English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 22 Hours | 6.52 GB
Visualisation in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.
Are you ready to start your path to becoming a Data Scientist!
KGP Talkie brings you all in one course. Learn all kinds of Data Visualization with practical datasets.
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations!
This is a very unique course where you will learn EDA on Kaggle’s Boston Housing, Titanic and Latest Covid-19 Datasets, Text Dataset, IPL Cricket Matches of all seasons, and FIFA world cup matches with real and practical examples.
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $110,000 in the United States and all over the World according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 200+ Full HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive courses on Complete Data Visualization in Python.
We’ll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project.
Here just a few of the topics we will be learning:
- Programming with Python
- NumPy with Python
- Using Pandas Data Frames to solve complex tasks
- Use Pandas to Files
- Use matplotlib and Seaborn for data visualizations
- Use Plotly and Cufflinks for interactive visualizations
- Exploratory Data Analysis (EDA) of Boston Housing Dataset
- Exploratory Data Analysis (EDA) of Titanic Dataset
- Exploratory Data Analysis (EDA) of Latest Covid-19 Dataset
- and much, much more!
By the end of this course you will:
- Have an understanding of how to program in Python.
- Know how to create and manipulate arrays using numpy and Python.
- Know how to use pandas to create and analyze data sets.
- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.
- Have an amazing portfolio of python data analysis skills!
- Have experience of creating a visualization of real-life projects
What you’ll learn
- Learn Complete Exploratory Data Analysis on the Latest Covid-19 Dataset
- Learn EDA on Kaggle’s Boston Housing and Titanic Datasets
- Learn IPL Cricket Matches and FIFA World Cup Matches Analysis and Visualization
- Learn Data Visualization by Plotly and Cufflinks, Seaborn, matplotlib, and Pandas
- Learn Interactive plots and visualization
- Installation of python and related libraries.
- Covid-19 Data Visualization
- Covid-19 Dataset Analysis and Visualization in Python
- Data Science Visualization with Covid-19
- Use the Numpy and Pandas in data manipulation
- Learn Complete Text Data EDA
- Create a variety of charts, Bar Charts, Line Charts, Stacked Charts, Pie Charts, Histograms, KDE plots, Violinplots, Boxplots, Auto
- Correlation plots, Scatter Plots, Heatmaps
- Learn Data Analysis by Pandas.
- Use the Pandas module with Python to create and structure data.
- Customize graphs, modifying colors, lines, fonts, and more
Table of Contents
Introduction
1 Welcome
2 Free Resources!!!
3 Introduction
4 Q&A Support
5 Course Overview
6 Anaconda Installation for Windows OS
7 Anaconda Installation for Mac OS
8 Anaconda Installation on Ubuntu OS
9 Jupyter Notebook Keyboard Shortcuts
10 Jupyter Notebook Shortcuts Article
Python Crash Course
11 Introduction
12 Data Types Numbers
13 Variable Assignment
14 String
15 List
16 Set
17 Tuple
18 Dictionary
19 Boolean and Comparison Operator
20 Logical Operator
21 Conditional Statements If Else and Elif
22 For and While Loops in Python
23 Methods and Lambda Functions
NumPy Crash Course
24 Introduction
25 Array
26 NaN and INF
27 Statistical Operations
28 Shape, Reshape, Ravel, Flatten
29 Sequence, Repetitions, and Random Numbers
30 Where
31 File Read and Write
32 Concatenate and Sorting
33 Working with Dates
Pandas Crash Course
34 Introduction
35 DataFrame and Series
36 File Reading and Writing
37 Info, Shape, Duplicated, and Drop
38 Columns
39 NaN and Null Values
40 Imputation
41 Lambda Function
Data Visualization with Pandas
42 Introduction
43 Data Generation
44 Line Plot
45 More on Line Plot
46 Bar Plot
47 Stacked Plot
48 Histogram
49 Box Plot
50 Area and Scatter Plot
51 Hex and Pie Plot
52 Scatter Matrix and Subplots
Matplotlib
53 Introduction
54 Line Plot
55 Label
56 Scatter, Bar, and Hist Plots
57 Box Plot
58 Subplot
59 xlim, ylim, xticks, and yticks
60 Pie Plot
61 Pie Plot Text Color
62 Nested Pie Plot
63 Labeling a Pie Plot
64 Bar Chart on Polar Axis
65 Line Plot on a Polar Axis
66 Scatter Plot on a Polar Axis
67 Integral in Calculus Plot as Area Under the Curve
68 Animation Plot Part 1
69 Animation Plot Part 2
Time Series Plots
70 Dataset Loading
71 Line and Scatter Plots
72 Subplots
73 Heatmap
74 Histogram and KDE Plots
Seaborn
75 Introduction
76 Scatter Plot
77 Hue, Style and Size Part 1
78 Hue, Style and Size Part 2
79 Line Plot Part 1
80 Line Plot Part 2
81 Line Plot Part 3
82 Subplot
83 sns.lineplot(), sns.scatterplot()
84 Cat Plot
85 Box Plot
86 Boxen Plot
87 Violin Plot
88 Bar Plot
89 Point Plot
90 Joint Plot
91 Pair Plot
92 Regression Plot
93 Controlling Plotted Figure Aesthetics
Plotly and Cufflinks
94 Introduction
95 Installation and Setup
96 Line Plot
97 Scatter Plot
98 Bar Plot
99 Box Plot and Area Plot
100 D Plot
101 Spread Plot and Hist Plot
102 Bubble Plot and Heatmap
Analysis and Visualization of Boston Housing Data
103 Introduction
104 Data Preparation
105 Data Deep Dive
106 pd.describe()
107 Bar Plot
108 Plot Styling
109 Pair Plot
110 Distribution Plot
111 Scatter Plot
112 Heatmap
113 Correlated Feature Selection
114 Heatmap and Pair Plot of Correlated Data
115 Box and Rel Plot
116 Joint Plot Part 1
117 Joint Plot Part 2
118 Linear Regression without ML Part 1
119 Linear Regression without ML Part 2
Analysis and Visualization of Titanic Dataset
120 Introduction
121 Data Understanding
122 Load Dataset
123 Heatmap
124 Univariate Analysis
125 Survived
126 Pclass Part 1
127 Pclass Part 2
128 Sex Part 1
129 Sex Part 2
130 Sex Part 3
131 Sex Part 4
132 Sex Part 5
133 Age Part 1
134 Age Part 2
135 Age Part 3
136 Age Part 4
137 Fare Part 1
138 Fare Part 2
139 Fare Part 3
140 Fare Part 4
141 Sibsp Part 1
142 Sibsp Part 2
143 Sibsp Part 3
144 Sibsp Part 4
145 Parch Part 1
146 Parch Part 2
147 Embarked
148 Who
Analysis and Visualization of Covid-19 Data
149 Introduction
150 Data Understanding
151 Import Packages
152 Clone Latest Covid-19 Dataset
153 Import Cleaned Covid-19 Dataset
154 Import Preprocessed Data
155 Scatter Plot for Confirmed Cases
156 Cases Timelaps on Worldmap
157 Total Cases on Ships
158 Cases Over the Time with Area Plot Part 1
159 Cases Over the Time with Area Plot Part 2
160 Covid-19 Cases on Folium Map
161 Confirmed Cases with Animation
162 Confirmed and Death Cases with Bar Plot
163 Confirmed and Death Cases with Colormap
164 Deaths per 100 Cases
165 New Cases and Countries per Day
166 Correction in Top 15 Countries Case Analysis Part 1
167 Top 15 Countries Case Analysis Part 1
168 Top 15 Countries Case Analysis Part 2
169 Top 15 Countries Case Analysis Part 3
170 Top 15 Countries Case Analysis Part 4
171 Top 15 Countries Case Analysis Part 5
172 Save Figures in PNG, JPEG, and PDF
173 Scatter Plot for Deaths vs Confirmed Cases
174 Stacked Bar Plot
175 Stacked Line Plot
176 Growth Rate After 100 Cases
177 Growth Rate After 1000 Cases
178 Growth Rate After 10000 Cases
179 Growth Rate After 100k Cases
180 Tree Map Analysis
181 First and Last Case Report Time Part 1
182 First and Last Case Report Time Part 2
183 First and Last Case Report Time Part 3
184 Confirmed Cases by Country and Daywise
185 Covid-19 vs Other Epidemics
Analysis and Visualization of Reviews Text Data
186 Introduction
187 Getting Started
188 Data Import
189 Data Cleaning
190 Feature Engineering
191 Distribution of Sentiment Polarity
192 Distribution of Reviews Rating and Reviewers Age
193 Distribution of Review Text Length and Word Length
194 Distribution of Department, Division, and Class
195 Distribution of Unigram, Bigram and Trigram Part 1
196 Distribution of Unigram, Bigram and Trigram Part 2
197 Distribution of Unigram, Bigram and Trigram without STOP WORDS
198 Distribution of Top 20 Parts-of-Speech POS tags
199 Bivariate Analysis Part 1
200 Bivariate Analysis Part 2
201 Bivariate Analysis Part 3
Analysis and Visualization of IPL Cricket Matches
202 Introduction
203 About Cricket Matches and Package Import
204 Data Understanding
205 Wins and Lost Matches Analysis
206 MoM, City and Venue wise Analysis
207 MI vs CSK Head to Head Matches
208 Seasonwise Analysis
209 Ball by Ball Analysis
Analysis and Visualization of FIFA World Cup Matches
210 Introduction
211 FIFA World Cup Data Import
212 Data Cleaning
213 Most Number of World Cup Winning Title
214 Number of Goal Per Country
215 Attendance, Number of Teams, Goals, and Matches per Cup
216 Goals Per Team Per Word Cup
217 Matches with Highest Number of Attendance
218 Stadiums with Highest Average Attendance
219 Match Outcomes by Home and Away Teams
Python Coding in Mobile
220 Introduction
221 Python in Mobile
222 Matplotlib Plot in Mobile
223 Pandas Coding in Mobile
224 Seaborn Coding in Mobile
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