Hands On Data Structure & Algorithm with Python: 2 in 1
Hands On Data Structure & Algorithm with Python: 2 in 1, available at $39.99, has an average rating of 3.56, with 56 lectures, 2 quizzes, based on 9 reviews, and has 76 subscribers.
You will learn about Set up your own development environment on Windows to create Python applications Understand concepts such as divide and conquer and greedy and recursion algorithms in Python Master dynamic programming and asymptotic analysis in Python for coding Implement Stacks, queues/deques, hash tables, various algorithm such as BFS, DFS, Dijkstra's & and DAG Topological sorting Use special Python techniques such as decorators and context managers This course is ideal for individuals who are This course is for experience programmers who would like to transit into Python development while gaining hand-on practical skills in using data structures and algorithms with Python It is particularly useful for This course is for experience programmers who would like to transit into Python development while gaining hand-on practical skills in using data structures and algorithms with Python.
Enroll now: Hands On Data Structure & Algorithm with Python: 2 in 1
Summary
Title: Hands On Data Structure & Algorithm with Python: 2 in 1
Price: $39.99
Average Rating: 3.56
Number of Lectures: 56
Number of Quizzes: 2
Number of Published Lectures: 56
Number of Published Quizzes: 2
Number of Curriculum Items: 58
Number of Published Curriculum Objects: 58
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Set up your own development environment on Windows to create Python applications
- Understand concepts such as divide and conquer and greedy and recursion algorithms in Python
- Master dynamic programming and asymptotic analysis in Python for coding
- Implement Stacks, queues/deques, hash tables, various algorithm such as BFS, DFS, Dijkstra's & and DAG Topological sorting
- Use special Python techniques such as decorators and context managers
Who Should Attend
- This course is for experience programmers who would like to transit into Python development while gaining hand-on practical skills in using data structures and algorithms with Python
Target Audiences
- This course is for experience programmers who would like to transit into Python development while gaining hand-on practical skills in using data structures and algorithms with Python
Are you looking forward to get well versed with Python that is designed to ground you up from zero to hero in the shortest time? Then this is the perfect course for you.
This course can be of utmost important to you as it guides you in many different ways such as learning basics of data structures, linked lists, and arrays along with coding tuples in Python followed by an example that shows how to program dicts and sets in Python. You will also be shown shown how to apply different algorithms such as Graph traversal, Shortest Path, Minimum Spanning Tree, Maximum Flow tree, and DAG topological sorting. It aslo demonstration on how to realize a hash table in Python.
By end of this Learning Path, you’ll be well versed with Implementing Classic Data Structures and Algorithms Using Python along with building your own CV.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Learn Python in 3 Hours illustrates how u can be up-to-speed with Python in a short period of time, but your search has so far come up with disconnected, unrelated tutorials or guides.
Learn Python in 3 hours is a fast-paced, action-packed course that maximizes your time; it’s designed from the ground up to bring you from zero to hero in the shortest time. The course is based on many years of Python development experience in both large enterprises and nimble startups. In particular, the course’s hands-on and practical approach comes from the author’s experience in rapidly iterating and shipping products in a startup setting, where responsiveness and speed are key.
With Learn Python in 3 hours, you will be up-and-running with Python like you are with your other languages, proving your value and expertise to your team today, and building your CV and skill set for tomorrow.
The second course, Python Data Structures and Algorithmsis about data structures and algorithms. We are going to implement problems in Python. You will start by learning the basics of data structures, linked lists, and arrays in Python. You will be shown how to code tuples in Python followed by an example that shows how to program dicts and sets in Python. You will learn about the use of pointers in Python. You will then explore linear data structures in Python such as stacks, queues, and hash tables. In these you will learn how to implement a stack and code queues and deques. There will also be a demonstration on how to realize a hash table in Python. Following this you will learn how to use tree/graph data structures including binary trees, heaps and priority queues in Python. You will program priority queues and red-black trees in Python with examples. Finally, you will be shown how to apply different algorithms such as Graph traversal, Shortest Path, Minimum Spanning Tree, Maximum Flow tree, and DAG topological sorting
This course teaches all these concepts in a very practical hands-on approach without burdening you with lots of theory. By the end of the course, you will have learned how to implement various data structures and algorithms in Python.
About the Authors:
Rudy Lai is the founder of Quant Copy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, Quant Copy 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 generated content. Prior to founding Quant Copy, Rudy ran High Dimension.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 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 from which to learn a lot 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.
Harish Garg, founder of BignumWorks Software LLP is a data scientist and a lead software developer with 17 years’ software Industry experience. BignumWorks Software LLP is an India based Software Consultancy that provides consultancy services in the area of software development and technical training. Harish has worked for McAfeeIntel for 11+ years. He is an expert in creating Data visualizations using R, Python, and Web-based visualization libraries.
Mithun Lakshmanaswamy, part of BignumWorks Software LLP, has been developing Applications in Python for more than nine years. He has written enterprise level distributed applications that are deployed on scores of servers and have the ability to support thousands of users simultaneously. Some of the applications he has developed are related to parsing millions of virus definitions, analyzing network packets from an enterprise setup, etc. He is also quite proficient in the teaching technical concepts and is quite involved with his current org’s training programmes. He has worked on multiple projects working with Python, AWS etc implementing the concepts of concurrent and distributed computing.
Course Curriculum
Chapter 1: Learn Python in 3 Hours
Lecture 1: The Course Overview
Lecture 2: Introducing Your One-Stop-Shop Python IDE – WinPython
Lecture 3: Writing Your First Hello World! Program in Python
Lecture 4: Using Functions, Lambdas, and List Comprehensions
Lecture 5: Downloading pip So That You Can Install New Packages
Lecture 6: Structuring Your Python Application with Classes and Modules
Lecture 7: Installing and Using pipenv to Manage Your Projects
Lecture 8: Object-Oriented Programming, the Pythonic Way
Lecture 9: Help Your Functions Do More Using Decorators
Lecture 10: Wrap Up All Dynamic Resources with Context Managers
Lecture 11: Create Your Own Crawlers with Scrapy
Lecture 12: Go Through News Articles with newspaper3k
Lecture 13: Digest RSS Feeds Using Feedparser
Lecture 14: Handle Your Big Datasets with NumPy and pandas
Lecture 15: Make Python Smarter with Machine Learning Using scikit-learn
Lecture 16: Visualizing Data in Charts and Graphs with matplotlib
Lecture 17: Generate a Static Website with Markdown and Pelican
Lecture 18: Customizing Your Static Website with Jinja2 Templates
Lecture 19: Deploying Your First Web Server with Flask
Chapter 2: Python Data Structures and Algorithms
Lecture 1: The Course Overview
Lecture 2: Introduction to Divide/Conquer
Lecture 3: Starting with Greedy
Lecture 4: Begin with Recursion
Lecture 5: Working with Dynamic Programming
Lecture 6: Using Asymptotic Analysis
Lecture 7: Examples of Linked Lists/Arrays in Python
Lecture 8: Coding Tuples, in Python Through Examples
Lecture 9: Programming Dicts in Python Through Examples
Lecture 10: Implementing Sets in Python
Lecture 11: Use of Pointers in Python Through Examples
Lecture 12: Examples on Stacks in Python
Lecture 13: Implementing a Stack in Python
Lecture 14: Coding for Queues in Python
Lecture 15: Utilizing a Deque in Python
Lecture 16: Realize a Hash Table in Python
Lecture 17: Basic Python Coding for Trees
Lecture 18: Implementing Binary Trees in Python Through Examples
Lecture 19: Examples of Heaps Queues in Python
Lecture 20: Programming Priority Queues in Python
Lecture 21: Coding Red-Black Trees in Python with Examples
Lecture 22: Working with Tries (or Search Trees) with Examples
Lecture 23: Python Coding for Graphs
Lecture 24: Directed Graphs
Lecture 25: Undirected Graphs
Lecture 26: Add Neighbor Function in Vertex Class
Lecture 27: Get Connections Function in Vertex Class
Lecture 28: Get Weight Function in Vertex Class
Lecture 29: Other Useful Graph Methods
Lecture 30: Breadth-First Graph Traversal Algorithm
Lecture 31: Depth-First Graph Traversal Algorithm
Lecture 32: Shortest Path Algorithm
Lecture 33: Implementing Shortest Path Through Dijkstra’s Algorithm
Lecture 34: Minimum Spanning Tree Algorithm
Lecture 35: Implementing Minimum Spanning Tree Through Kruskal’s Algorithm
Lecture 36: Coding Maximum Flow Tree Algorithm in Python
Lecture 37: Example on Programming Dag Topological Sorting
Instructors
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Packt Publishing
Tech Knowledge in Motion
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 3 votes
- 4 stars: 1 votes
- 5 stars: 3 votes
Frequently Asked Questions
How long do I have access to the course materials?
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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