Python and Pandas for the Anaconda Jupyter Notebook

Python and Pandas for the Anaconda Jupyter Notebook, available at $44.99, has an average rating of 4.15, with 54 lectures, based on 20 reviews, and has 139 subscribers.

You will learn about Python Pandas Data Analysis Jupyter Notebook Data Science This course is ideal for individuals who are Busy professionals looking to advance their data analysis skills and learn some data science and programming. It is particularly useful for Busy professionals looking to advance their data analysis skills and learn some data science and programming.

Enroll now: Python and Pandas for the Anaconda Jupyter Notebook

Summary

Title: Python and Pandas for the Anaconda Jupyter Notebook

Price: $44.99

Average Rating: 4.15

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Python
Pandas
Data Analysis
Jupyter Notebook
Data Science

Who Should Attend

Busy professionals looking to advance their data analysis skills and learn some data science and programming.

Target Audiences

Busy professionals looking to advance their data analysis skills and learn some data science and programming.

My name is Henry Palma and I have been working with data in Finance and Technology for the last 16 years. I have worked for Investment Banks, Consulting Firms and Credit Card companies helping to design financial calculation engines and reporting systems. The lessons in this program are designed to teach you real world applications of Pandas and Python in a professional environment. My goal is to get you up and ready to start coding in Python and Pandas as soon as possible.

This program is designed for professionals curious about data analysis and data engineering with Python and the Pandas Data Analysis Library. You don’t need to be an experienced programmer to learn in these tutorials. I will give you some code and explain how it works. You will be able to take that code and use it in your day to day learning. In my experience learning to program really boils down to learning some code and then tinkering until you understand how things work. This program is designed that way.

This program is spite into 3 sections. The section on the Anaconda Notebook will walk you through the basics of the Anaconda Notebook. The section on Python will get you familiar with the Python language and the code you will need to get started with the Pandas library. The section on Pandas will get you up and running with all of the fundamentals of the Pandas data analysis library.

Course Curriculum

Chapter 1: Pandas Tutorials

Lecture 1: Introduction to the program

Lecture 2: Pre-Requisite: Installing the Jupyter Notebook

Lecture 3: Pre-Requisite: Using the Jupyter Notebook

Lecture 4: Introduction to The Python Pandas Library

Lecture 5: Data Analysis QuickStart: Loading a CSV file into a DataFrame

Lecture 6: Data Analysis QuickStart: Viewing and Selecting DataFrame Data

Lecture 7: Data Analysis QuickStart: Adding Columns to a Pandas DataFrame

Lecture 8: Data Analysis: Aggregating Data using Groupby and Pivot a Pandas DataFrame

Lecture 9: Data Analysis Plot a Pandas DataFrame

Lecture 10: 8 Techniques to Create a DataFrame

Lecture 11: 10 Techniques to Filter/Search a DataFrame

Lecture 12: 12 Techniques to Update A DataFrame

Lecture 13: DataFrame Technique (ApplyMap) on a DataFrame

Lecture 14: DataFrame Technique (Map) function on a DataFrame

Lecture 15: 6 Techniques to Export a DataFrame

Lecture 16: 8 Techniques to Create a Pandas Series

Lecture 17: Pandas Series Attributes for Analysis

Lecture 18: 9 Techniques to Select and Filter a Series

Lecture 19: 5 Techniques to Update a Series

Chapter 2: (Optional) Python Fundamentals

Lecture 1: Hello World in Python

Lecture 2: Keywords in Python

Lecture 3: Python Variables and DataTypes

Lecture 4: Math in Python

Lecture 5: Strings in Python

Lecture 6: String.format() in Python

Lecture 7: Standard Lib in Python

Lecture 8: Getting User input in Python

Lecture 9: Decision Making in Python

Lecture 10: Loops in Python

Lecture 11: Lists in Python

Lecture 12: Dictionaries in Python

Lecture 13: Sets in Python

Lecture 14: Tuples in Python updated 2023

Lecture 15: List Comprehensions in Python

Lecture 16: Dealing with Dates and Time in Python

Lecture 17: Functions in Python

Lecture 18: Lambda Functions in Python

Lecture 19: Classes in Python

Lecture 20: Exceptions in Python

Lecture 21: Decorators in Python (Added 2023)

Lecture 22: Using Files in Python (Added 2023)

Lecture 23: Using the Requests library in Python (Added 2023)

Chapter 3: Mark Down Tutorials (Optional)

Lecture 1: MarkDown Creating Headers

Lecture 2: MarkDown Creating Horizontal Lines

Lecture 3: MarkDown Bulleted Lists

Lecture 4: MarkDown Numbered Lists

Lecture 5: MarkDown Nested Lists

Lecture 6: MarkDown Indented Text

Lecture 7: MarkDown Formatted Text

Lecture 8: MarkDown Links

Lecture 9: MarkDown Images

Lecture 10: MarkDown Tables

Lecture 11: MarkDown Latex and Greeks

Lecture 12: Keyboard ShortCuts

Instructors

Henry Palma
Software/Data Engineer/Mobile Developer

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 8 votes
5 stars: 8 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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