Our Python training courses are designed to help meet the growing demand for Python skills amongst data scientists, AI and ML engineers.
Python ranks as the 1st most popular programming language and it is projected to continue to grow in popularity. Get trained and certified with our courses!
If you have just left school or changing careers from a non-IT field our Career Kickstarter training progammes are for you. Designed to take one from rudimetary computer skills to a full blown developer in as little as four sessions.
4 Week Training Programme
Our Career Kickstarter is a 4 week long training course with learning material and exercises assigned weekly accompanied by instructor-led lectures to help students master the material.
Additional support is provided via one-on-one online-chat and video calls with mentors and instructors using our learning platform. This platform also facilitates student interaction via forums and social media integration.
Instructor-Led Lectures
Instructor-led lectures are held online from 16:00-19:00 or face-to-face from 9:30 - :12:30 on Saturdays at our campus in Ferndale. These lectures will help assist students to obtain a comprehensive understanding of Python programming. Choose the course format that is right for you!
For corporates and professionals who already have some coding experience or background in IT we offer a weekend training option. This format is preferred by our corporate customers and those who like a more focused and intensive training session.
Our weekend courses are delivered in face-to-face training sessions or online via our learning platform that allows students to access additional support and training material.
Coding Classes
Want to change careers, master a new skill or give your kids a lead-start in life? If so then our coding classes are for you. Our coding class format is aimed at those who prefer a more open ended approach to learning a new skill. A weekly 1 hour session is held with students either online or in class to discuss material they are working through. There is no fixed time line to cover concepts and material and we work at the pace of each student. This format is similar to the approach used to learn to play a musical instrument or additional maths lessons.
Description
Sessions
ZAR Price
USD Price
Python Coding Classes
4
R800
$50
Python Certification Training
Our fundamentals to advanced python training courses are based on the Python Institutes certifications objectives. Students completing these training courses will be able to pass the Python certification exams from entry-level to professional.
Combined Python Fundamentals & Intermediate Training
This format is preferred by our corporate customers who have limited time and want to cover both courses with a combined, focused and intensive training session.
With the growth of Data Science, Artificial Intelligence, and Machine Learning we have introduced courses that focus on the specific needs of professionals looking to gain additional skills in this area.
fundamental concepts: interpreting and the interpreter, compilation and the compiler, language elements, lexis, syntax and semantics, Python keywords, instructions, indenting
Data Collections – Lists, Tuples, and Dictionaries
simple lists: constructing vectors, indexing and slicing, the len() function
lists in detail: indexing, slicing, basic methods (append(), insert(), index()) and functions (len(), sorted(), etc.), del instruction, iterating lists with the for loop, initializing, in and not in operators, list comprehension, copying and cloning
lists in lists: matrices and cubes
tuples: indexing, slicing, building, immutability
tuples vs. lists: similarities and differences, lists inside tuples and tuples inside lists
dictionaries: building, indexing, adding and removing keys, iterating through dictionaries as well as their keys and values, checking key existence, keys(), items() and values() methods
strings in detail: ASCII, UNICODE, UTF-8, immutability, escaping using the \ character, quotes and apostrophes inside strings, multiline strings, copying vs. cloning, advanced slicing, string vs. string, string vs. non-string, basic string methods (upper(), lower(), isxxx(), capitalize(), split(), join(), etc.) and functions (len(), chr(), ord()), escape characters
Functions
defining and invoking your own functions and generators
return and yield keywords, returning results,
the None keyword,
recursion
parameters vs. arguments,
positional keyword and mixed argument passing,
default parameter values
converting generator objects into lists using the list() function
name scopes, name hiding (shadowing), the global keyword
Python Intermediate Training
PCAP | Certified Associate in Python Programming certification
Control and Evaluations
basic concepts: interpreting and the interpreter, compilation and the compiler, language elements, lexis, syntax and semantics, Python keywords, instructions, indenting
building loops: while, for, range(), in, iterating through sequences
expanding loops: while-else, for-else, nesting loops, and conditional statements
controlling loop execution: break, continue
Data Aggregates
strings in detail: ASCII, UNICODE, UTF-8, immutability, escaping using the \ character, quotes and apostrophes inside strings, multiline strings, copying vs. cloning, advanced slicing, string vs. string, string vs. non-string, basic string methods (upper(), lower(), isxxx(), capitalize(), split(), join(), etc.) and functions (len(), chr(), ord()), escape characters
lists in detail: indexing, slicing, basic methods (append(), insert(), index()) and functions (len(), sorted(), etc.), del instruction, iterating lists with the for loop, initializing, in and not in operators, list comprehension, copying and cloning
lists in lists: matrices and cubes
tuples: indexing, slicing, building, immutability
tuples vs. lists: similarities and differences, lists inside tuples and tuples inside lists
dictionaries: building, indexing, adding and removing keys, iterating through dictionaries as well as their keys and values, checking key existence, keys(), items() and values() methods
Functions and Modules (25%)
defining and invoking your own functions and generators
return and yield keywords, returning results, the None keyword, recursion
parameters vs. arguments, positional keyword and mixed argument passing, default parameter values
converting generator objects into lists using the list() function
name scopes, name hiding (shadowing), the global keyword
lambda functions, defining and using
map(), filter(), reduce(), reversed(), sorted() functions and the sort() method
the if operator
import directives, qualifying entities with module names, initializing modules
writing and using modules, the __name__ variable
pyc file creation and usage
constructing and distributing packages, packages vs. directories, the role of the __init__.py file
hiding module entities
Python hashbangs, using multiline strings as module documentation
Classes, Objects, and Exceptions
defining your own classes, superclasses, subclasses, inheritance, searching for missing class components, creating objects
class attributes: class variables and instance variables, defining, adding, and removing attributes, explicit constructor invocation
class methods: defining and using, the self parameter meaning and usage
inheritance and overriding, finding class/object components
single inheritance vs. multiple inheritance
name mangling
invoking methods, passing and using the self argument/parameter
using predefined exceptions and defining your own ones
the try-except-else-finally block, the raise statement, the except-as variant
exceptions hierarchy, assigning more than one exception to one except branch
adding your own exceptions to an existing hierarchy
assertions
the anatomy of an exception object
input/output basics: opening files with the open() function, stream objects, binary vs. text files, newline character translation, reading and writing files, bytearray objects
Pandas – a library providing high-performance and data analysis tools
SciKit-image – a collection of algorithms for image processing
Design Patterns
Object-oriented design principles and the concept of design patterns
The Singleton Design Pattern
The Factory Pattern
The Façade Pattern
The Proxy Pattern
The Observer Pattern
The Command Pattern
The Template Method Pattern
Model-View-Controller
The State Design Pattern
Interprocess Communication
multiprocessing — Process-based parallelism
threading — Thread-based parallelism
subprocess — Subprocess management
Multiprocess synchronisation
queue — A synchronized queue class
socket — Low-level networking interface
mmap — Memory-mapped file support
Python-MySQL Database Access
Relational databases – fundamental principles and how to work with them
MySQL vs. rest of the world
CRUD Application
db connection
db create
db insert
db read
db update
db delete
Python for Data Science Training
Introduction & Review of Python Syntax - Quick introduction to Python and revision of the fundamentals of Python Programming
Jypter Notebooks - Learn how to use Jupter Notebooks for interactive data science & scientific computing
Numpy - Master the basics of data analysis in Python with Numpy. Expand your skill set by learning scientific computing.
Pandas - Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames. Learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
Visualisations - Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas. master complex data visualization techniques using Matplotlib and Seaborn and create versatile and interactive data visualizations using Bokeh.
Machine Learning
Supervised learning with scikit-learn: Learn how to build and tune predictive models with supervised learning and understand how to evaluate their performance on unseen data. Learn how to build a model to automatically classify items.
Unsupervised learning with scikit-learn: Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Neural Networks using Keras 2.0: Learn the fundamentals of neural networks and how to build deep learning models .
Network Analysis - Master the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Working with databases & text processing - Learn to import data into Python from various sources, such as Excel, SQL, SAS and from the web. Master the basics of querying tables in relational databases such as MySQL, Oracle, SQL Server, and PostgreSQL.
We build long relationships with our customers that helps improve & understanding their needs. We offer customised solutions & training to meet business requirements.
Our clients include large & small businesses in South Africa & across the globe. We offer both remote and on-site support.
Passion for Technology
We are passionate about open source & pride ourselves with living on the bleeding edge of technology innovation. Our customers lean on our practical experience with emerging technologies to ensure they get the benefits of early adopters & avoid the pitfalls.