Data Science: Python for Data Analysis Full Bootcamp

Data Science: Python for Data Analysis Full Bootcamp, available at $64.99, has an average rating of 4.29, with 76 lectures, 10 quizzes, based on 2075 reviews, and has 184015 subscribers.

You will learn about 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 This course is ideal for individuals who are Python beginners and newbies or Data Scientist who knows other language tools or New Python Data Analysts or Data Science Beginners or New developers and Programmers or Programmers and developers who know other programming language but are new to python or Anyone who wants to use Python for data analysis and visualization in a short time! It is particularly useful for Python beginners and newbies or Data Scientist who knows other language tools or New Python Data Analysts or Data Science Beginners or New developers and Programmers or Programmers and developers who know other programming language but are new to python or Anyone who wants to use Python for data analysis and visualization in a short time!.

Enroll now: Data Science: Python for Data Analysis Full Bootcamp

Summary

Title: Data Science: Python for Data Analysis Full Bootcamp

Price: $64.99

Average Rating: 4.29

Number of Lectures: 76

Number of Quizzes: 10

Number of Published Lectures: 76

Number of Published Quizzes: 10

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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!

Target Audiences

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!

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.

This course will cover:

Python tools for Data Analysis
Python Basics
Python Fundamentals
Python Object-Oriented
Advanced Python Foundations
Data Handling with Python
Numerical Python(NumPy)
Data Analysis with Pandas
Data Visualization with Matplotlib
Advanced Graphs with Seaborn
Instructor QA Support and Help

HD Video Training + Working Files + Resources + QA Support.

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!

Course Curriculum

Chapter 1: Mastering Python, Data Handling, Analysis and Visualization

Lecture 1: Welcome to Data Science: Python for Data Analysis 2022 Full Bootcamp

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

Chapter 2: The Basics of Python

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

Chapter 3: Python Data Structures

Lecture 1: Structure Data using lists

Lecture 2: Structure data using tuples

Lecture 3: Structure Data using Dictionaries

Lecture 4: Structure Data using sets

Chapter 4: The Fundamentals of Python

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

Chapter 5: OOP in Python

Lecture 1: Core Python OOP: Classes and Instances

Lecture 2: Core Python OOP: Exploring Inheritance

Chapter 6: Advanced Foundations

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

Chapter 7: Python Data Handling

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

Chapter 8: Numerical Python – NumPy

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

Chapter 9: Analyze Data with Pandas

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

Chapter 10: Visualize Data with Matplotlib

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

Chapter 11: Advanced Data graphs with Seaborn

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

Lecture 8: Seaborn statistical graphs level 8

Chapter 12: Resources

Lecture 1: Python Programming

Lecture 2: NumPy

Lecture 3: Pandas

Lecture 4: Matplotlib

Lecture 5: Seaborn

Chapter 13: BONUS SECTION

Lecture 1: Bonus

Instructors

Ahmed El Mohandes
Expert Software Engineer | Sr. Data Science & ML Consultant
SDE Arts by Ahmed EL Mohandes
Where Skills Soar and Careers Take Flight

Rating Distribution

1 stars: 36 votes
2 stars: 64 votes
3 stars: 332 votes
4 stars: 729 votes
5 stars: 917 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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