd Party Mapping data.
Project Choice 2 – Twitter Analysis
Write a script that automatically grabs all the latest tweets on a particular topic in twitter, and then generates a csv file containing the main keywords appearing in the tweets.
The script is an excellent demonstration of writing simple python scripts to automate manual, tedious workflows that people often carry out in their job, when handling and analysing data.
Project Choice 3 – Financial Market Pricing Data
Retrieve equity price data, plot graphs using Python and create price alerts. Your script will send an email/text message to notify of a buy or sell event.
Optional Follow Up Course – Introduction to Machine Learning
You my choose to extend the 3 week course by 1 week to incorporate our Introduction to Machine Learning module:
- Principles of Machine Learning
- Supervised Learning
- Linear Regression
- Classification
- Decision Trees
- Unsupervised Learning
- K-Means Clustering
- Exercises
Please note that the details of this Introduction to Python Course syllabus are accurate at the time of printing. There may be some variation in the final syllabus that is taught.
Step Function, August 2019