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  • Code with Python Programming Language
  • Python Functional Programming
  • Structure Data using collection containers
  • Object-Oriented Design
  • Advanced Python Foundations
  • Handling Data with Python Libraries
  • Numerical Python
  • Extracting and Analyzing data from different resources
  • Data Analysis with Pandas
  • Data Visualization using matplotlib
  • Advanced Visualization with Seaborn
  • Build Python solutions for data science
  • Get Instructor QA Support and help

Hello and welcome to Data Science: Python for Data Analysis Full Bootcamp.
 
Data science is a huge field and one of the promising fields that is spreading in a fast way. Also, it is one of the very rewarding, and it is increasing in expansion day by day, due to its great importance and benefits, as it is the future.
 
Data science enables companies to measure, track, and record performance metrics for facilitating and enhancing decision-making. Companies can analyze trends to make critical decisions to engage customers better, enhance company performance, and increase profitability.
 
And the employment of data science and its tools depends on the purpose you want from them.
 
For example, using data science in health care is very different from using data science in finance and accounting, and so on. And I’ll show you the core libraries for data handling, analysis and visualization which you can use in different areas.
 
One of the most powerful programming languages ​​that are used for Data science is Python, which is an easy, simple and very powerful language with many libraries and packages that facilitate working on complex and different types of data.
 
In this course, you will learn how to code in Python from the beginning and then you will master how to deal with the most famous libraries and tools of the Python language related to data science, starting from data collection, acquiring and analysis to visualize data with advanced techniques, and based on that, the necessary decisions are taken by companies.
 
I am Ahmed Ibrahim, a software engineer and Instructor and I have taught more than 500,000 engineers and developers around the world in topics related to programming languages ​​and their applications, and in this course, we will dive deeply into the core Python fundamentals, Advanced Foundations, Data handling libraries, Numerical Python, Pandas, Matplotlib and finally Seaborn.
 
I hope that you will join us in this course to master the Python language for data analysis and Visualization like professionals in this field.
 
We have a lot to cover in this course.
 
Let’s get started!

  • No Python prior experience is required to take this Training
  • Computer and Internet access
  • Python beginners and newbies
  • Data Scientist who knows other language tools
  • New Python Data Analysts
  • Data Science Beginners
  • New developers and Programmers
  • Programmers and developers who know other programming language but are new to python
  • Anyone who wants to use Python for data analysis and visualization in a short time!
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  • Section 1 : Course Introduction 5 Lectures 00:18:41

    • Lecture 1 :
    • Welcome to Data Science: Python for Data Analysis 2023 Full Bootcamp Preview
    • Lecture 2 :
    • Download and Install the working tools
    • Lecture 3 :
    • Jupyter Overview + Markdown in Jupyter tutorial
    • Lecture 4 :
    • Using Jupyter Notebook for coding with Python
    • Lecture 5 :
    • Using Anaconda Prompt
  • Section 2 : Python Basics 6 Lectures 00:53:05

    • Lecture 1 :
    • Variables and Types Tutorial
    • Lecture 2 :
    • Describe what's inside the code
    • Lecture 3 :
    • Define Blocks and Avoid IndentationError
    • Lecture 4 :
    • Strings full tutorial
    • Lecture 5 :
    • Numbers, Math and f-string tutorial
    • Lecture 6 :
    • Handling inputs and outputs
  • Section 3 : Python Data Structures 4 Lectures 00:46:58

    • Lecture 1 :
    • Structure Data using lists
    • Lecture 2 :
    • Structure data using tuples
    • Lecture 3 :
    • Structure Data using Dictionaries
    • Lecture 4 :
    • Structure Data using sets
  • Section 4 : Python Fundamentals 12 Lectures 01:14:49

    • Lecture 1 :
    • Comparing Values
    • Lecture 2 :
    • Output from Logics
    • Lecture 3 :
    • Conditional Statements
    • Lecture 4 :
    • The while loop in Python
    • Lecture 5 :
    • The for loop in Python
    • Lecture 6 :
    • Python Library Functions
    • Lecture 7 :
    • User-Defined Functions
    • Lecture 8 :
    • The lambda power
    • Lecture 9 :
    • The break statement
    • Lecture 10 :
    • The continue statement
    • Lecture 11 :
    • The for else statement
    • Lecture 12 :
    • Program to Put all together
  • Section 5 : OOP in Python 2 Lectures 00:22:11

    • Lecture 1 :
    • Core Python OOP: Classes and Instances
    • Lecture 2 :
    • Core Python OOP: Exploring Inheritance
  • Section 6 : Advanced Concepts 6 Lectures 00:32:42

    • Lecture 1 :
    • Concise Comprehensions
    • Lecture 2 :
    • Constructed modules and random
    • Lecture 3 :
    • Doing mathematics
    • Lecture 4 :
    • Doing statistics
    • Lecture 5 :
    • Errors Exploration
    • Lecture 6 :
    • Exceptions Playground
  • Section 7 : Python Data Handling 5 Lectures 00:26:00

    • Lecture 1 :
    • IO data in memory
    • Lecture 2 :
    • Interacting with operating system data
    • Lecture 3 :
    • Moving data files between directories
    • Lecture 4 :
    • Data will be in the trash bin
    • Lecture 5 :
    • Zipping and Unzipping Data
  • Section 8 : Numerical Python(NumPy) 9 Lectures 00:37:59

    • Lecture 1 :
    • NumPy Level 1
    • Lecture 2 :
    • NumPy Level 2
    • Lecture 3 :
    • NumPy Level 3
    • Lecture 4 :
    • NumPy Level 4
    • Lecture 5 :
    • NumPy Level 5
    • Lecture 6 :
    • NumPy Level 6
    • Lecture 7 :
    • NumPy Level 7
    • Lecture 8 :
    • NumPy Level 8
    • Lecture 9 :
    • NumPy Level 9
  • Section 9 : Data Analysis with Pandas 6 Lectures 00:21:41

    • Lecture 1 :
    • Pandas Data Analysis Level 1
    • Lecture 2 :
    • Pandas Data Analysis Level 2
    • Lecture 3 :
    • Pandas Data Analysis Level 3
    • Lecture 4 :
    • Pandas Data Analysis Level 4
    • Lecture 5 :
    • Pandas Data Analysis Level 5
    • Lecture 6 :
    • Pandas Data Analysis Level 6
  • Section 10 : Data Visualization with Matplotlib 7 Lectures 00:17:55

    • Lecture 1 :
    • Matplotlib Data Visualization Level 1
    • Lecture 2 :
    • Matplotlib Data Visualization Level 2
    • Lecture 3 :
    • Matplotlib Data Visualization Level 3
    • Lecture 4 :
    • Matplotlib Data Visualization Level 4
    • Lecture 5 :
    • Matplotlib Data Visualization Level 5
    • Lecture 6 :
    • Matplotlib Data Visualization Level 6
    • Lecture 7 :
    • Matplotlib Data Visualization Level 7
  • Section 11 : Advanced Graphics with Seaborn 7 Lectures 00:15:21

    • Lecture 1 :
    • Seaborn Statistical Graphs Level 1
    • Lecture 2 :
    • Seaborn Statistical Graphs Level 2
    • Lecture 3 :
    • Seaborn Statistical Graphs Level 3
    • Lecture 4 :
    • Seaborn Statistical Graphs Level 4
    • Lecture 5 :
    • Seaborn Statistical Graphs Level 5
    • Lecture 6 :
    • Seaborn Statistical Graphs Level 6
    • Lecture 7 :
    • Seaborn Statistical Graphs Level 7
  • Section 12 : Resources 5 Lectures 00:02:34

    • Lecture 1 :
    • Python Programming
    • Lecture 2 :
    • NumPy
    • Lecture 3 :
    • Pandas
    • Lecture 4 :
    • Matplotlib
    • Lecture 5 :
    • Seaborn
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Ahmed Ibrahim is A Software Development Engineer | Data Science Professional | Training Content Engineer/Instructor I taught more than 700,000 students, developers and engineers from more than 190 countries around the world. - Programming Languages: Python, R, JavaScript, Java and Go. - Data Science: Data Analysis and Visualization tools and Libraries with Python, R, SQL and Spark. - Databases: Relational and Non-Relational. - Applied experience with many programming languages and tools, also a proficient knowledge and experience in Software Engineering and Data Science with skills to analyze, design and develop. - Bachelor's degree in Electrical, Communications and Computer Engineering. I always have a passion to develop my work and I like to simplify and clarify Software and Data Science skills and tools and share my skills and expertise with others via high-quality, and direct-to-point video training courses.
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