Python for Data Analysis Visualization 2023

Python for Data Analysis Visualization 2023

What you’ll learn

Python, we will be using Python3 in this course
Data Analysis Libraries in Python such as NumPy and Pandas
Data Visualization Libraries in Python such as Matplotlib and Seaborn
How to analyse data
Data Visualization
Jupyter Notebooks IDE / Anaconda Distribution

Requirements

No prior knowledge required

Description

Learn one of the most in demand programming languages in the world and master the most important libraries when it comes to analysing and visualizing data.This course can be split into 3 key areas:The first area of the course focuses on core Python3 and teaches you the essentials you need to be able to master the libraries taught in this courseThe second area focuses on analysing and manipulating data. You will learn how to master both NumPy and PandasFor the final part of the course you learn how to display our data in the form of interesting charts using Matplotlib,  Seaborn and Plotly ExpressYou will be using Jupyter Notebooks as part of the Anaconda Distribution. Jupyter is the most popular Python IDE available.The course is packed with lectures, code-along videos, coding exercises and quizzes. On top of that there are numerous dedicated challenge sections that utilize interesting datasets to enable you to make the most out of these external libraries.There should be more than enough to keep you engaged and learning! As an added bonus you will also have lifetime access to all the lectures as well as lots of downloadable course resources consisting of detailed Notebooks.The aim of this course is to make you proficient at using Python and the data analysis and visualization libraries. This course is suitable for students of all levels and it doesn’t matter what operating system you use.Curriculum summary:Set Up & InstallationCore PythonPython Objects, Variables and Data TypesControl Flow and LoopsFunctionsExternal LibrariesData Analysis LibrariesNumPyPandasConnecting to different Data SourcesVisualization LibrariesMatplotlibSeabornPlotly Express4 dedicated Challenge Sections!!!!

Overview

Section 1: Course Welcome & Set Up

Lecture 1 Course Overview

Lecture 2 Udemy 101

Lecture 3 Python Overview

Lecture 4 Anaconda Distribution Installation

Lecture 5 Jupyter Notebook 101

Lecture 6 Jupyter Notebook – Adding Comments in Cells

Lecture 7 Course Resources – Important!

Section 2: Objects, Variables and Data Types

Lecture 8 Objects and Variables Overview

Lecture 9 Numbers

Lecture 10 Coding Exercise Solution

Lecture 11 Coding Exercise Solution

Lecture 12 Strings

Lecture 13 Coding Exercise Solution

Lecture 14 String Operations

Lecture 15 String Methods and Properties

Lecture 16 Coding Exercise Solution

Lecture 17 String Concatenation and Formatting

Lecture 18 Lists

Lecture 19 Coding Exercise Solution

Lecture 20 Coding Exercise Solution

Lecture 21 Dictionaries

Lecture 22 Coding Exercise Solution

Lecture 23 Tuples and Sets

Lecture 24 Coding Exercise Solution

Lecture 25 Booleans

Lecture 26 Key Words in Python

Section 3: Control Flow and Loops

Lecture 27 Python Operators

Lecture 28 Control Flow

Lecture 29 Coding Exercise Solution

Lecture 30 For Loops

Lecture 31 For Loops (continued)

Lecture 32 Coding Exercise Solution

Lecture 33 Coding Exercise Solution

Lecture 34 While Loops

Lecture 35 Break, Continue and Pass Statements

Lecture 36 List Comprehension

Lecture 37 Coding Exercise Solution

Lecture 38 IN and NOT IN

Section 4: Functions

Lecture 39 Built-In Functions

Lecture 40 Coding Exercise Solution

Lecture 41 User Defined Functions

Lecture 42 User Defined Functions – Examples

Lecture 43 Coding Exercise Solution

Lecture 44 Coding Exercise Solution

Lecture 45 Arguments and Keyword Arguments

Lecture 46 Map and Filter

Lecture 47 Lambda Functions

Lecture 48 Coding Exercise Solution

Lecture 49 Errors and Exception Handling

Section 5: Challenge Section – Core Python

Lecture 50 Challenge Questions Overview

Lecture 51 Solutions Walkthrough

Lecture 52 Corection: Solutions

Section 6: Modules, Packages and Libraries

Lecture 53 Built-In Modules

Lecture 54 External Libraries

Section 7: NumPy

Lecture 55 NumPy Overview

Lecture 56 Array Slicing and Indexing

Lecture 57 Array Manipulation Functions

Lecture 58 Additional Array Creation Functions

Lecture 59 Array Arithmetic and Mathematical Functions

Lecture 60 IO Functions in NumPy

Section 8: Challenge Section – NumPy

Lecture 61 Challenge Questions

Lecture 62 Challenge Solutions

Section 9: Pandas

Lecture 63 Pandas Overview

Lecture 64 Introduction to Series

Lecture 65 Introduction to DataFrames

Lecture 66 Selecting Data 1

Lecture 67 Selecting Data 2

Lecture 68 Data Manipulation 1

Lecture 69 Data Manipulation 2

Lecture 70 Data Aggregation and Grouping

Lecture 71 Data Cleansing

Lecture 72 Combining DataFrames

Lecture 73 Windowing Operations

Section 10: Challenge Section – Pandas

Lecture 74 Challenge Questions – TfL Dataset

Lecture 75 Solutions Walkthrough

Lecture 76 Challenge Questions – Employees Dataset

Lecture 77 Solutions Walkthrough

Section 11: Data Sources

Lecture 78 Excel and CSV

Lecture 79 HTML

Lecture 80 Databases

Lecture 81 Pandas Input and Output Methods

Section 12: Matplotlib

Lecture 82 Matplotlib Overview

Lecture 83 Choosing the Right Chart Type

Lecture 84 Creating a Plot Area 1

Lecture 85 Creating a Plot Area 2

Lecture 86 Bar Plots

Lecture 87 Line Plots

Lecture 88 FIFA 21 Player Dataset

Lecture 89 Scatter Plots

Lecture 90 Histograms

Lecture 91 Box Plots and Violin Plots

Lecture 92 Style and Presentation

Lecture 93 Additional Resources and Cheat Sheets

Section 13: Challenge Section – Matplotlib

Lecture 94 Challenge Questions Overview

Lecture 95 Solutions Walkthrough

Section 14: Seaborn

Lecture 96 Seaborn Overview

Lecture 97 Categorical Plots

Lecture 98 Relational Plots

Lecture 99 Distribution Plots

Lecture 100 Regression Plots

Lecture 101 Matrix Plots

Lecture 102 Multi Plot Grids

Lecture 103 Style and Presentation

Section 15: Challenge Section – Seaborn

Lecture 104 Challenge Questions Overview

Lecture 105 Solutions Walkthrough

Section 16: Plotly Express

Lecture 106 Plotly Express Overview

Lecture 107 Interactive Charts in Plotly Express

Lecture 108 3D Charts

Lecture 109 BONUS: Further Learning

Lecture 110 BONUS: Further Learning Resources

Section 17: Keep learning with me

Lecture 111 BONUS: Check out my other courses

Python developers curious about the data analysis libraries,Python developers curious about the data visualization libraries,Anyone interested in learning Python,Data Analysts,Anyone working with data

Course Information:

Udemy | English | 10h 3m | 3.66 GB
Created by: Malvik Vaghadia

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