Python Programming Multithreading, OOP, NumPy and Pandas

Python Programming – Multithreading, OOP, NumPy and Pandas, available at $79.99, has an average rating of 4.57, with 197 lectures, 18 quizzes, based on 494 reviews, and has 14059 subscribers.

You will learn about Get a fundamental understanding of the Python programming language. Acquire the background and skills of Python to apply for Python programming jobs Understand the memory management of Python Understand NumPy and Pandas Understand Matplotlib Get a good grasp on multithreading, concurrent programming and parallel programming Can move to more advanced topics such as algorithms or machine learning Understand databases and database management with Python This course is ideal for individuals who are Students or beginners with no previous programming experience looking to obtain the skills to get their first programming job or If you are an expert Python programmer with decades of programming experience, then this course is not for you It is particularly useful for Students or beginners with no previous programming experience looking to obtain the skills to get their first programming job or If you are an expert Python programmer with decades of programming experience, then this course is not for you.

Enroll now: Python Programming – Multithreading, OOP, NumPy and Pandas

Summary

Title: Python Programming – Multithreading, OOP, NumPy and Pandas

Price: $79.99

Average Rating: 4.57

Number of Lectures: 197

Number of Quizzes: 18

Number of Published Lectures: 196

Number of Published Quizzes: 15

Number of Curriculum Items: 215

Number of Published Curriculum Objects: 211

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Get a fundamental understanding of the Python programming language.

Acquire the background and skills of Python to apply for Python programming jobs

Understand the memory management of Python

Understand NumPy and Pandas

Understand Matplotlib

Get a good grasp on multithreading, concurrent programming and parallel programming

Can move to more advanced topics such as algorithms or machine learning

Understand databases and database management with Python

Who Should Attend

Students or beginners with no previous programming experience looking to obtain the skills to get their first programming job

If you are an expert Python programmer with decades of programming experience, then this course is not for you

Target Audiences

Students or beginners with no previous programming experience looking to obtain the skills to get their first programming job

If you are an expert Python programmer with decades of programming experience, then this course is not for you

Join us and become a Python Programmer, learn one of most requested skills of 2022!

This course is about the fundamental basics of Python programming language. Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! You can learn about the hardest topics in programming: memory management, multithreading and object-oriented programming. So these are the topics you will learn about:

1.) Basics of Python

installing Python and the integrated development environment (IDE)

basic operations

conditional statements

loops

2.) Functions

what are functions in Python

positional and keyword arguments

return and yield

recursion

3.) Data Structures

how to measure the performance of data structures?

data structures introduction

lists

tuples

dictionaries and sets

4.) Object-Oriented Programing (OOP)

what is the advantages and disadvantages of OOP?

classes and objects

constructors

inheritance

polymorphism

5.) Memory Management

stack memory and heap memory

memory management in Python

6.) Handling Files (I/O)

read files and write files

7.) Exceptions

exceptions and errors

how to deal with exception

try-except-finally blocks

8.) Multithreading and Concurrent Programming

what are threads and processes?

synchronization

locks

deadlocks and livelocks

inter-thread communication

9.) Parallel Programming

multithreading and parallel programming

what is the Global Interpreter Lock (GIL)?

10.) Lambda Expressions

what is functional programming?

why to learn lambda expressions?

anonymous functions

filter

map

reduce

11.) NumPy

real array data structures in Python

lists and arrays comparison

NumPy fundamentals

12.) Matplotlib

how to create plots in Python

charts, line charts and scatter plots

13.) Pandas

why do we need Pandas in data sciences?

Series

DataFrames

apply function (in comparison with loops)

vectorization

14.) Database Management in Python

what are databases and why do we need them?

MySQL and SQL

SQL statements in Python

You will get lifetime access to 110+ lectures plus slides and source codes for the lectures!

This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back.

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Why to Learn Python Programming Language?

Lecture 1: Why to learn Python?

Chapter 3: Environment Setup

Lecture 1: Installing Python

Lecture 2: Installing PyCharm

Chapter 4: ### PYTHON FUNDAMENTALS ###

Lecture 1: Python fundamentals

Chapter 5: Python Basics

Lecture 1: First steps in Python

Lecture 2: What are the basic data types?

Lecture 3: Booleans

Lecture 4: Strings

Lecture 5: String slicing

Lecture 6: Type casting

Lecture 7: Operators

Lecture 8: Conditional statements

Lecture 9: How to use multiple conditions?

Lecture 10: Exercise – conditional statements

Lecture 11: Solution – conditional statements

Lecture 12: Logical operators

Lecture 13: Loops – for loop

Lecture 14: Loops – while loop

Lecture 15: Exercise: calculating the average

Lecture 16: Solution: calculating the average

Lecture 17: What are nested loops?

Lecture 18: Enumerate

Lecture 19: Break and continue

Lecture 20: Calculating Fibonacci-numbers

Lecture 21: Exercise: Fibonacci-numbers

Lecture 22: Solution: Fibonacci-numbers

Chapter 6: Functions in Python

Lecture 1: What are functions?

Lecture 2: Defining functions

Lecture 3: Positional arguments and keyword arguments

Lecture 4: Returning values

Lecture 5: Returning multiple values

Lecture 6: Solution – functions

Lecture 7: Yield operator

Lecture 8: Local and global variables

Lecture 9: What are the most relevant built-in functions?

Lecture 10: What is recursion?

Lecture 11: Solution – recursion

Lecture 12: Local vs global variables

Lecture 13: The __main__ function

Chapter 7: Data Structures in Python

Lecture 1: How to measure the running time of algorithms?

Lecture 2: Data structures introduction

Lecture 3: What are array data structures?

Lecture 4: What are lists in Python?

Lecture 5: Arrays introduction – operations

Lecture 6: Lists in Python

Lecture 7: Lists in Python – advanced operations

Lecture 8: Lists in Python – list comprehension

Lecture 9: (!!!) Python lists and arrays

Lecture 10: Exercise: list comprehension

Lecture 11: Solution – list comprehension

Lecture 12: Measuring running time of lists

Lecture 13: What are tuples?

Lecture 14: Mutability and immutability

Lecture 15: What are linked list data structures?

Lecture 16: Doubly linked list implementation in Python

Lecture 17: Hashing and O(1) running time complexity

Lecture 18: Dictionaries in Python

Lecture 19: Sets in Python

Lecture 20: Exercise: constructing dictionaries

Lecture 21: Solution: constructing dictionaries

Lecture 22: Sorting

Chapter 8: Object Oriented Programming (OOP)

Lecture 1: What is object oriented programming (OOP)?

Lecture 2: Class and objects basics

Lecture 3: Using the constructor

Lecture 4: Class variables and instance variables

Lecture 5: Exercise: constructing classes

Lecture 6: Solution: constructing classes

Lecture 7: Private variables and name mangling

Lecture 8: What is inheritance in OOP?

Lecture 9: The super keyword

Lecture 10: Function (method) override

Lecture 11: What is polymorphism?

Lecture 12: Polymorphism and abstraction example

Lecture 13: Exercise: abstraction

Lecture 14: Solution: abstraction

Lecture 15: Modules

Lecture 16: The __str__ function

Lecture 17: Comparing objects – overriding functions

Chapter 9: Memory Management

Lecture 1: What are stack and heap memory?

Lecture 2: Stack memory and heap memory simulation

Lecture 3: Garbage collection and reference counting

Lecture 4: Revisiting the types of variables

Lecture 5: The == and the is operators

Lecture 6: Call by reference and value

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 39 votes

4 stars: 188 votes

5 stars: 260 votes

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