Programming Effectively in Python
Programming Effectively in Python, available at $49.99, has an average rating of 4.2, with 82 lectures, 3 quizzes, based on 30 reviews, and has 369 subscribers.
You will learn about Practice refactoring methods and get to grips with real-world scenarios Refactor classes and objects by making them easier to understand, maintain, and more efficient Implementing pattern-based refactoring Make major progress by using third-party refactoring tools to speed up your refactoring work Learn to use dictionaries in a smarter way to keep track of your application's state. Save time writing custom subclasses by learning new data structures built right into Python. Evolve into a seasoned Python developer with top Pythonic tips Locate root causes by benchmarking and profiling your application Make your apps run faster with parallel programming Organize your code better using Object Oriented Programming This course is ideal for individuals who are This course is targeted at Python developers, software architects and senior software engineers, who use Python for their everyday work and build their applications and projects using Python ● . It is particularly useful for This course is targeted at Python developers, software architects and senior software engineers, who use Python for their everyday work and build their applications and projects using Python ● .
Enroll now: Programming Effectively in Python
Summary
Title: Programming Effectively in Python
Price: $49.99
Average Rating: 4.2
Number of Lectures: 82
Number of Quizzes: 3
Number of Published Lectures: 82
Number of Published Quizzes: 3
Number of Curriculum Items: 85
Number of Published Curriculum Objects: 85
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Practice refactoring methods and get to grips with real-world scenarios
- Refactor classes and objects by making them easier to understand, maintain, and more efficient
- Implementing pattern-based refactoring
- Make major progress by using third-party refactoring tools to speed up your refactoring work
- Learn to use dictionaries in a smarter way to keep track of your application's state.
- Save time writing custom subclasses by learning new data structures built right into Python.
- Evolve into a seasoned Python developer with top Pythonic tips
- Locate root causes by benchmarking and profiling your application
- Make your apps run faster with parallel programming
- Organize your code better using Object Oriented Programming
Who Should Attend
- This course is targeted at Python developers, software architects and senior software engineers, who use Python for their everyday work and build their applications and projects using Python ● .
Target Audiences
- This course is targeted at Python developers, software architects and senior software engineers, who use Python for their everyday work and build their applications and projects using Python ● .
Python is an easy to learn, powerful programming language. If you’re a developer who wishes to build a strong programming foundation with this simple yet powerful programming language Python, then this course is for you.
This learning path is your step-by-step guide to exploring the possibilities in the field of Go. With this course, you’ll start with understanding the principles of refactoring, & spot opportunities by identifying code that requires refactoring. Also, you will be shown how to remove Python anti-patterns from your programs in simple steps. Next, you will learn how you can increase the speed & performance of your code with quick tips, tricks, and techniques for loops, data structures, object-oriented programming, functions, and more. Finally, after all this, its time to troubleshoot Python Application Development Quickly detect which lines of code are causing problems, and fix them quickly without going through lakhs of pages.
Contents and Overview
This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Refactoring Python Codestarts with teaching you to resolve Python anti-patterns with techniques and methods to improve the design of your existing code. Tackle bugs by understanding the principles of refactoring, and learn to spot opportunities by identifying code that requires refactoring. We will also show you how to build test-driven processes to make refactoring easier. This course will show you how to remove Python anti-patterns from your programs in simple steps. We cover specific techniques for refactoring and improving the sloppy Python code. Take this course if you want to have a legacy Python code base with a lot of issues. Apply real-world refactoring techniques, and turn your code into clean, efficient, and maintainable projects.
The second course, Python Tips, Tricks, and Techniqueswill take you from a Python outsider to an insider. You will benefit from insights from the Python documentation, PEPs, and online developer communities to learn the ultimate Pythonic ways to tackle common programming patterns. This course covers tips, tricks, and techniques for loops, data structures, object-oriented programming, functions, and more, helping you work on ordered collections and key-value stores for dictionaries. You will be able to increase the speed and performance of your code while making it easier to debug. Start writing cleaner code for your applications and learn to organize it better in just 3 hours. No other course can transform every corner of your Python code. Take this course NOW and become an overnight Python rockstar developer.
The third course, Troubleshooting Python Application Developmenttakes 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:
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James Crossis a Big Data Engineer and certified AWS Solutions Architect with a passion for data-driven applications. He’s spent the last 3-5 years helping his clients to design and implement huge scale streaming Big Data platforms, Cloud-based analytics stacks, and serverless architectures. He started his professional career in Investment Banking, working with well-established technologies such as Java and SQL Server, before moving into the big data space. Since then he’s worked with a huge range of big data tools including most of the Hadoop eco-system, Spark and many No-SQL technologies such as Cassandra, MongoDB, Redis, and DynamoDB. More recently his focus has been on Cloud technologies and how they can be applied to data analytics, culminating in his work at Scout Solutions as CTO, and more recently with Mckinsey. James is an AWS-certified solutions architect with several years’ experience designing and implementing solutions on this cloud platform. As CTO of Scout Solutions Ltd, he built a fully serverless set of APIs and an analytics stack based around Lambda and Redshift. He is interested in almost anything that has to do with technology. He has worked with everything from WordPress to Hadoop, from C++ to Java, and from Oracle to DynamoDB. If it’s new and solves a problem in an innovative way he’s keen to give it a go!
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Colibri Ltdis 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.
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Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. After 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 feedback into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.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 from which 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.
Course Curriculum
Chapter 1: Refactoring Python Code
Lecture 1: The Course Overview
Lecture 2: Benefits of Refactoring
Lecture 3: Understanding Refactoring Principles
Lecture 4: Overview of Refactoring Tools
Lecture 5: Overview of Python Anti-Patterns
Lecture 6: Various Types of Python Anti-Patterns
Lecture 7: Playbooks for Removing Anti-Patterns
Lecture 8: Refactoring Through Splitting Up Functions
Lecture 9: Refactoring Through Merging Functions
Lecture 10: Replace Complex Expressions with Inner Function Calls
Lecture 11: Refactoring Through Local Variable Creation
Lecture 12: Assessing the Correct Class for Fields and Methods
Lecture 13: Moving Functions Around Different Classes to Group Functionality
Lecture 14: Refactor Delegate Classes to Remove Double Dependencies
Lecture 15: Removing Middlemen Classes to Reduce Needless Complexity
Lecture 16: Introduction to Pattern Based Refactoring
Lecture 17: Using Encapsulation Methods for Refactoring
Lecture 18: Removing Multiple Checks for None in Your Python Code
Lecture 19: Python Refactoring Using Conditionals
Lecture 20: Using Rope, a Python Refactoring Library
Lecture 21: Using Codemods to Do Python Refactoring
Lecture 22: Other Tools Available for Refactoring
Chapter 2: Python Tips, Tricks and Techniques
Lecture 1: The Course Overview
Lecture 2: Using List Comprehensions to Shorten for Loops
Lecture 3: Inserting if Statements Using Conditional List Comprehensions
Lecture 4: Operating on Lists of Lists with Nested List Comprehensions
Lecture 5: Simplify Your Collapsing Nested Lists
Lecture 6: List Slicing Tricks
Lecture 7: Never Get a KeyError Again with the defaultdict Data Structure
Lecture 8: Collapsing Nested Dictionaries to Simplify Your Code
Lecture 9: Mini Switch-case Statements with Dictionaries
Lecture 10: Merging Two Dictionaries with Just One line of Code
Lecture 11: Creating a Dictionary with List Comprehension in Python
Lecture 12: Counting Occurrences of Items Quickly with Counter
Lecture 13: Creating Stacks or Queues with Deque Objects
Lecture 14: Grouping Related Values with Tuples and Sequences
Lecture 15: Gathering Unique Sets of Values with Sets and Frozensets
Lecture 16: Powering Up Your Dictionaries with Ordered Dictionaries
Lecture 17: New Ways Of Calling Functions with Arguments and kwargs
Lecture 18: One Line Functions with Lambdas
Lecture 19: Segmenting Your Code with Functions within Functions
Lecture 20: Creating Dynamic Functions by Returning Functions
Lecture 21: Power Up Your Functions by Wrapping Them With Decorators
Lecture 22: Copying and Cloning Objects the Right Way
Lecture 23: Mini-Classes of Python- namedtuples
Lecture 24: Creating Smart Values with Static Methods and Properties
Lecture 25: Comparing Two Different Objects
Lecture 26: Do Real OOP by Implementing Abstract Base Classes in Python
Lecture 27: Enumerating the Indices of Your Loops with No Extra Lines
Lecture 28: Underscore Useless Variables to Make Your Code Easier to Look at
Lecture 29: Uncommon “for..else” Loop to End Your Iteration
Lecture 30: Pretty Printing Any Python Data Structure
Lecture 31: Managing Your Dynamic Resources Carefully with Context Managers
Chapter 3: Troubleshooting Python Application Development
Lecture 1: The Course Overview
Lecture 2: Measuring Time Between Two Lines of Code with timeit
Lecture 3: Figuring out Where Time Is Spent with the Profile Module
Lecture 4: More Precise Time Tracking with cProfile
Lecture 5: Looking at Memory Consumption with memory_profiler
Lecture 6: Reduce Execution Time and Memory Consumption with __slots__
Lecture 7: Use Tuples Instead of Lists When Your Data Does Not Change
Lecture 8: Save on Memory Consumption with Generators Instead of Lists
Lecture 9: When to Use Lists Instead of Generators
Lecture 10: Leveraging Itertools to Create Generator Pipelines
Lecture 11: The Problem with Using Lists to Perform Vector Calculations
Lecture 12: Using NumPy’s Arrays for More Powerful Vector Representations
Lecture 13: Rewriting Our Problem with NumPy to Speed It up 40x
Lecture 14: Fast MapReduce with NumPy Broadcasting
Lecture 15: Optimize All Calculations in One Go with numexpr
Lecture 16: The Problem of Serially Executing Web Scraping Calls
Lecture 17: Simple Asynchronous Programming with coroutines and gevent
Lecture 18: Event-Driven Concurrency with Tornado
Lecture 19: Concurrency and Futures with asyncio
Lecture 20: Getting Started with Parallel Programming
Lecture 21: Doubling the Speed of Your List Processing with Tuples
Lecture 22: Easily Speed up a Group of Processes with Pool
Lecture 23: Stop Processes from Interfering with Each Other with Locks
Lecture 24: Logging What Happens When You Have Many Processes
Lecture 25: Stop Modifying the Wrong Object Instance with Correct Object Cloning
Lecture 26: Speed Up Your OOP with namedtuples
Lecture 27: Reduce Getters and Setters with Static Methods and Properties
Lecture 28: Comparing Two Different Objects
Lecture 29: Improve Readability with Abstract Base Classes in Python
Instructors
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Packt Publishing
Tech Knowledge in Motion
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 1 votes
- 3 stars: 5 votes
- 4 stars: 10 votes
- 5 stars: 14 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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