Python for Data Analysis & Visualization
Python for Data Analysis & Visualization, available at $89.99, has an average rating of 4.54, with 111 lectures, 18 quizzes, based on 1610 reviews, and has 34500 subscribers.
You will learn about Python, we will be using Python3 in this course Data Analysis Libraries in Python such as NumPy and Pandas Data Visualization Libraries in Python such as Matplotlib and Seaborn How to analyse data Data Visualization Jupyter Notebooks IDE / Anaconda Distribution This course is ideal for individuals who are Python developers curious about the data analysis libraries or Python developers curious about the data visualization libraries or Anyone interested in learning Python or Data Analysts or Anyone working with data It is particularly useful for Python developers curious about the data analysis libraries or Python developers curious about the data visualization libraries or Anyone interested in learning Python or Data Analysts or Anyone working with data.
Enroll now: Python for Data Analysis & Visualization
Summary
Title: Python for Data Analysis & Visualization
Price: $89.99
Average Rating: 4.54
Number of Lectures: 111
Number of Quizzes: 18
Number of Published Lectures: 111
Number of Published Quizzes: 18
Number of Curriculum Items: 129
Number of Published Curriculum Objects: 129
Number of Practice Tests: 1
Number of Published Practice Tests: 1
Original Price: £99.99
Quality Status: approved
Status: Live
What You Will Learn
- Python, we will be using Python3 in this course
- Data Analysis Libraries in Python such as NumPy and Pandas
- Data Visualization Libraries in Python such as Matplotlib and Seaborn
- How to analyse data
- Data Visualization
- Jupyter Notebooks IDE / Anaconda Distribution
Who Should Attend
- Python developers curious about the data analysis libraries
- Python developers curious about the data visualization libraries
- Anyone interested in learning Python
- Data Analysts
- Anyone working with data
Target Audiences
- Python developers curious about the data analysis libraries
- Python developers curious about the data visualization libraries
- Anyone interested in learning Python
- Data Analysts
- Anyone working with data
Learn one of the most in demand programming languages in the world and master the most important libraries when it comes to analysing and visualizing data.
This course can be split into 3 key areas:
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The first area of the course focuses on core Python3and teaches you the essentials you need to be able to master the libraries taught in this course
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The second area focuses on analysing and manipulating data. You will learn how to master both NumPy and Pandas
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For the final part of the course you learn how to display our data in the form of interesting charts using Matplotlib, Seabornand Plotly Express
You will be using Jupyter Notebooks as part of the Anaconda Distribution.Jupyter is the most popular Python IDE available.
The course is packed with lectures, code-along videos, coding exercises and quizzes.
On top of that there are numerous dedicated challenge sections that utilize interesting datasets to enable you to make the most out of these external libraries.
There should be more than enough to keep you engaged and learning! As an added bonus you will also have lifetime access to all the lectures as well as lots of downloadable course resources consisting of detailed Notebooks.
The aim of this course is to make you proficient at using Python and the data analysis and visualization libraries.
This course is suitable for students of all levels and it doesn’t matter what operating system you use.
Curriculum summary:
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Set Up & Installation
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Core Python
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Python Objects, Variables and Data Types
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Control Flow and Loops
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Functions
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External Libraries
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Data Analysis Libraries
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NumPy
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Pandas
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Connecting to different Data Sources
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Visualization Libraries
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Matplotlib
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Seaborn
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Plotly Express
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4 dedicated Challenge Sections!
Course Curriculum
Chapter 1: Course Welcome & Set Up
Lecture 1: Course Overview
Lecture 2: Udemy 101
Lecture 3: Python Overview
Lecture 4: Anaconda Distribution Installation
Lecture 5: Jupyter Notebook 101
Lecture 6: Jupyter Notebook – Adding Comments in Cells
Lecture 7: Course Resources – Important!
Chapter 2: Objects, Variables and Data Types
Lecture 1: Objects and Variables Overview
Lecture 2: Numbers
Lecture 3: Coding Exercise Solution
Lecture 4: Coding Exercise Solution
Lecture 5: Strings
Lecture 6: Coding Exercise Solution
Lecture 7: String Operations
Lecture 8: String Methods and Properties
Lecture 9: Coding Exercise Solution
Lecture 10: String Concatenation and Formatting
Lecture 11: Lists
Lecture 12: Coding Exercise Solution
Lecture 13: Coding Exercise Solution
Lecture 14: Dictionaries
Lecture 15: Coding Exercise Solution
Lecture 16: Tuples and Sets
Lecture 17: Coding Exercise Solution
Lecture 18: Booleans
Lecture 19: Key Words in Python
Chapter 3: Control Flow and Loops
Lecture 1: Python Operators
Lecture 2: Control Flow
Lecture 3: Coding Exercise Solution
Lecture 4: For Loops
Lecture 5: For Loops (continued)
Lecture 6: Coding Exercise Solution
Lecture 7: Coding Exercise Solution
Lecture 8: While Loops
Lecture 9: Break, Continue and Pass Statements
Lecture 10: List Comprehension
Lecture 11: Coding Exercise Solution
Lecture 12: IN and NOT IN
Chapter 4: Functions
Lecture 1: Built-In Functions
Lecture 2: Coding Exercise Solution
Lecture 3: User Defined Functions
Lecture 4: User Defined Functions – Examples
Lecture 5: Coding Exercise Solution
Lecture 6: Coding Exercise Solution
Lecture 7: Arguments and Keyword Arguments
Lecture 8: Map and Filter
Lecture 9: Lambda Functions
Lecture 10: Coding Exercise Solution
Lecture 11: Errors and Exception Handling
Chapter 5: Challenge Section – Core Python
Lecture 1: Challenge Questions Overview
Lecture 2: Solutions Walkthrough
Lecture 3: Corection: Solutions
Chapter 6: Modules, Packages and Libraries
Lecture 1: Built-In Modules
Lecture 2: External Libraries
Chapter 7: NumPy
Lecture 1: NumPy Overview
Lecture 2: Array Slicing and Indexing
Lecture 3: Array Manipulation Functions
Lecture 4: Additional Array Creation Functions
Lecture 5: Array Arithmetic and Mathematical Functions
Lecture 6: IO Functions in NumPy
Chapter 8: Challenge Section – NumPy
Lecture 1: Challenge Questions
Lecture 2: Challenge Solutions
Chapter 9: Pandas
Lecture 1: Pandas Overview
Lecture 2: Introduction to Series
Lecture 3: Introduction to DataFrames
Lecture 4: Selecting Data 1
Lecture 5: Selecting Data 2
Lecture 6: Data Manipulation 1
Lecture 7: Data Manipulation 2
Lecture 8: Data Aggregation and Grouping
Lecture 9: Data Cleansing
Lecture 10: Combining DataFrames
Lecture 11: Windowing Operations
Instructors
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Malvik Vaghadia
Founder – Pathfinder Analytics
Rating Distribution
- 1 stars: 11 votes
- 2 stars: 16 votes
- 3 stars: 107 votes
- 4 stars: 577 votes
- 5 stars: 899 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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