Efficient Application Development With Python3 For Beginners

What you'll learn
Implement the List and Dictionary data types to take text as input and produce a word count
Work with Python Modules to create your first web-scraping app in Python
Handle files using your Python code to build your own Python-based text editor
Programming in Python using a modular approach
Developing apps using object-oriented Python programming
Build powerful Graphical User Interfaces (GUIs)
Speed up your code with natively Python idioms
Requirements
This course doesn't assume any knowledge of Python or Python programming experience.
Description
Python is an open-source community-supported, a general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python.With this application development course with Python 3, you'll first learn about variables, control flow statements & much more make use of them in Python programs. Then you will learn to use Python's advanced data structures such as lists and dictionaries. Then you will get a hands-on project building such as build a game that consists of a deck of playing cards, Dice-Rolling Simulator in Python, Building Architectural Marvels & much more. Moving further, you'll learn to troubleshoot your python application where you can quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Begin Python Programming in 7 Days will get you started by setting up your environment and the tools you need to start programming in Python. You will be learning about variables and operators and how to make use of them in Python programs. You will learn all about control flow statements and loops in Python and you will be using them in your programs to solve your coding problems. Then you will learn to use Python's advanced data structures such as lists and dictionaries. You will be able to organize in functions and save time coding by writing code that can be reused. Then, you will learn about Python modules and how to make use of them. On the last day, you will start interacting with files using Python code. The course will give you a strong entry point into programming in general and programming in Python in particular.The second course, Python By Example explores Python basics, data structures, and algorithms. We'll build a die rolling simulator to see how to use Python dictionaries, loops, functions, and control statements. Then, we will see how we can develop dictionaries that contain other dictionaries to build complex data structures. Next, we will use a modular approach to build a game that consists of a deck of playing cards. We will use object-oriented (OOP) Python classes to do so. We will display the playing cards both in a textual form, which we create, as well as via image files. This will lead to our displaying card images in a graphical form using Python's built-in Tkinter package. In the next part, we will use multiple inheritances with OOP classes. While the Java and C# programming languages are limited to only single inheritance, Python classes can inherit from multiple classes. You will learn how to use multiple inheritances with Python. You will also build Graphical User Interfaces (GUIs). We will use Python's built-in Tkinter package and delve more deeply into GUI programming. By the end of this video tutorial, you will have built some useful utilities using Python. Python is very strong at searching directory folders, replacing words within modules, and much more. You will find these utilities useful in your everyday work as a developer.The third course, Troubleshooting Python Application Development takes you through a structured journey of performance problems that your application is likely to encounter, and presents both the intuition and the solution to these issues. You'll get things done, without a lengthy detour into how Python is implemented or computational theory. Quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.About the Authors:Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with High Dimension, IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform to learn deeply about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.Burkhard is a professional software test automation designer, developer, and analyst. He has more than 18 years' professional experience working for several software companies in California, USA. He currently works as an independent Python consultant from New York. He is the author of the Python GUI Programming Recipes using PyQt5 Packt video course. He is the author of Python GUI Programming Cookbook, First and Second Edition. This book is also available as a Packt video course. He is also the author of the Python Projects Packt video course. In his professional career, he has developed advanced in-house testing frameworks written in Python 3. He has also created advanced test automation GUIs in Python, which highly increased the productivity of the software development testing team. When not dreaming in Python code, he reads programming books about design, likes to go for walks, and reads classical poetry.
Overview
Section 1: Begin Python Programming in 7 Days
Lecture 1 The Course Overview
Lecture 2 Installing Python and Code Editor
Lecture 3 Getting Familiar with Command Line
Lecture 4 Writing and Running Your First Python Program
Lecture 5 Understanding Basic Syntax
Lecture 6 Assignment Day 1
Lecture 7 Understanding Python Variables
Lecture 8 Using Variables in Code
Lecture 9 Understanding Python Operators
Lecture 10 Usage of Python Operators
Lecture 11 Assignment Day 2
Lecture 12 Introducing Control Statements
Lecture 13 Usage of Control Statements
Lecture 14 Understand Loops
Lecture 15 Use Loops in Your Python Code
Lecture 16 Assignment Day 3
Lecture 17 Introducing Python Lists
Lecture 18 List Operators, Functions, and Methods
Lecture 19 Introducing the Dictionary Data Type
Lecture 20 Dictionary Operators, Functions, and Methods
Lecture 21 Assignment Day 4
Lecture 22 Introducing Functions
Lecture 23 Usage of Functions in Your Code
Lecture 24 Understanding Scope of Variables
Lecture 25 Example Code for a Scope of Variable Demonstration
Lecture 26 Assignment Day 5
Lecture 27 Python Modules
Lecture 28 Using Third-Party Python Modules
Lecture 29 Compiling Python Files
Lecture 30 Using Python Packages
Lecture 31 Assignment Day 6
Lecture 32 Reading Text from a File
Lecture 33 Writing Text to a File
Lecture 34 Handling Exceptions
Lecture 35 Assignment Day 7
Section 2: Python By Example
Lecture 36 The Course Overview
Lecture 37 Dice Rolling Simulator
Lecture 38 Nesting Python Dictionaries
Lecture 39 Using Python Generators
Lecture 40 Iterating over
Lecture 41 Deck of Cards Game Using Textual Cards
Lecture 42 Deck of Cards Game Using Graphical Cards
Lecture 43 Correctly Sizing Image Files
Lecture 44 Playing the Game
Lecture 45 Laying the Foundation
Lecture 46 Blue Prints of Architectural Design
Lecture 47 Building Our First Building
Lecture 48 The Greatness of Software Applied
Lecture 49 Building a Graphical User Interface
Lecture 50 Adding Many Widgets
Lecture 51 Using Several Layered Notebooks
Lecture 52 Making Our GUI Pretty
Lecture 53 Searching Directories
Lecture 54 Replacing Words Within Modules
Lecture 55 Administration Tasks
Lecture 56 Making Life Easy with Automation
Section 3: Troubleshooting Python Application Development
Lecture 57 The Course Overview
Lecture 58 Measuring Time Between Two Lines of Code with timeit
Lecture 59 Figuring out Where Time Is Spent with the Profile Module
Lecture 60 More Precise Time Tracking with cProfile
Lecture 61 Looking at Memory Consumption with memory_profiler
Lecture 62 Reduce Execution Time and Memory Consumption with __slots__
Lecture 63 Use Tuples Instead of Lists When Your Data Does Not Change
Lecture 64 Save on Memory Consumption with Generators Instead of Lists
Lecture 65 When to Use Lists Instead of Generators
Lecture 66 Leveraging Itertools to Create Generator Pipelines
Lecture 67 The Problem with Using Lists to Perform Vector Calculations
Lecture 68 Using NumPy's Arrays for More Powerful Vector Representations
Lecture 69 Rewriting Our Problem with NumPy to Speed It up 40x
Lecture 70 Fast MapReduce with NumPy Broadcasting
Lecture 71 Optimize All Calculations in One Go with numexpr
Lecture 72 The Problem of Serially Executing Web Scraping Calls
Lecture 73 Simple Asynchronous Programming with coroutines and gevent
Lecture 74 Event-Driven Concurrency with Tornado
Lecture 75 Concurrency and Futures with asyncio
Lecture 76 Getting Started with Parallel Programming
Lecture 77 Doubling the Speed of Your List Processing with Tuples
Lecture 78 Easily Speed up a Group of Processes with Pool
Lecture 79 Stop Processes from Interfering with Each Other with Locks
Lecture 80 Logging What Happens When You Have Many Processes
Lecture 81 Stop Modifying the Wrong Object Instance with Correct Object Cloning
Lecture 82 Speed Up Your OOP with namedtuples
Lecture 83 Reduce Getters and Setters with Static Methods and Properties
Lecture 84 Comparing Two Different Objects
Lecture 85 Improve Readability with Abstract Base Classes in Python
This course is for Python developers, who would like to learn the Python programming language in a hands-on way & tackle application performance problems to speed up your apps.


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