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
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- 4 stars: 2 votes
- 5 stars: 6 votes
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