Python Novice to Pythonista - Part 2 Python Apprentice

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duration: 40 hours |

Language: English (US) |

access duration: 180 days |

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Details

This is part 1 of the Python Novice to Pythonista learning path. Python continues to be one of the fastest growing programming languages on the market today. Because of its ease of use and many supporting frameworks, Python is widely used in web development, script writing, task automation, data science, and even cybersecurity. In this learning path, you will explore the different stages required to become a Pythonista.

In this part of the learning path, the focus is on Python classes and inheritance. In addition, you will explore data structures and algorithms.

You'll find several training courses that prepare you to become a Python Apprentice. There is also a livelab available for practice. You finish this part with an exam.

Result

After completing this part of the learning path you will be familiar with Python classes, inheritance, data structures and algorithms. You are a true Python Apprentice.

In addition, you are ready to start part 3 of this learning path.

Prerequisites

You are familiar with the basics of software development.

You have completed part 2 of this learning path.

Target audience

Software Developer, Web Developer

Content

Python Novice to Pythonista - Part 2 Python Apprentice

40 hours

Advanced Python Topics: File Operations in Python

  • Python includes functions and modules that simplify working with

  • files. In this 11-video course, explore operations for standard
  • text files and handling of formatted data such as JSON and CSV by
  • using Python modules designed for such data. Learners begin with a
  • look at some of the file operations in Python. Use Python's open
  • function to open a file for reading, and differentiate between the
  • read(), readline(), and readlines() functions. View differences
  • between opening a file in write mode and append mode in Python.
  • This leads on to distinguishing between the r+ and a+ modes to read
  • from and write to a file; using load and loads functions of the
  • json module to parse JSON data, and converting Python dictionaries
  • and lists into JSON strings and files. Next, learn how to identify
  • file formats that can be handled by the CSV module in Python, and
  • convert Python dictionaries and lists into CSV files. The final
  • tutorial concerns defining a customized file format, creating a CSV
  • dialect, and using that to parse and write data.

Advanced Python Topics: Exceptions & Command Line Arguments

  • In this 11-video course, learners can examine different types of

  • exceptions that can be raised in Python and how they can be handled
  • with except blocks. Explore execution of Python scripts from the
  • command line, including use and parsing of command-line arguments.
  • To begin, use a try and except block to handle a Python exception,
  • then learn how to handle exceptions that your code may raise.
  • Explore how exceptions are defined in a hierarchy and how related
  • exceptions are caught and handled. Next, define multiple except
  • blocks to handle various exceptions that can be raised by code;
  • convert code prototyped in a Jupyter notebook into a Python script
  • that can be executed from a shell, and run code from the Python
  • shell and execute single-line and multi-line commands. Learn about
  • parsing command line arguments, and use the sys and argparse module
  • to access command-line arguments to a Python script, then parse and
  • use the arguments passed to a Python script from the command line.
  • Finally, learn to define command-line arguments by using
  • argparse.

Advanced Python Topics: Python Modules & Virtual Environments

Advanced Python Topics: Python Modules & Virtual Environments

Advanced Python Topics: Migrating from Python 2 to Python 3

Advanced Python Topics: Migrating from Python 2 to Python 3

Python Classes and Inheritance: Introduction

Python Classes and Inheritance: Introduction

Python Classes & Inheritance: Getting Started with Classes in Python

Python Classes & Inheritance: Getting Started with Classes in Python

Python Classes & Inheritance: Working with Inheritance in Python

Python Classes & Inheritance: Working with Inheritance in Python

Python Classes & Inheritance: Advanced Functionality Using Python Classes

Python Classes & Inheritance: Advanced Functionality Using Python Classes

Data Structures & Algorithms in Python: Fundamental Data Structures

Explore Python data structures and delve into the details of some of the basic data structures, such as linked lists, stacks, and queues.

Data Structures & Algorithms in Python: Implementing Data Structures

Begin by examining operations that have different values of time complexity. Then delve into the implementation of some of the basic data structures, such as linked lists, stacks, and queues in Python.

Data Structures & Algorithms in Python: Sorting Algorithms

Explore the workings of some of the most widely used sorting algorithms in Python and examine how their performance affects various measures.

Data Structures & Algorithms in Python: Implementing Sorting Algorithms

Examine the Python implementation of common sorting algorithms such as Selection Sort, Bubble Sort, and Insertion Sort, as well as divide and conquer sorts such as Shell Sort, Merge Sort, and Quicksort.

Data Structures & Algorithms in Python: Trees & Graphs

Examine the theory around graph and tree data structures in Python. Explore binary search trees, different graph representations, and traversal operations on these data structures.

Data Structures & Algorithms in Python: Implementing Trees & Graphs

Discover how to implement trees and graphs and examine with the associated functions used to build and maintain these data structures in Python. In addition, explore algorithms for data structure traversal.

Python Apprentice

  • Perform apprentice level Python development tasks such as file handling, implementing polymorphism, implementing special method names, as well as implementing an abstract class and using static methods. Then, test your skills by answering assessment questions after using a Python list as a stack, performing queue operations, implementing a graph as an adjacency matrix, and traversing a Binary Search Tree (BST).
  • This lab provides access to tools typically used when developing with Python, including:
  • � Python
  • � Anaconda
  • � Jupyter Notebook + JupyterHub
  • � Pandas, NumPy, SiPy
  • � Seaborn Library
  • � PyCharm IDE
  • � Spyder IDE
  • � MongoDB
  • � MySQL
  • � VS Code
  • This lab is part of the Python Apprentice track of the Skillsoft Aspire Python Apprentice to Pythonista journey.

Final Exam: Python Novice

  • Final Exam: Python Novice will test your knowledge and
  • application of the topics presented throughout the Python Novice
  • track of the Skillsoft Aspire Python Novice to Pythonista Journey.
  • Learners seeking to test their knowledge and application of Python
  • Novice skills and those seeking to complete the Python Novice track
  • of the Skillsoft Aspire Python Novice to Pythonista Journey.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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