Learning Python for Data Analysis and Visualization

Learning Python for Data Analysis and Visualization, available at $49.99, has an average rating of 4.2, with 62 lectures, 12 quizzes, based on 10 reviews, and has 171 subscribers.

You will learn about Data Analysis and Visualization using Python Pandas and various operations on Pandas Create your own Data set and Data Frame in Python Learn to Handle Files in Python Work with HTML, JSON, EXCEL, CSV files with easily Get Started with Data Visualization Projects Exploratory Data Analysis Work with Python Plotting Libraries like Matplotlib, Seaborn, Plotly Learn Plotly interactive plotting library to add aesthetic appeal to your projects Learn various plots of and draw insights from plots using Matplotlib Library Learn to plot various plots using Seaborn Library and meaning of each plot Learn to display plots, graphs, maps, add animation using Plotly Interested in Data Visualization and want to draw insights from the visualizations This course is ideal for individuals who are Beginners in field of Data Science, Data Analytics and Data Visualization or Anyone who wants to Learn Data Analysis and Visualization. or Graduates who want a Career switch or Post Graduates who want a Career Switch or Professionals Clueless on how to enter Data Science or Those who want to learn to Code less and obtain good results It is particularly useful for Beginners in field of Data Science, Data Analytics and Data Visualization or Anyone who wants to Learn Data Analysis and Visualization. or Graduates who want a Career switch or Post Graduates who want a Career Switch or Professionals Clueless on how to enter Data Science or Those who want to learn to Code less and obtain good results.

Enroll now: Learning Python for Data Analysis and Visualization

Summary

Title: Learning Python for Data Analysis and Visualization

Price: $49.99

Average Rating: 4.2

Number of Lectures: 62

Number of Quizzes: 12

Number of Published Lectures: 62

Number of Published Quizzes: 12

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: ₹1,799

Quality Status: approved

Status: Live

What You Will Learn

Data Analysis and Visualization using Python
Pandas and various operations on Pandas
Create your own Data set and Data Frame in Python
Learn to Handle Files in Python
Work with HTML, JSON, EXCEL, CSV files with easily
Get Started with Data Visualization Projects
Exploratory Data Analysis
Work with Python Plotting Libraries like Matplotlib, Seaborn, Plotly
Learn Plotly interactive plotting library to add aesthetic appeal to your projects
Learn various plots of and draw insights from plots using Matplotlib Library
Learn to plot various plots using Seaborn Library and meaning of each plot
Learn to display plots, graphs, maps, add animation using Plotly
Interested in Data Visualization and want to draw insights from the visualizations

Who Should Attend

Beginners in field of Data Science, Data Analytics and Data Visualization
Anyone who wants to Learn Data Analysis and Visualization.
Graduates who want a Career switch
Post Graduates who want a Career Switch
Professionals Clueless on how to enter Data Science
Those who want to learn to Code less and obtain good results

Target Audiences

Beginners in field of Data Science, Data Analytics and Data Visualization
Anyone who wants to Learn Data Analysis and Visualization.
Graduates who want a Career switch
Post Graduates who want a Career Switch
Professionals Clueless on how to enter Data Science
Those who want to learn to Code less and obtain good results

Data Analysis & Visualization With Python is a course designed for all those who want to learn how to analyze, visualize and dive deep into data.

Why Learn Data Visualization for Data Science?

Data Visualization is a very powerful tool available to showcase our data, findings, and insights.
This course is designed keeping in mind the role of how important Data Visualization is while building a Machine Learning project.
Data Analysis provides strong guidance rather than general guidance.
Data Visualization can speak volumes about the data, it can show the trends, distribution, correlation, spread of the data.
The insights from the data, aids in decision making, improving results and performance

Course Details

The course is for those who want to learn how to visualize the data while working on several Data Science Projects;
Those who want to learn about how to plot in Python using various plotting libraries, and all those who are interested in Data Analysis and Visualization.
The Course is divided into several sections each section has mostly practical exercises, projects, quizzes, and resources to give a complete learning experience.
The Course is designed to take you one step further in Machine Learning and Data Science field.
The projects follow a systematic approach, this will help you to understand the basic concepts and will give you a kick start in your Data Science Career.

The course is crafted with due diligence keeping in mind the need of the learners. Learning is made very easy and simple by following steps like explaining the concepts, applying the concepts in practical lectures followed by quizzes to deepen the understanding of the concepts also to have a quick revision.

Enjoy the Course as I have enjoyed it and it will be completed in no time!

All the Best and Good Luck! See you aboard.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Preview

Chapter 2: 2 : Getting Started : Installation and Set Up

Lecture 1: Getting Started With Anaconda Distribution

Lecture 2: Learn how to Install

Lecture 3: Installation Continues

Lecture 4: Introduction to Jupyter

Chapter 3: Create your own Data with Pandas, Mini Project in Pandas and Matplolib

Lecture 1: Why Learn Pandas

Lecture 2: Pandas Introduction

Lecture 3: Introduction to the Project

Lecture 4: Learn to Start the Mini Project

Lecture 5: Export the data

Lecture 6: Import the Data

Lecture 7: Prepare Data

Lecture 8: Analyze Data

Lecture 9: Plotting the Data

Lecture 10: Project Wrap Up

Lecture 11: Pivot Tables in Pandas

Lecture 12: Cross Tables in Pandas

Lecture 13: Comparison between Pivot and Cross tables

Chapter 4: Handling Files in Python

Lecture 1: Handling Files Introduction

Lecture 2: Import and Export an Excel File

Lecture 3: Import and Export JSON FIle

Lecture 4: Import and Export HTML File

Lecture 5: Import and Export a CSV File

Chapter 5: Visualization with Seaborn

Lecture 1: Seaborn Project Introduction

Lecture 2: Seaborn Introduction

Lecture 3: Get Started with the Data

Lecture 4: Introduction to Univariate Plots

Lecture 5: Univariate Plots Practical Exercise

Lecture 6: Introduction to Bivariate Plots

Lecture 7: Bivariate Plots Practical Exercise

Lecture 8: Multi Variate Plots in Seaborn

Lecture 9: Multi Variate Plots Practical Exercise

Lecture 10: Regression Plots In Seaborn

Lecture 11: Regression Plots Practical Exercise

Lecture 12: Categorical Plots in Seaborn

Lecture 13: Categorical Plots Practical Exercise

Lecture 14: Seaborn Project Wrap Up

Lecture 15: Facet Grids in Seaborn

Lecture 16: Facet Grids Practical Exercise

Chapter 6: Major Project : Objectives

Lecture 1: Major Project : Objectives

Lecture 2: Project Introduction: Loan Prediction

Lecture 3: Loading Data

Lecture 4: Exploring the Data

Lecture 5: Cleaning the Data

Lecture 6: Solution for dropna() not working

Lecture 7: Correlation and Frequency Analysis

Lecture 8: Data Analysis with Group By Method

Lecture 9: Data Analysis and Visualization with Pivot Table

Lecture 10: Data Analysis & Visualization with Cross Tables

Lecture 11: Project Wrap Up

Chapter 7: Data Analysis and Visualization with Plotly

Lecture 1: Plotly Introduction

Lecture 2: Plotly Project: World Development Indicators

Lecture 3: Install and Load Plotly and Data

Lecture 4: Pie Charts In Plotly

Lecture 5: Bar and Scatter in Plotly

Lecture 6: Animation Plots in Plotly

Lecture 7: Choropleth in Plotly

Lecture 8: Plotly Project Wrap Up

Chapter 8: Appendix

Lecture 1: Way Froward

Lecture 2: Projects

Chapter 9: Bonus Section

Lecture 1: Bonus

Instructors

Priyanka Sharma
Data Analyst, Content Writer, Instructor

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 6 votes
5 stars: 2 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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