Data Wrangling and Visualization with Python

Data Wrangling and Visualization with Python, available at $19.99, has an average rating of 3.25, with 105 lectures, based on 2 reviews, and has 13 subscribers.

You will learn about Learn key analytical skills (data cleaning, analysis, & visualization) Understand how to clean and organize data for analysis, and complete analysis and calculations using Python programming Learn how to visualize and present data findings in visualization platforms (Pandas, Seaborn, Plotly express) Understand and practice the use of Pandas DataFrames Visualize data with Seaborn and Plotly Use matplotlib to plot basic graphs and charts Create common visualization charts using open source tools Learn how to create geographic plots using plotly express and geopandas Learn how to use pandas to sort, filter, import and clean data students will learn how to import xcell, CSV and custom data to Jupyter notebooks Understand key data analysis terms and definitions This course is ideal for individuals who are Beginners interested in Data analysis and Visualisation using Python or Intermdiate users who want to learn how to create graphs and charts with python or users interested in learning how to create grographic plots using plotly express and geopandas or students interested in learning how to import, clean, sort, filter and manipulate data using pandas It is particularly useful for Beginners interested in Data analysis and Visualisation using Python or Intermdiate users who want to learn how to create graphs and charts with python or users interested in learning how to create grographic plots using plotly express and geopandas or students interested in learning how to import, clean, sort, filter and manipulate data using pandas.

Enroll now: Data Wrangling and Visualization with Python

Summary

Title: Data Wrangling and Visualization with Python

Price: $19.99

Average Rating: 3.25

Number of Lectures: 105

Number of Published Lectures: 105

Number of Curriculum Items: 105

Number of Published Curriculum Objects: 105

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

Learn key analytical skills (data cleaning, analysis, & visualization)
Understand how to clean and organize data for analysis, and complete analysis and calculations using Python programming
Learn how to visualize and present data findings in visualization platforms (Pandas, Seaborn, Plotly express)
Understand and practice the use of Pandas DataFrames
Visualize data with Seaborn and Plotly
Use matplotlib to plot basic graphs and charts
Create common visualization charts using open source tools
Learn how to create geographic plots using plotly express and geopandas
Learn how to use pandas to sort, filter, import and clean data
students will learn how to import xcell, CSV and custom data to Jupyter notebooks
Understand key data analysis terms and definitions

Who Should Attend

Beginners interested in Data analysis and Visualisation using Python
Intermdiate users who want to learn how to create graphs and charts with python
users interested in learning how to create grographic plots using plotly express and geopandas
students interested in learning how to import, clean, sort, filter and manipulate data using pandas

Target Audiences

Beginners interested in Data analysis and Visualisation using Python
Intermdiate users who want to learn how to create graphs and charts with python
users interested in learning how to create grographic plots using plotly express and geopandas
students interested in learning how to import, clean, sort, filter and manipulate data using pandas

Welcome to the Data wrangling and visualization course with Python. This course is intended for beginners who are interested in the wonderful world of data wrangling and visualization. This course assumes you don’t have experience with python and it attempts to demystify and make it as clear as possible using basic and concise examples. The course begins with an introduction to the python programming. Next, we move on and learn about common visualization tools and some popular python Data Visualization plugins (pandas, seaborn, plotly express) libraries with some practical examples. In this course we chosen to use python because is a powerful language that, is free, beginner friendly. We will use open-source data manipulation libraries such as pandas, geopandas, plotly, plotly express, sci-py, matplotlib. We would also learn about popular data manipulation libraries such as Pandas, NumPy, and matplotlib. These libraries will be used to understand the basics of data wrangling with numerous practical examples. Furthermore, we will investigate using python for Geographic Plots , using JSON files and the popular geopandas library for creating map plots. To wrap things up, we look at some practical projects and solutions to enforce, solidify and strengthen the concepts we have learnt throughout the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome and Thank You

Lecture 2: Course Materials and Exercise Files

Chapter 2: Introduction to Data Science

Lecture 1: What is Data Science, Machine Learning, Data Analys and AI?

Lecture 2: Significance of Data Science

Lecture 3: Understanding Data

Lecture 4: The Future of Data

Chapter 3: Tools

Lecture 1: Installing Anaconda

Lecture 2: Jupyter Notebook Overview

Lecture 3: Installing Jupyter Notebook

Lecture 4: Jupyter Notebook Interface

Lecture 5: Saving and Loading Jupyter Notebook files

Lecture 6: Jupyter notebook comments and Markdown cells

Lecture 7: Jupyter notebook markdown

Lecture 8: Converting Jupyter files to other formats

Lecture 9: Getting Help

Chapter 4: Python Quick Course

Lecture 1: Section Introduction

Lecture 2: Python's Variables and Datatypes

Lecture 3: Variables and Datatypes part 2

Lecture 4: Variable Naming Rules

Lecture 5: Using Python as a calculator

Lecture 6: Converting between different Datatypes using Python

Lecture 7: Output Formatting

Lecture 8: Output Formatting part 2

Chapter 5: Numpy and Python for Data Analysis

Lecture 1: Numpy Introduction

Lecture 2: Installing NumPy

Lecture 3: Creating nd-Arrays with numpy

Lecture 4: Creating Arrays with numPy Part 2

Lecture 5: Basic Array Operations with NumPy

Lecture 6: Basic Statistics with NumPy

Lecture 7: NumPy Array Slicing and Indexing

Lecture 8: Generating Random nd Arrays

Lecture 9: Solving Basic Equations with numPy

Lecture 10: Numpy Exercise Section

Lecture 11: Numpy Exercise Solutions

Chapter 6: Pandas and Python for Data Analysis

Lecture 1: Pandas Introduction

Lecture 2: Data Sources

Lecture 3: Reading CSV files with Pandas

Lecture 4: Reading Excel files with Pandas

Lecture 5: Pandas Series

Lecture 6: Pandas DataFrame

Lecture 7: Pandas DataFrame 2

Lecture 8: Descriptive Statistics with Pandas

Lecture 9: Handling Missing Data with Pandas

Lecture 10: Using Indexers (loc & iloc)

Lecture 11: Sorting and Using Expressions

Lecture 12: Using Conditions

Lecture 13: Combining DataFrames

Lecture 14: Using Merged to intersect DataFrames

Lecture 15: Converting DataFrames to csv, excel, txt

Lecture 16: Pandas Reference Guide

Lecture 17: Pandas time series

Lecture 18: Slicing strings in columns

Lecture 19: Pandas Exercise

Lecture 20: Pandas Exercise Solution

Chapter 7: Data Visualisation-matplotlib

Lecture 1: Introduction to matlotlib

Lecture 2: matplotlib first run

Lecture 3: matplotlib figure object

Lecture 4: matplotlib histogram and scatter plot

Lecture 5: matplotlib vertical bar graph

Lecture 6: Matplotlib Horizontal Bar Graph

Lecture 7: Line Plot Styles

Lecture 8: basic vs object oriented approach in matplotlib

Lecture 9: adding legends and saving files

Lecture 10: Stacked Bar Graph

Lecture 11: Grouped Bar Graph

Lecture 12: matplotlib bar chart

Lecture 13: Matplotlib pie chart

Lecture 14: Matplotlib pie chart from csv file

Lecture 15: Matplotlib Exercise

Lecture 16: matplotlib Exercise Solutions

Chapter 8: Data Visualization- Seaborn

Lecture 1: Section Introduction

Lecture 2: installing seaborn and statsmodels with pip

Lecture 3: working with csv files using seaborn and pandas

Lecture 4: Relational Plot

Lecture 5: categorical plots introduction

Lecture 6: Seaborn Categorical Plots

Lecture 7: Seaborn Categorical Plots Part 2

Lecture 8: Distribution Plots Introduction

Lecture 9: Environments and Packages Introduction

Lecture 10: Environments and Packages Setup

Lecture 11: Distribution plots(Hist, ecdf, kde )

Lecture 12: Seaborn Jointplot

Lecture 13: Seaborn pairplot

Lecture 14: Seaborn Array Plots (Heatmap, Annotated Heatmap, Clustermap)

Lecture 15: Seaborn Facet Grid

Lecture 16: Regression Plots

Lecture 17: seaborn colors

Lecture 18: seaborn themes and styles

Chapter 9: Data Visualisation with plotly

Lecture 1: Introduction to plotly

Lecture 2: installing plotly

Lecture 3: plotly first run

Instructors

Mahmud Shuaib
Digital/Traditional Artist/Programmer

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