Data Visualization with Python Masterclass | Python A-Z

June 26, 2023 Courses
Data Visualization with Python Masterclass | Python A-Z

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 144 lectures (20h 33m) | 5.40 GB

Python Data visualization: Python data analysis and visualization, Machine Learning, Deep Learning, Pandas, Matplotlib

Data visualization, data analysis, and visualization, python data analysis and visualization, tableau data visualization, data visualization, data visualization expert

Welcome to the “Data Visualization with Python Masterclass | Python A-Z” course.
Learn python and how to use it for data analysis and visualization, present data. Includes codes of data visualization.

Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job. Whether you’re interested in learning Tableau, D3.js, After Effects, or Python, OAK Academy has a course for you.
Statistics alone can fall flat. That’s why data visualization is so important to communicate the meaning behind data sets. Good visualizations can magically transform complex data analysis into appealing and easily understood representations that in turn inform smarter, more calculated business moves. Python data analysis and visualization, python, python data analysis, data visualization, data visualization with python masterclass | python a-z, oak academy, data visualization python, data analysis and visualization, python for data analysis, data visualization with python masterclass, pyplot, data visualization using python, data analysis, python visualization, data visualization in python, data analysis using python, python data visualization, visualization python, python for data visualization

In this course, we will learn what is data visualization and how does it work with python.

Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.

This course has suitable for everybody who interested in data visualisation concept.

First of all, in this course, we will learn some fundamentals of pyhton, and object oriented programming ( OOP ). These are our first steps in our Data Visualisation journey. After then we take our journey to the Data Science world. Here we will take a look at data literacy and data science concepts. Then we will arrive at our next stop. Numpy library. Here we learn what is numpy and how we can use it. After then we arrive at our next stop. Pandas library. And now our journey becomes an adventure. In this adventure we’ll enter the Matplotlib world then we exit the Seaborn world. Then we’ll try to understand how we can visualize our data, data viz. But our journey won’t be over. Then we will arrive our final destination. Geographical drawing or best known as Geoplotlib in tableau data visualization.

Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.

Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python’s simple syntax is especially suited for desktop, web, and business applications. Python’s design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python’s large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.

In this course, you will learn data analysis and visualization in detail.

Also during the course, you will learn:

  • Fundamental stuff of pyhton and OOP, Overview of Jupyter Notebook and Google Colab.
  • What is the Data Science and Data Literacy
  • Fundamental stuff of Numpy and Pandas library in data analysis.
  • What is Data Visualization
  • Python data analysis and visualization
  • Python data analysis
  • Data visualization
  • Advanced excel for data analysis
  • The Logic of Matplotlib
  • What is Matplotlib
  • Using Matplotlib
  • Pyplot – Pylab – Matplotlib – Excel
  • Figure, Subplot, Multiplot, Axes,
  • Figure Customization
  • Plot Customization
  • Grid, Spines, Ticks
  • Basic Plots in Matplotlib
  • Overview of Jupyter Notebook and Google Colab
  • Seaborn library with these topics
  • What is Seaborn
  • Controlling Figure Aesthetics
  • Color Palettes
  • Basic Plots in Seaborn
  • Multi-Plots in Seaborn
  • Regression Plots and Squarify
  • Geoplotlib with these topics
  • What is Geoplotlib
  • Tile Providers and Custom Layers

And of course, we enhanced all of it lots of examples with different concept and level. I bet you will like it.

Table of Contents

Code Files And Resources Python data analysis and visualization
1 Section 6 Data Visualisation – Matplotlib Files
2 Section 7 Data Visualisation – Seaborn Files
3 Section 9 Data Visualisation – Geoplotlib

Introduction to Data Visualization with Python
4 Introduction to Data Visualization with Python
5 FAQ regarding Data Visualization, Python

Python Setup
6 Installing Anaconda Distribution For Windows
7 Installing Anaconda Distribution For Mac
8 Installing Anaconda Distribution For Linux
9 Overview of Jupyter Notebook and Google Colab

Fundamentals of Python 3
10 Data Types in Python
11 Operators in Python
12 Conditionals in Python
13 Loops in Python
14 Lists, Tuples, Dictionaries and Sets in pyhton
15 Data Type Operators and Methods in Python
16 Modules in Python
17 Functions in Python
18 Exercise – Analyse in Python
19 Exercise – Solution in Python

Object Oriented Programming (OOP)
20 Logic of Object Oriented Programming
21 Constructor in Object Oriented Programming (OOP)
22 Methods in Object Oriented Programming (OOP)
23 Inheritance in Object Oriented Programming (OOP)
24 Overriding and Overloading in Object Oriented Programming (OOP)

Fundamentals of Data Science
25 What is Data Science
26 Data Literacy
27 Introduction to NumPy Library
28 Notebook Project Files Link regarding NumPy Python Programming Language Library
29 The Power of NumPy
30 Article Advice And Links about Numpy, Numpy Pyhon
31 Creating NumPy Array with The Array() Function
32 Creating NumPy Array with Zeros() Function
33 Creating NumPy Array with Ones() Function
34 Creating NumPy Array with Full() Function
35 Creating NumPy Array with Arange() Function
36 Creating NumPy Array with Eye() Function
37 Creating NumPy Array with Linspace() Function
38 Creating NumPy Array with Random() Function
39 Properties of NumPy Array
40 Reshaping a NumPy Array Reshape() Function
41 Identifying the Largest Element of a Numpy Array
42 Detecting Least Element of Numpy Array Min(), Ar
43 Concatenating Numpy Arrays Concatenate() Functio
44 Splitting One-Dimensional Numpy Arrays The Split
45 Splitting Two-Dimensional Numpy Arrays Split(),
46 Sorting Numpy Arrays Sort() Function
47 Indexing Numpy Arrays
48 Slicing One-Dimensional Numpy Arrays
49 Slicing Two-Dimensional Numpy Arrays
50 Assigning Value to One-Dimensional Arrays
51 Assigning Value to Two-Dimensional Array
52 Fancy Indexing of One-Dimensional Arrrays
53 Fancy Indexing of Two-Dimensional Arrrays
54 Combining Fancy Index with Normal Indexing
55 Combining Fancy Index with Normal Slicing
56 Operations with Comparison Operators
57 Arithmetic Operations in Numpy
58 Statistical Operations in Numpy
59 Solving Second-Degree Equations with NumPy
60 Introduction to Pandas Library
61 Pandas Project Files Link
62 Creating a Pandas Series with a List
63 Creating a Pandas Series with a Dictionary
64 Creating Pandas Series with NumPy Array
65 Object Types in Series
66 Examining the Primary Features of the Pandas Series
67 Most Applied Methods on Pandas Series
68 Indexing and Slicing Pandas Series
69 Creating Pandas DataFrame with List
70 Creating Pandas DataFrame with NumPy Array
71 Creating Pandas DataFrame with Dictionary
72 Examining the Properties of Pandas DataFrames
73 Element Selection Operations in Pandas DataFrames Lesson 1
74 Element Selection Operations in Pandas DataFrames Lesson 2
75 Top Level Element Selection in Pandas DataFramesLesson 1
76 Top Level Element Selection in Pandas DataFramesLesson 2
77 Top Level Element Selection in Pandas DataFramesLesson 3
78 Element Selection with Conditional Operations in Pandas Data Frames
79 Adding Columns to Pandas Data Frames
80 Removing Rows and Columns from Pandas Data frames
81 Null Values ​​in Pandas Dataframes
82 Dropping Null Values Dropna() Function
83 Filling Null Values Fillna() Function
84 Setting Index in Pandas DataFrames
85 Multi-Index and Index Hierarchy in Pandas DataFrames
86 Element Selection in Multi-Indexed DataFrames
87 Selecting Elements Using the xs() Function in Multi-Indexed DataFrames
88 Concatenating Pandas Dataframes Concat Function
89 Merge Pandas Dataframes Merge() Function Lesson 1
90 Merge Pandas Dataframes Merge() Function Lesson 2
91 Merge Pandas Dataframes Merge() Function Lesson 3
92 Merge Pandas Dataframes Merge() Function Lesson 4
93 Joining Pandas Dataframes Join() Function
94 Loading a Dataset from the Seaborn Library
95 Examining the Data Set 1
96 Aggregation Functions in Pandas DataFrames
97 Examining the Data Set 2
98 Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes
99 Advanced Aggregation Functions Aggregate() Function
100 Advanced Aggregation Functions Filter() Function
101 Advanced Aggregation Functions Transform() Function
102 Advanced Aggregation Functions Apply() Function
103 Examining the Data Set 3
104 Pivot Tables in Pandas Library
105 Accessing and Making Files Available
106 Data Entry with Csv and Txt Files
107 Data Entry with Excel Files
108 Outputting as an CSV Extension
109 Outputting as an Excel File

(Optional) Recap, Exercises, and Bonus İnfo from the Numpy Library
110 What is Numpy
111 Why Numpy
112 Array and features in Python Numpy
113 Array’s Operators in Python Numpy
114 Numpy Functions in Python Numpy
115 Indexing and Slicing in Python Numpy
116 Numpy Exercises in Python Numpy

(Optional) Recap, Exercises, and Bonus İnfo from the Pandas Library
117 What is Pandas
118 Series and Features in Pandas
119 Data Frame attributes and Methods in Pandas
120 Groupby Operations in Pandas
121 Combining DataFrames I in Pandas
122 Combining DataFrames II in Pandas
123 Work with CSV Files in Pandas

Matplotlib
124 What is Matplotlib
125 Using Pyplot
126 Pyplot – Pylab – Matplotlib
127 Figure, Subplot and Axes
128 Figure Customization
129 Plot Customization
130 Grid, Spines, Ticks
131 Basic Plots in Matplotlib I
132 Basic Plots in Matplotlib II

Seaborn
133 What is Seaborn
134 Controlling Figure Aesthetics in Seaborn
135 Example in Seaborn
136 Color Palettes in Seaborn
137 Basic Plots in Seaborn
138 Multi-Plots in Seaborn
139 Regression Plots and Squarify in Seaborn

Geoplotlib
140 What is Geoplotlib
141 Example – 1
142 Example – 2
143 Example – 3

Extra
144 Data Visualization with Python Masterclass Python A-Z

Homepage

Resolve the captcha to access the links!