Python Pandas for Data Science- Pandas,Matplotlib, JupyterNb

Python Pandas for Data Science: Pandas,Matplotlib, JupyterNb, available at $54.99, has an average rating of 3.64, with 32 lectures, based on 7 reviews, and has 561 subscribers.

You will learn about Build confidence in your ability to handle complex data analysis tasks independently. Apply data analysis skills to real-world datasets and derive actionable insights. install Python on both Windows and macOS systems Create and Manage Virtual Environments Create and manage Jupyter Notebooks for interactive data analysis. Create compelling visualizations of data using Pandas Perform detailed analysis on financial data to extract meaningful insights. Apply data transformation techniques to reshape and modify datasets Conduct thorough data inspections and clean data to prepare it for analysis. Gain an understanding of the Pandas library and its capabilities. Create Pandas Series from lists and dictionaries and understand their structure and functionality. Efficiently access and manipulate data within DataFrames This course is ideal for individuals who are Aspiring Data Analysts or Beginners in Programming and Data Science or Anyone Interested in Data or Professionals Looking to Upskill or Students and Academics or Business Analysts and Managers It is particularly useful for Aspiring Data Analysts or Beginners in Programming and Data Science or Anyone Interested in Data or Professionals Looking to Upskill or Students and Academics or Business Analysts and Managers.

Enroll now: Python Pandas for Data Science: Pandas,Matplotlib, JupyterNb

Summary

Title: Python Pandas for Data Science: Pandas,Matplotlib, JupyterNb

Price: $54.99

Average Rating: 3.64

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Build confidence in your ability to handle complex data analysis tasks independently.

Apply data analysis skills to real-world datasets and derive actionable insights.

install Python on both Windows and macOS systems

Create and Manage Virtual Environments

Create and manage Jupyter Notebooks for interactive data analysis.

Create compelling visualizations of data using Pandas

Perform detailed analysis on financial data to extract meaningful insights.

Apply data transformation techniques to reshape and modify datasets

Conduct thorough data inspections and clean data to prepare it for analysis.

Gain an understanding of the Pandas library and its capabilities.

Create Pandas Series from lists and dictionaries and understand their structure and functionality.

Efficiently access and manipulate data within DataFrames

Who Should Attend

Aspiring Data Analysts

Beginners in Programming and Data Science

Anyone Interested in Data

Professionals Looking to Upskill

Students and Academics

Business Analysts and Managers

Target Audiences

Aspiring Data Analysts

Beginners in Programming and Data Science

Anyone Interested in Data

Professionals Looking to Upskill

Students and Academics

Business Analysts and Managers

With this course, you’ll learn why pandas is the world’s most popular Python library, used for everything from data manipulation to data analysis. You’ll explore how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis.

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.

Pandas Data Manipulation, Analysis and Visualization

Armed with a solid understanding of Pandas, we venture into the realm of financial data analysis. You’ll learn to download datasets, load them into DataFrames, and conduct thorough data inspections. We’ll guide you through essential data cleaning techniques to ensure your datasets are ready for analysis.

Data transformation and analysis take center stage as you uncover insights from your financial data. You’ll apply various Pandas operations to transform raw data into meaningful information. Finally, we’ll explore data visualization, teaching you how to create compelling visual representations of your analysis.

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 Python Pandas

Lecture 1: Introduction

Lecture 2: Overview of Python for data analysis

Lecture 3: Introduction to pandas library

Chapter 2: Installation and setup

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 Structures in pandas

Lecture 1: Series and DataFrame objects

Lecture 2: Creating a Pandas Series from a List

Lecture 3: Creating a Pandas Series from a List with Custom Index

Lecture 4: Creating a pandas series from a Python Dictionary

Lecture 5: Accessing Data in a Series using the index by label

Lecture 6: Accessing Data in a Series By position

Lecture 7: Slicing a Series by Label

Lecture 8: Creating a DataFrame from a dictionary of lists

Lecture 9: Creating a DataFrame From a list of dictionaries

Lecture 10: Accessing data in a DataFrame

Lecture 11: Manipulating Data in a DataFrame

Chapter 4: Data Manipulation and Visualization with pandas

Lecture 1: Download Dataset

Lecture 2: Loading Dataset into a DataFrame

Lecture 3: Inspecting the data

Lecture 4: Data Cleaning

Lecture 5: Data transformation and analysis

Lecture 6: Visualizing data

Instructors

Skill Tree
Skill based learning

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Frequently Asked Questions

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