Data Analysis with Python Pandas and Jupyter Notebook

Data Analysis with Python Pandas and Jupyter Notebook, available at $54.99, has an average rating of 4.5, with 34 lectures, based on 10 reviews, and has 808 subscribers.

You will learn about install Python on both Windows and macOS systems. Create and Manage Virtual Environments Install and set up Jupyter Notebook and navigate its interface efficiently. Create Pandas Series from lists and dictionaries and understand their structure and functionality. Access data in Series using labels and positions, and perform slicing operations. Create and manipulate DataFrames from various data structures such as dictionaries and lists of dictionaries. Efficiently access and manipulate data within DataFrames. Conduct thorough data inspections and clean data to prepare it for analysis. Build confidence in your ability to handle complex data analysis tasks independently. Apply data transformation techniques to reshape and modify datasets. Create compelling visualizations of data using Pandas This course is ideal for individuals who are Aspiring Data Analysts or Beginners in Programming and Data Science or Professionals Looking to Upskill or Students and Academics or Business Analysts and Managers or Anyone Interested in Data It is particularly useful for Aspiring Data Analysts or Beginners in Programming and Data Science or Professionals Looking to Upskill or Students and Academics or Business Analysts and Managers or Anyone Interested in Data.

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Summary

Title: Data Analysis with Python Pandas and Jupyter Notebook

Price: $54.99

Average Rating: 4.5

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will Learn

install Python on both Windows and macOS systems.
Create and Manage Virtual Environments
Install and set up Jupyter Notebook and navigate its interface efficiently.
Create Pandas Series from lists and dictionaries and understand their structure and functionality.
Access data in Series using labels and positions, and perform slicing operations.
Create and manipulate DataFrames from various data structures such as dictionaries and lists of dictionaries.
Efficiently access and manipulate data within DataFrames.
Conduct thorough data inspections and clean data to prepare it for analysis.
Build confidence in your ability to handle complex data analysis tasks independently.
Apply data transformation techniques to reshape and modify datasets.
Create compelling visualizations of data using Pandas

Who Should Attend

Aspiring Data Analysts
Beginners in Programming and Data Science
Professionals Looking to Upskill
Students and Academics
Business Analysts and Managers
Anyone Interested in Data

Target Audiences

Aspiring Data Analysts
Beginners in Programming and Data Science
Professionals Looking to Upskill
Students and Academics
Business Analysts and Managers
Anyone Interested in Data

Unlock the full potential of data analysis and visualization with Data Analysis with Python Pandas and Jupyter Notebook

This course is designed to take you from the very basics of Python setup to financial data insights, equipping you with the skills necessary to thrive in the data-driven world.

Introduction to Pandas

We’ll start by understanding what Python is and how to install it on both Windows and macOS platforms. You’ll learn the importance of virtual environments, how to create and activate them, ensuring a clean and organized workspace for your projects.

We’ll then introduce you to Jupyter Notebook, a powerful tool that enhances the data analysis experience. You’ll learn how to install Pandas and Jupyter Notebook within your virtual environment, start the Jupyter Notebook server, and navigate its intuitive interface. By the end of this section, you’ll be proficient in creating and managing notebooks, setting the stage for your data analysis journey.

Pandas Data Structures

With your environment set up, we dive into the heart of Pandas: its core data structures. You’ll discover the power of Series and DataFrame, the fundamental building blocks of data manipulation in Pandas. You’ll learn to create Series from lists and dictionaries, access data using labels and positions, and perform slicing operations.

The course then progresses to DataFrames, where you’ll master creating DataFrames from dictionaries and lists of dictionaries. You’ll gain practical experience in accessing and manipulating data within DataFrames, preparing you for more complex data analysis tasks.

Conclusion

By the end of this course, you will have a deep understanding of Pandas and its capabilities in data analysis and visualization. You’ll be equipped with the skills to handle and analyze complex datasets, transforming them into actionable insights. Whether you’re a beginner or looking to enhance your data science skills, this course will empower you to harness the power of Pandas for financial data analysis and beyond. Embark on this transformative learning journey and become a proficient data analyst with Pandas.

Course Curriculum

Chapter 1: Introduction to Data Analysis and Pandas Basics

Lecture 1: Introduction

Lecture 2: What is Data Analysis

Lecture 3: Understanding the role of data analysis in decision-making

Lecture 4: Overview of Python tools for data analysis

Lecture 5: What is Pandas and why it's essential for data analysis

Chapter 2: Setting up your environment

Lecture 1: Python Installation on Windows

Lecture 2: What are virtual environments

Lecture 3: Creating and activating a virtual environment on Windows

Lecture 4: Python Installation on macOS

Lecture 5: Creating and activating a virtual environment on macOS

Lecture 6: What is Jupyter Notebook

Lecture 7: Installing Pandas and Jupyter Notebook in the Virtual Environment

Lecture 8: Starting Jupyter Notebook

Lecture 9: Exploring Jupyter Notebook Server Dashboard Interface

Lecture 10: Creating a new Notebook

Lecture 11: Exploring Jupyter Notebook Source and Folder Files

Lecture 12: Exploring the Notebook Interface

Chapter 3: Data Analysis ,Manipulation and Visualization

Lecture 1: Creating a Pandas Series from a List

Lecture 2: Creating a Pandas Series from a List with Custom Index

Lecture 3: Creating a pandas series from a Python Dictionary

Lecture 4: Accessing Data in a Series using the index by label

Lecture 5: Accessing Data in a Series By position

Lecture 6: Slicing a Series by Label

Lecture 7: Creating a DataFrame from a dictionary of lists

Lecture 8: Creating a DataFrame From a list of dictionaries

Lecture 9: Accessing data in a DataFrame

Lecture 10: Download Dataset

Lecture 11: Loading Dataset into a DataFrame

Lecture 12: Inspecting the data

Lecture 13: Data Cleaning

Lecture 14: Data transformation and analysis

Lecture 15: Visualizing data

Chapter 4: Best Practices and Tips for Efficient Data Analysis

Lecture 1: Optimizing performance with Pandas

Lecture 2: Handling large datasets and memory efficiency

Instructors

247 Learning
An investment in knowledge pays the best interest

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5 stars: 6 votes

Frequently Asked Questions

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