Python Training Overview

Python Training Courses

Our Python training courses are designed to help meet the growing demand for Python skills amongst data scientists, AI and ML engineers.

Python's rank as the 3rd most popular programming language and it is projected to continue to grow in popularity.Get trained and certified with our courses!

Python Certification Training

We fundamental to advanced python training courses are based on the Python Institutes certifications objectives. Students completing these training will be able to pass the Python certification exams from entry-level to professional.

Description Days Price
Python Fundamentals (PCEP | Certified Entry-Level Python Programmer) 3 R7,900
Python Intermediate (PCAP | Certified Associate in Python Programming) 3 R9,900

Python Advanced (PCPP | Certified Professional in Python Programming 1)

4 R11,000

Python Advanced (PCPP | Certified Professional in Python Programming 2)

4 R11,000
 

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Python Specialist Trainings

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.

Description Days Price
Python Data Science Training 4 R21,900
 

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Python Entry Level & Assocaite

Python Entry Level Training

PCEP | Certified Entry-Level Python Programmer Certification

  • Basic Concepts
    • fundamental concepts: interpreting and the interpreter, compilation and the compiler, language elements, lexis, syntax and semantics, Python keywords, instructions, indenting
    • literals: Boolean, integer, floating-point numbers, scientific notation, strings
    • comments
    • the print() function
    • the input() function
    • numeral systems (binary, octal, decimal, hexadecimal)
    • numeric operators: ** * / % // + –
    • string operators: * +
    • assignments and shortcut operators
  • Data Types, Evaluations, and Basic I/O Operations
    • operators: unary and binary, priorities and binding
    • bitwise operators: ~ & ^ | << >>
    • Boolean operators: not and or
    • Boolean expressions
    • relational operators ( == != > >= < <= ), building complex Boolean expressions
    • accuracy of floating-point numbers
    • basic input and output operations using the input(), print(), int(), float(), str(), len() functions
    • formatting print() output with end= and sep= arguments
    • type casting
    • basic calculations
    • simple strings: constructing, assigning, indexing, slicing comparing, immutability
  • Flow Control – loops and conditional blocks (20%)
    • conditional statements: if, if-else, if-elif, if-elif-else
    • multiple conditional statements
    • the pass instruction
    • 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 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
    • literals: Boolean, integer, floating-point numbers, scientific notation, strings
    • operators: unary and binary, priorities and binding
    • numeric operators: ** * / % // + –
    • bitwise operators: ~ & ^ | << >>
    • string operators: * +
    • Boolean operators: not and or
    • relational operators ( == != > >= < <= ), building complex Boolean expressions
    • assignments and shortcut operators
    • accuracy of floating-point numbers
    • basic input and output: input(), print(), int(), float(), str() functions
    • formatting print() output with end= and sep= arguments
    • conditional statements: if, if-else, if-elif, if-elif-else
    • the pass instruction
    • simple lists: constructing vectors, indexing and slicing, the len() function
    • simple strings: constructing, assigning, indexing, slicing comparing, immutability
    • 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
    • the __init__ method
    • the role of the __str__ method
    • introspection: __dict__, __name__, __module__, __bases__ properties, examining class/object structure
    • writing and using constructors
    • hasattr(), type(), issubclass(), isinstance(), super() functions
    • 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
    • read(), readinto(), readline(), write(), close() methods

Python Professional Training

Python Professional Training

PCPP-32-1: Certified Professional in Python Programming 1

  • File Processing and Communicating with a Program’s Environment
    • Processing different kinds of files
      • sqlite3 – interacting with SQLite databases
      • xml – creating and processing XML files
      • csv – CSV file reading and writing
      • logging – basics logging facility for Python
      • configparser – configuration file parser
    •  Communicating with a program’s environment:
      • os – interacting with the operating system,
      • datetime – manipulating with dates and time
      • io – working with streams,
      • time – time access and conversions
  • Math, Science, and Engineering Tools
    • math – a basic tool for elementary evaluations
    • NumPy – fundamental package for scientific computing
    • SciPy – an ecosystem for mathematics, science, and engineering
    • Matplotlib – 2D plotting library producing publication quality figures
    • Pandas – a library providing high-performance and data analysis tools
    • SciKit-image – a collection of algorithms for image processing
  • GUI Programming
    • What is GUI and where it comes from
    • Constructing a GUI – basic blocks and conventions
    • Event-driven programming
    • Currently used GUI environments and toolkits
    • tkinter — Python interface to Tcl/Tk
      • tkinter’s application life cycle
      • Widgets, windows and events
      • Sample applications
      • pygame – a simple way of developing multimedia applications
  • Python Enhancement Proposals
    • What is PEP?
    • Coding conventions – not only style and naming
    • PEP 20 – The Zen of Python: a collection of principles that influences the design of Python code
    • PEP 8 – Style Guide for Python Code: coding conventions for code comprising the standard library in the main Python distribution
    • PEP 257 – Docstring Conventions: what is docstring and some semantics as well as conventions associated with them
    • A tour of important PEPs
  • Advanced Perspective of Classes and Object-Oriented Programming in Python
    • Classes, Instances, Attributes, Methods
    • Working with class and instance data
    • Copying object data using shallow and deep operations
    • Inheritance and Polymorphism
    • Different faces of Python methods: static and class methods
    • Abstract classes vs. method overloading
    • Composition vs. Inheritance – two ways to the same destination
    • Implementing Core Syntax
    • Subclassing built-ins
    • Attribute Encapsulation
    • Advanced techniques of creating and serving exceptions
    • Serialization of Python objects using the pickle module
    • Making Python object persistent using the shelve module
    • Metaprograming
      • Function decorators
      • Class decorators
      • Metaclasses

Python Certificated Professional Level 2

PCPP-32-2: Certified Professional in Python Programming 2 Certification

  • Creating and Distributing Packages
    • Using pip
    • Basic directory structure
    • The setup.py file
    • Sharing, storing, and installing packages
    • Documentation
    • License
    • Testing principles and techniques
      • unittest – Unit testing framework
      • Pytest – framework to write tests

 

  • 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 Network Programming
    • Python Socket Module
      • Introduction to sockets
      • Server Socket Methods
      • Client socket methods
      • General socket methods
      • Client-Server vs. Peer-to-peer
      • Other Internet nodules

 

  • 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.

About Us

About Us

Jumping Bean is an open source integration and training company that has been delivery solutions to customers for over 17 years.

Our services include:

  • Support
    • 24x7SLA based
    • Ad-hoc support,
  • Security consulting
    • Vulnerability scans,
    • Server hardening
    • Penetration tests
  • Training
    • Linux
    • Java
    • DevOps
    • Cloud

Long-Term Partnerships

We build long relationships with our customers which helps us better  understand their needs and offer customised solutions and training to meet their business requirements.

Our clients include large and small businesses in South Africa and across the globe.  We offer both remote and on-site support.

Passion for Technology

We are passionate about open source and love living on the bleeding edge of technology innovation. Our customers lean our our practical experience with emerging technologies to ensure they get the benefits of early adopters and avoid the pitfalls.

Training 100% Money Back Guarantee

We are so confident of the quality of our training that our courses carry a 100% money back guarantee. If at the end of the first day of training you are unsatisfied with the course we will refund 100% of your spend no questions asked!

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Jumping Bean
Appian Place, 373 Kent Ave
Ferndale,
2194
Tel: +2711-781 8014

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