Python Advanced

Python Advanced Training Course

This course is not for novices in coding, we assume you know the non OO fundamentals of any coding language. A critical course in Python

Prerequisites and Further Training

You should definitely be very well familiar with our Python Beginner Topics.

Intended Audience

Whether you are new to programming or a professional developer, this course is designed to bring you up to speed on the Python language in ways that more limited approaches cannot.

Further Training

Also have a look at our Python Data Science Bootcamp .

Course Material

Supplied in electronic format unless specified otherwise

Course Content

DAY 1

Text Processing and Function Topics

  • Text Manipulation
  • First Class Functions
  • Design Patterns with First Class Functions
  • Decorators and Closures

DAY 2

OO Review and sequence operations

  • Object References, mutability and recycling
  • Pythonic Objects
  • Sequence operations (hacking, hashing, slicing)

DAY 3

Coroutines and concurrency

  • Coroutines
  • Concurrency
  • Asyncio
  • Tornado
  • Trolius

DAY 4

Metaprogramming

  • Dynamic attributes and properties
  • Data wrangling with dynamic attributes
  • Exploring JSON-like data with dynamic attributes
  • The invalid attribute name problem
  • Flexible object creation with __new__
  • Restructuring the OSCON feed with shelve
  • Linked record retrieval with properties
  • Using a property for attribute validation
  • LineItem take #1: class for an item in an
  • LineItem take #2: a validating property
  • A proper look at properties
  • Properties override instance attributes
  • Property documentation
  • Coding a property factory
  • Handling attribute deletion
  • Essential attributes and functions for attribute handling
  • Special attributes that affect attribute handling
  • Built-in functions for attribute handling
  • Special methods for attribute handling

Attribute descriptors

  • Descriptor example: attribute validation
  • LineItem take #3: a simple descriptor
  • LineItem take #4: automatic storage attribute names
  • LineItem take #5: a new descriptor type
  • Overriding versus non-overriding descriptors
  • Overriding descriptor
  • Overriding descriptor without __get__
  • Non-overriding descriptor
  • Overwriting a descriptor in the class
  • Methods are descriptors
  • Descriptor usage tips
  • 1. Use property to keep it simple
  • 2. Read-only descriptors require __set__
  • 3. Validation descriptors can work with __set__ only
  • 4. Caching can be done efficiently with __get__ only
  • 5. Non-special methods can be shadowed by instance attributes
  • Descriptor docstring and overriding deletion

Class metaprogramming

  • A class factory
  • A class decorator for customizing descriptors
  • What happens when: import time versus run time
  • The evaluation time exercises
  • Metaclasses
  • The metaclass evaluation time exercise
  • A metaclass for customizing descriptors
  • The metaclass __prepare__ special method
  • Classes as objects

DAY 5

  • Data Science Libraries Overview
  • Practicals and Test

Duration and pricing

  • Full-time over 10 days (R19 995)
  • Part-time over 4 weeks (2 nights per week, 3 hour sessions) (R11995)
  • Part-time over 8 Saturdays, 3 hour sessions (R11995)
  • Please note : For part-time courses we do not have a fixed schedule and you will be placed on a waiting list until we get a group of 4+ together. Please book with no dates on the bookings form. This will automatically put you on the waiting list. We will confirm with you as soon as we have a part-time group together.
  • Distance-learning over up to 3 months (R9995)
  • International exams are not included in the course price.
  • Prices exclude Vat

Certificate

  1. Upon completion of this course we will issue you with attendance certificate to certify your attendance and / or completion of the prescribed minimum examples.
  2. You may sit for our competency assessment test and on passing you will obtain our competency certificate.
  3. Our competency assessment can be booked and taken by someone who has not attended the course at a cost of R950.

Bookings

You can download the course registration form on our home page or by clicking here

Brochure

You may download a pdf copy of this page by clicking on the pdf icon at the top of the page.

Questions

Please email us

Schedule

On the calendar below. If your browser doesn’t display the calendar below, please click on this link or try using Google Chrome, alternatively please enquire via our Contact Us page.

We are a member of the Python Software Foundation

PythonSoftwareFoundation




Python Advanced (Full-Stack)

Python Advanced (Full-Stack Python )

Want to learn the different ways a Python App can be built? We start with an overview of the language, then develop a game using Python’s popular gaming libraries. Our second project is a Python GUI app to store and report on data. Our last app is a Web App, using Django.

Prerequisites / Further Training

Python Beginner

Also have a look at our Python Bootcamp

Intended Audience

The goal of this course is to bring you up to speed with Python as quickly as possible so you can build programs that work—games, data visualizations, and web applications—while developing a foundation in programming that will serve you well for the rest of your career. Python Advanced is written for Python Beginner Developers who want to learn how to apply the language practically – it is not a Python beginner’s course – you should have passed the level of Python Beginner before doing this course . In this course we look at the 3 most popular ways to develop Python apps – Games, GUI-based and Web-based.

After this course you should be able to

  • In the first part of this course you’ll learn basic programming concepts you need to know to write Python programs.
    You’ll learn about different kinds of data and the ways you can store data in lists and dictionaries within your programs.
  • You’ll learn to build collections of data and work through those collections in efficient ways.
    You’ll learn to use while and if loops to test for certain conditions so you can run specific sections of code while those conditions are true and run other sections when they’re not—a technique that greatly helps to automate processes.
    You’ll learn to accept input from users to make your programs interactive and to keep your programs running as long as the user is active.
    You’ll explore how to write functions to make parts of your program reusable, so you only have to write blocks of code that perform certain actions once, which you can then use as many times as you like.
  • You’ll then extend this concept to more complicated behavior with classes, making fairly simple programs respond to a variety of situations.
  • You’ll learn to write programs that handle common errors gracefully. After working through each of these basic concepts, you’ll write a few short programs that solve some well-defined problems.
  • Finally, you’ll take your first step toward intermediate programming by learning how to write tests for your code so you can develop your programs further without worrying about introducing bugs.
  • In the first project you’ll create a Space Invaders–style shooting game called Alien Invasion, which consists of levels of increasing difficulty.
    After you’ve completed this project, you should be well on your way to being able to develop your own 2D games.
    The second project introduces you to data visualization. Data scientists aim to make sense of the vast amount of information available to them through a variety of visualization techniques.
  • You’ll work with data sets that you generate through code, data sets downloaded from online sources, and data sets your programs download automatically.
    After you’ve completed this project, you’ll be able to write programs that sift through large data sets and make visual representations of that stored information.
    In the third project you’ll build a small web application that allows you to keep a journal of ideas and concepts you’ve learned about a specific topic. You’ll be able to keep separate logs for different topics and allow others to create an account and start their own journals.
  • You’ll also learn how to deploy your project on Heroku so anyone can access it online from anywhere.

Course Material

Supplied

Course Contents

Day 1

  • Python 2 vs Python 3
  • Python on different Operating Systems
  • Running Python from a Terminal
  • Python Variables and Simple Data Types
  • Lists
  • Working with Lists
  • if Statements
  • Dictionaries

Day 2

  • User Input and while Loops
  • Functions
  • Classes, Inheritance, Importing Classes
  • Python Standard Library
  • Files and Exceptions,
  • Testing Your Code
Day 3
Project 1: Game Alien Invasion
  • Planning phase
  • Installation Pygame and more Packages
  • Building a game
  • Detecting mouse events and hiding the cursor.
  • Help button to display instructions on how to play.
  • Modify the speed of the game as it progresses
  • Building a Spaceship That Fires Bullets
  • Watching out for Aliens!
  • Display information in textual and non-textual ways.
  • Implement a progressive scoring system
Day 4
Project 2: Data Visualization
  • Generating Data
  • Installing Matplotlib
  • Plotting a Line Graph
  • Changing the Label Type and Graph Thickness
  • Correcting the Plot
  • Plotting and Styling Individual Points with scatter()
  • Plotting a Series of Points with scatter()
  • Calculating Data Automatically
  • Removing Outlines from Data Points
  • Defining Custom Colors
  • Using a Colormap
  • Saving Your Plots Automatically
  • Random Walks
  • Creating the RandomWalk() Class
  • Choosing Directions
  • Plotting the Random Walk
  • Generating Multiple Random Walks
  • Styling the Walk
  • Coloring the Points
  • Plotting the Starting and Ending Points
  • Cleaning Up the Axes
  • Adding Plot Points
  • Altering the Size to Fill the Screen
  • Installing Pygal
  • Collecting Data
  • Analysing Data
  • Creating Graphs
  • Downloading Data
  • The CSV File Format
  • Parsing the CSV File Headers
  • Printing the Headers and Their Positions
  • Extracting and Reading Data
  • Plotting Data in a Temperature Chart
  • The datetime Module
  • Plotting Dates
  • Plotting a Longer Timeframe
  • Plotting a Second Data Series
  • Shading an Area in the Chart
  • Mapping Global Data Sets: JSON Format
  • Downloading Lots of Data
  • Extracting Relevant Data
  • Converting Strings into Numerical Values
  • Building a Data Map
  • Plotting Numerical Data on a Map
  • Plotting a Complete Data Map
  • Grouping Categories within Categories
  • Styling Maps in Pygal
  • Lightening the Color Theme
  • Using a Web API
  • Git and GitHub
  • Requesting Data Using an API Call
  • Installing Requests
  • Processing an API Response
  • Working with the Response Dictionary
  • Summarizing the Top Repositories
  • Monitoring API Rate Limits
  • Visualizing Repositories Using Pygal
  • Refining Pygal Charts
  • Adding Custom Tooltips
  • Plotting the Data
  • Adding Clickable Links to Our Graph .
  • Use APIs to write self-contained programs that automatically gather the data they need and use that data to create a visualization.
  • Use GitHub API to explore the most-starred Python projects on GitHub
  • Explore the Hacker News API
    Use the requests package to automatically issue API calls to GitHub and process the results of that call.
  • Use Pygal settings to further customize the appearance of our generated charts.
Day 5
Project 3: Web Applications
  • Getting Started with Django
  • Writing a Spec
  • Creating a Virtual Environment
  • Installing virtualenv
  • Activating the Virtual Environment
  • Installing Django
  • Creating a Project in Django
  • Creating the Database
  • Viewing the Project
  • Defining Models
  • Activating Models
  • The Django Admin Site
  • Defining the Entry Model
  • Migrating the Entry Model
  • Registering Entry with the Admin Site
  • The Django Shell
  • Mapping a URL
  • Writing a View
  • Writing a Template
  • Template Inheritance
  • The Topics Page
  • Individual Topic Pages
  • Allowing Users to Enter Data
  • Adding New Topics
  • Adding New Entries
  • Editing Entries
  • Setting Up User Accounts
  • The users App
  • The Login Page
  • Logging Out
  • The Registration Page
  • Blog Accounts
  • Allowing Users to Own Their Data
  • Restricting Access with @login_required
  • Connecting Data to Certain Users
  • Restricting Topics Access to Appropriate Users
  • Protecting a User’s Topics
  • Protecting the edit_entry Page
  • Associating New Topics with the Current User
  • The django-bootstrap3 App
  • Using Bootstrap to Style Learning Log
  • Modifying base.html
  • Styling the Home Page Using a Jumbotron
  • Styling the Login Page
  • Styling the new_topic Page
  • Styling the Topics Page
  • Styling the Entries on the Topic Page
  • Other Forms
  • Stylish Blog
  • Deploying Learning Log
  • Making a Heroku Account
  • Installing the Heroku Toolbelt
  • Installing Required Packages
  • Creating a Packages List with a requirements.txt File
  • Specifying the Python Runtime
  • Modifying settings.py for Heroku
  • Making a Procfile to Start Processes
  • Modifying wsgi.py for Heroku
  • Making a Directory for Static Files
  • Using the gunicorn Server Locally
  • Using Git to Track the Project’s Files
  • Pushing to Heroku
  • Setting Up the Database on Heroku
  • Refining the Heroku Deployment
  • Securing the Live Project
  • Committing and Pushing Changes
  • Creating Custom Error Pages
  • Ongoing Development
  • The SECRET_KEY Setting
  • Deleting a Project on Heroku
  • Competency project

Duration and pricing

Certificate

About Our Certificates

Schedule

On the calendar on this page below.
If your browser doesn’t display the calendar below, please click on this link or try using Google Chrome, alternatively please enquire via our ‘Contact Us’ page.

Bookings

You can download the course registration form on our home page or by clicking here

Brochure

You may download a pdf copy of this page by clicking here.

Questions

Please email us

We are a member of the Python Software Foundation

PythonSoftwareFoundation




Python Beginner

Python Beginner Training Course

This course is not for novices in coding, we assume you know the non OO fundamentals of any coding language. A critical course in Python

Prerequisites and Further Training

You should not be a complete beginner for this course. If you cannot pass this test, you must do Intro To Programming first.

Also have a look at our Python Bootcamps

Intended Audience

Whether you are new to programming or a professional developer, this course is designed to bring you up to speed on the Python language in ways that more limited approaches cannot.

After this course you should be able to

Start programming in Python and tackle real world solutions in Python as taught on our Advanced Python course

Further Training

Consider doing our Advanced Python course

Course Material

We give you an original copy of the book: Learning Python (by Mark Lutz) as we use this mainly,
but we also give additional examples where it falls short.

Course Content

The contents could be adjusted slightly to best suit the group’s skill level and / or requirements.

DAY 1:

Getting Started

  • Why Do People Use Python?
  • Is Python a “Scripting Language”?
  • What’s the Downside?
  • Who Uses Python Today?
  • What Can I Do with Python?
  • How Is Python Developed and Supported?
  • What Are Python’s Technical Strengths?
  • How Does Python Stack Up to Language X?

How Python Runs Programs

  • Introducing the Python Interpreter
  • Program Execution
  • Execution Model Variations

How You Run Programs

  • The Interactive Prompt
  • System Command Lines and Files
  • Unix-Style Executable Scripts: #!
  • Clicking File Icons
  • Module Imports and Reloads
  • Using exec to Run Module Files
  • The IDLE User Interface
  • Other IDEs
  • Other Launch Options
  • Which Option Should I Use?

Types and Operations 

  • Why Use Built-in Types?
  • Python’s Core Data Types
  • Numbers
  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • Files
  • Other Core Types

Numeric Types

  • Numeric Type Basics
  • Numbers in Action
  • Other Numeric Types
  • Numeric Extensions

The Dynamic Typing Interlude 

  • The Case of the Missing Declaration Statements
  • Shared References
  • Dynamic Typing Is Everywhere

String Fundamentals 

  • String Basics
  • String Literals
  • Strings in Action
  • String Methods
  • String Formatting Expressions
  • String Formatting Method Calls
  • General Type Categories

DAY 2:

Lists and Dictionaries 

  • Lists
  • Lists in Action
  • Dictionaries
  • Dictionaries in Action

Tuples, Files, and Everything Else 

  • Tuples
  • Files
  • Core Types Review and Summary
  • Built-in Type Gotchas

Statements and Syntax 

  • The Python Conceptual Hierarchy Revisited
  • Python’s Statements
  • A Tale of Two ifs
  • A Quick Example: Interactive Loops

Assignments, Expressions, and Prints 

  • Assignment Statements
  • Expression Statements
  • Print Operations

if Tests and Syntax Rules 

  • if Statements
  • Python Syntax Revisited
  • Truth Values and Boolean Tests
  • The if/else Ternary Expression

Exceptions

  • Why Use Exceptions?
  • Exceptions: The Short Story
  • The try/except/else Statement
  • The try/finally Statement
  • Unified try/except/finally
  • The raise Statement
  • The assert Statement
  • Context Managers

DAY 3:

While and for Loops 

  • Loops
  • continue, pass, and the Loop else
  • Loops
  • Loop Coding Techniques

Iterations and Comprehensions 

  • Iterations: A First Look
  • List Comprehensions: A First Detailed Look
  • Other Iteration Contexts
  • New Iterables in Python 3
  • Other Iteration Topics

The Documentation Interlude 

  • Python Documentation Sources
  • Common Coding Gotchas

DAY 4:

Functions

  • Why Use Functions?
  • Coding Functions
  • A First Example: Definitions and Calls
  • A Second Example: Intersecting Sequences

Scopes 

  • Python Scope Basics
  • The global Statement
  • Scopes and Nested Functions
  • The nonlocal Statement in 3
  • Why nonlocal? State Retention Options

Modules 

  • Why Use Modules?
  • Python Program Architecture
  • How Imports Work
  • Byte Code Files: __pycache__ in Python 3.2+
  • The Module Search Path
  • Module Creation
  • Module Usage
  • Module Namespaces
  • Reloading Modules

DAY 5:

Classes and OOP 

  • Why Use Classes?
  • Classes Generate Multiple Instance Objects
  • Classes Are Customized by Inheritance
  • Classes Can Intercept Python Operators
  • The World’s Simplest Python Class
  • The class Statement
  • Methods
  • Inheritance
  • Namespaces: The Conclusion
  • Documentation Strings Revisited
  • Classes Versus Modules
  • OOP and Inheritance: “Is-a” Relationships
  • OOP and Composition: “Has-a” Relationships
  • OOP and Delegation: “Wrapper” Proxy Objects
  • Class Attributes
  • Methods Are Objects: Bound or Unbound
  • Classes Are Objects: Generic Object Factories
  • Multiple Inheritance: “Mix-in” Classes
  • Other Design-Related Topics
  • 7 Steps to a Full Class-Based Application

Duration and pricing

Price Group B

Certificate

  1. Upon completion of this course we will issue you with attendance certificate to certify your attendance and / or completion of the prescribed minimum examples.
  2. You may sit for our competency assessment test and on passing you will obtain our competency certificate.
  3. Our competency assessment can be booked and taken by someone who has not attended the course at a cost of R2950.

Bookings

You can download the course registration form on our home page or by clicking here

Brochure

You may download a pdf copy of this page by clicking on the pdf icon at the top of the page.

Questions

Please email us

Schedule

On the calendar below. If your browser doesn’t display the calendar below, please click on this link or try using Google Chrome, alternatively please enquire via our Contact Us page.

We are a member of the Python Software Foundation

PythonSoftwareFoundation




Python Data Science Bootcamp

Python Data Science Training Course

Pre-requisites

Intro To Programming or IT in Matric

Content

Duration:

Every module is presented in-classroom for 5 full days, with projects to be completed on campus and / or at home with video conferencing support over the internet over the following week. The total duration is 6 months. Weekly project progress meetings must be attended.

Full-time:

The above core modules are attended in the classroom on an average of one every 4 weeks. In between these modules practical assignments are worked on. Students do not have to be on campus when they work on practical assignments but will need to be internet connected to the lecturers via video conferencing if they are working from home.

Pricing

  1. Normal price is : R74 950
  2. Pre-paid price is : R64 950    <– most popular!!
  3. Two Payments of R35 000, 3 months apart
  4. Prices exclude Vat

Customised schedules are compiled for part-time (distance learning) students

Bootcamp Group discounts

Book more than one student on a 6 month bootcamp (normal price s R60 000). (Student has to attend class only 1 week per month – in between homework meetings are done via Google Hangouts ).

Contents

Self-study / Distance Learning

The Python Data Science Bootcamp is also available in a distance learning format.

Brochure

To download a brochure of this page in pdf format, please click on the pdf icon at the top of this page.

Booking / Enquiries

Please click here

Any questions? Please click here

Schedule

Look at the calendar below or please enquire .




Python Full-Stack Web Bootcamp

Pre-requisites

Intro to Coding or IT in Matric

Content

Modules covered
Summary
Beginner Python Python Language and OO Fundamentals
Advanced Python (Full-Stack) Building Various Apps with Python
SQL SQL Databases
Django Python Web Framework
Angular Front-End Front-End connecting to Python API
Django REST API Build Powerful REST API’s with Django

Duration:

Every module is presented in-classroom for 5 full days, with projects to be completed on campus and / or at home with video conferencing support over the internet over the following week. The total duration is 6 months. Weekly project progress meetings must be attended.

Full-time:

The above core modules are attended in the classroom on an average of one every 4 weeks. In between these modules practical assignments are worked on. Students do not have to be on campus when they work on practical assignments but will need to be internet connected to the lecturers via video conferencing if they are working from home.

Pricing

  1. Normal price is : R74 950
  2. Pre-paid price is : R64 950    <– most popular!!
  3. Two Payments of R35 000, 3 months apart
  4. Prices exclude Vat

Bootcamp Group discounts

Book more than one student on a 6 month bootcamp (normal price s R60 000). (Student has to attend class only 1 week per month – in between homework meetings are done via Google Hangouts ).

Self-study / Distance Learning

The Python Bootcamp is also available in a distance learning format.

Brochure

To download a brochure of this page in pdf format, please click on the pdf icon at the top of this page.

Booking / Enquiries

Please click here

Any questions? Please click here

Schedule

The next Python Bootcamp starts on 1 April 2019 to 30 September 2019

Look at the calendar below or please enquire .




Python Machine Learning

Python Machine Learning Training Course

Prerequisites and Further Training

You should definitely be very well familiar with our  Python AdvancedTopics.

Intended Audience

One of the fasted growing areas in the industry at teh moment is Data Science and Machine Learning. Join us now!

Further Training

Also have a look at our Python Data Science Bootcamp .

Course Material

Supplied in electronic format unless specified otherwise

Course Content

  • Giving Computers the Ability to Learn from Data

 

  • Building intelligent machines to transform data into knowledge
  • The three different types of machine learning
  • An introduction to the basic terminology and notations
  • A roadmap for building machine learning systems
  • Using Python for machine learning

 

Training Machine Learning Algorithms for Classification

 

  • Artificial neurons – a brief glimpse into the early history of machine learning
  • Implementing a perceptron learning algorithm in Python
  • Adaptive linear neurons and the convergence of learning

 

A Tour of Machine Learning Classifiers Using Scikit-learn

 

  • Choosing a classification algorithm
  • First steps with scikit-learn
  • Modeling class probabilities via logistic regression
  • Maximum margin classification with support vector machines
  • Solving nonlinear problems using a kernel SVM
  • Decision tree learning
  • K-nearest neighbors – a lazy learning algorithm

 

Building Good Training Sets – Data Preprocessing

 

  • Dealing with missing data
  • Handling categorical data
  • Partitioning a dataset in training and test sets
  • Bringing features onto the same scale
  • Selecting meaningful features
  • Assessing feature importance with random forests

 

Compressing Data via Dimensionality Reduction

 

  • Unsupervised dimensionality reduction via principal component analysis
  • Supervised data compression via linear discriminant analysis
  • Using kernel principal component analysis for nonlinear mappings

 

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

 

  • Streamlining workflows with pipelines
  • Using k-fold cross-validation to assess model performance
  • Debugging algorithms with learning and validation curves
  • Fine-tuning machine learning models via grid search
  • Looking at different performance evaluation metrics

 

Combining Different Models for Ensemble Learning

 

  • Learning with ensembles
  • Implementing a simple majority vote classifier
  • Evaluating and tuning the ensemble classifier
  • Bagging – building an ensemble of classifiers from bootstrap samples
  • Leveraging weak learners via adaptive boosting

 

Applying Machine Learning to Sentiment Analysis

 

  • Obtaining the IMDb movie review dataset
  • Introducing the bag-of-words model
  • Training a logistic regression model for document classification
  • Working with bigger data – online algorithms and out-of-core learning

 

9: Embedding a Machine Learning Model into a Web Application

 

  • Serializing fitted scikit-learn estimators
  • Setting up a SQLite database for data storage
  • Developing a web application with Flask
  • Turning the movie classifier into a web application
  • Deploying the web application to a public server

 

Predicting Continuous Target Variables with Regression Analysis

 

  • Introducing a simple linear regression model
  • Exploring the Housing Dataset
  • Implementing an ordinary least squares linear regression model
  • Fitting a robust regression model using RANSAC
  • Evaluating the performance of linear regression models
  • Using regularized methods for regression
  • Turning a linear regression model into a curve – polynomial regression

 

Working with Unlabeled Data – Clustering Analysis

 

  • Grouping objects by similarity using k-means
  • Organizing clusters as a hierarchical tree
  • Locating regions of high density via DBSCAN

 

Training Artificial Neural Networks for Image Recognition

 

  • Modeling complex functions with artificial neural networks
  • Classifying handwritten digits
  • Training an artificial neural network
  • Developing your intuition for backpropagation
  • Debugging neural networks with gradient checking
  • Convergence in neural networks
  • Other neural network architectures
  • A few last words about neural network implementation

 

Parallelizing Neural Network Training with Theano

 

  • Building, compiling, and running expressions with Theano
  • Choosing activation functions for feedforward neural networks
  • Training neural networks efficiently using Keras

Duration and pricing

Certificate

  1. Upon completion of this course we will issue you with attendance certificate to certify your attendance and / or completion of the prescribed minimum examples.
  2. You may sit for our competency assessment test and on passing you will obtain our competency certificate.
  3. Our competency assessment can be booked and taken by someone who has not attended the course at a cost of R950.

Bookings

You can download the course registration form on our home page or by clicking here

Brochure

You may download a pdf copy of this page by clicking on the pdf icon at the top of the page.

Questions

Please email us

Schedule

On the calendar below. If your browser doesn’t display the calendar below, please click on this link or try using Google Chrome, alternatively please enquire via our Contact Us page.

We are a member of the Python Software Foundation

PythonSoftwareFoundation




Python Pandas

This Python Pandas training course will teach you all about using Pandas for data analysis, from the beginning to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights.

Prerequisites / Further Training

Have a look at our Python Bootcamp

Intended Audience

DAY 1

Pandas Foundations 

Essential DataFrame Operations

DAY 2

Beginning Data Analysis 

Selecting Subsets of Data

DAY 3

Boolean Indexing 

Index Alignment 

DAY 4

Grouping for Aggregation, Filtration, and Transformation 

Restructuring Data into a Tidy Form 

DAY 5

Combining Pandas Objects 

Time Series Analysis 

Visualization with Matplotlib, Pandas, and Seaborn 

————————————-

Duration and pricing

In Price Group A

Certificate

Please read here about our certificates

Schedule

On the calendar on this page below.
If your browser doesn’t display the calendar below, please click on this link or try using Google Chrome, alternatively please enquire via our ‘Contact Us’ page.

Bookings

Please click click here or send us an email.

Questions

Please email us