CF

Python Data Analysis & Visualization Masterclass

20h 17m 23s
English
Paid
Released
Added to CourseFlix

Python Data Analysis & Visualization Masterclass is a 199-lesson 20 hours 17 minutes self-paced course by Udemy. Welcome to (what I think is) the web's best course on Pandas, Matplotlib, Seaborn, and more!

Course facts

Lessons
199
Duration
20 hours 17 minutes
Language
English
Released
December 2021
Added to CourseFlix
April 2024
Instructor
Udemy
Price
Premium

Welcome to (what I think is) the web's best course on Pandas, Matplotlib, Seaborn, and more! This course will level up your data skills to help you grow your career in Data Science, Machine Learning, Finance, Web Development, or any tech-adjacent field. This is a tightly structured course that covers a ton, but it's all broken down into human-sized pieces rather than an overwhelming reference manual that throws everything at you at once.

After each and every new topic, you'll have the chance to practice what you're learning and challenge yourself with exercises and projects.

We work with dozens of fun and real-world datasets including Amazon bestsellers, Rivian stock prices, Presidential Tweets, Bitcoin historic data, and UFO sightings.

If you're still reading, let me tell you a little about the curriculum.. In the course, you'll learn how to:

  • Work with Jupyter Notebooks

  • Use Pandas to read and manipulate datasets

  • Work with DataFrames and Series objects

  • Organize, filter, clean, aggregate, and analyze DataFrames

  • Extract and manipulate date, time, and textual information from data

  • Master Hierarchical Indexing

  • Merge datasets together in Pandas

  • Create complex visualizations with Matplotlib

  • Use Seaborn to craft stunning and meaningful visualizations

  • Create line, bar, box, scatter, pie, violin, rug, swarm, strip, and other plots!

What makes this course different from other courses on the same topics?  First and foremost, this course integrates visualizations as soon as possible rather than tacking it on at the end, as many other courses do.  You'll be creating your first plots within the first couple of sections!  Additionally, we start using real datasets from the get go, unlike most other courses which spend hours working with dull, fake data (colors, animals, etc) before you ever see your first real dataset.  With all of that said, I feel bad trash talking my competitors, as there are quite a few great courses on the platform :) 

I think that about wraps it up! The topics in this courses are extremely visual and immediate, which makes them a joy to teach (and hopefully for you to learn).   If you have even a passing interest in these topics, you'll likely enjoy the course and tear through it quickly.  This stuff might seem intimidating, but it's actually really approachable and fun! I'm not kidding when I say this is my favorite course I've ever made. I hope you enjoy it too.

Who teaches Python Data Analysis & Visualization Masterclass? Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in Python Data Analysis & Visualization Masterclass?

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#1: Course Welcome & Curriculum Walkthrough
All Course Lessons (199)
#Lesson TitleDurationAccess
1
Course Welcome & Curriculum Walkthrough Demo
08:24
2
What Do You Need To Know To Take This Course?
01:50
3
Downloading The Course Materials IMPORTANT!!
02:39
4
How The Exercises Work
02:15
5
Introducing Jupyter Notebook!
05:33
6
Mac Installation Walkthrough
06:21
7
Windows Installation Walkthrough
06:39
8
"Installing" Pandas & Matplotlib (Mac & Windows)
04:08
9
Creating Notebooks & Running Cells
06:39
10
Shutting Down The Notebook Server
05:09
11
How Cell Output Works
02:32
12
Command Mode Shortcuts
06:21
13
Cell Types: Markdown Time!
04:57
14
Restarting The Kernel
06:48
15
Viewing The Docs Inside A Notebook
02:48
16
EXERCISE: Jupyter Notebook
02:43
17
SOLUTION: Jupyter Notebook
06:03
18
Datasets & CSV
05:32
19
pd.read_csv & DataFrames
06:43
20
Inspecting DataFrames: head(), tail(), etc.
07:18
21
DataTypes and info()
04:48
22
The House Sales Dataset Walkthrough
05:14
23
The Titanic Passenger Dataset Walkthrough
08:28
24
Non-comma Separators: Netflix Dataset
08:18
25
Overriding Headers: Country Population Dataset
04:19
26
EXERCISE: DataFrames & Datasets
03:11
27
SOLUTION: DataFrames & Datasets
08:49
28
Min & Max
05:25
29
Sum & Count
09:01
30
Mean, Median, & Mode
05:36
31
Describe With Numeric Values
04:24
32
Describe With Objects (Text) Values
07:48
33
EXERCISE: Basic DataFrame Methods
01:46
34
SOLUTION: Basic DataFrame Methods
04:36
35
Selecting A Single Column
07:22
36
A Closer Look At Series
08:32
37
Important Series Methods
05:11
38
unique & nunique
05:16
39
nlargest & nsmallest
07:16
40
Selecting Multiple Columns
03:43
41
The powerful value_counts() method
08:14
42
Using plot() to visualize!
10:51
43
EXERCISE: Series & Plotting
02:57
44
SOLUTION: Series & Plotting
08:50
45
Set_Index Basics
09:34
46
set_index: The World Happiness Index Dataset
05:07
47
setting index with read_csv
02:40
48
sort_values intro
03:55
49
sorting by multiple columns
03:06
50
sorting text columns
03:38
51
sort_index
02:22
52
Sorting and Plotting!
05:02
53
loc
07:51
54
iloc
04:19
55
loc & iloc with Series
05:52
56
EXERCISE: Indexes & Sorting
04:22
57
SOLUTION: Indexes & Sorting
09:56
58
Filtering DataFrames With A Boolean Series
08:49
59
Filtering With Comparison Operators
08:16
60
The Between Method
03:06
61
The isin() Method
04:08
62
Combining Conditions Using AND (&)
11:53
63
Combining Conditions Using OR (|)
11:09
64
Bitwise Negation
06:57
65
isna() and notna() Methods
03:37
66
Filtering + Plotting Examples
06:02
67
EXERCISE: Filtering
01:45
68
SOLUTION: Filtering Exercise
10:38
69
Dropping Columns
06:03
70
Dropping Rows
06:26
71
Adding Static Columns
06:00
72
Creating New "Dynamic" Columns
06:55
73
Finding The Highest price/sqft homes
04:02
74
Finding Largest Bitcoin Price Changes
05:15
75
EXERCISE: Adding/Removing Columns & Rows
03:19
76
SOLUTION: Adding/Removing Columns & Rows
05:11
77
Renaming Columns and Index Labels
04:51
78
The replace() method
07:32
79
Updating Values Using loc[]
08:00
80
Updating Multiple Values Using loc[]
04:12
81
Making Updates With loc[] and Boolean Masks
07:55
82
EXERCISE: Updating Values
02:21
83
SOLUTION: Updating Values Exercise
08:22
84
Casting Types With astype()
07:15
85
Introducing the Category Type
04:46
86
Casting With pd.to_numeric()
04:44
87
dropna() and isna()
08:39
88
fillna()
05:38
89
EXERCISE: Dealing With NA Values
01:22
90
SOLUTION: Dealing With NA Values
05:09
91
Why Dates Matter
03:43
92
Converting With pd.to_datetime()
08:07
93
Specifying Fancy Formats With pd.to_datetime()
09:06
94
Dates and DataFrames
07:08
95
The Useful dt Properties
08:50
96
Comparing Dates
06:15
97
Finding StarLink Flybys In UFO Dataset
08:44
98
Date Math & TimeDeltas
08:48
99
Billboard Charts Dataset Exploration
11:30
100
EXERCISE: Dates & Times
04:52
101
SOLUTION: Dates & Times
15:04
102
Intro to Matplotlib
04:25
103
Our First Matplotlib Plots!
07:05
104
Do We Need plt.show() ?
02:34
105
Anatomy of Plots
09:07
106
Figsize & Plot Dimensions
04:26
107
Changing Matplotlib Stylesheets
04:12
108
Line Styles, Colors, Widths, and More!
07:10
109
Plot Labels & Titles
06:01
110
Changing X & Y Ticks
07:07
111
Adding Legends To Plots
05:11
112
EXERCISE: Matplotlib Challenge #1
04:46
113
Creating Bar Plots
09:40
114
Creating Histograms
10:29
115
EXERCISE: Matplotlib Challenge #2
04:07
116
Creating Scatter Plots
04:42
117
Creating Pie Charts
05:43
118
EXERCISE: Matplotlib Challenge #3
04:28
119
Working With Subplots
10:54
120
Putting It All Together
05:55
121
EXERCISE: Matplotlib Challenge #4
09:37
122
A Pandas Plotting Recap
05:14
123
Changing Pandas Plot Styles
02:30
124
Adding Labels and Titles to Pandas Plots
07:47
125
Using rename() When Plotting
03:12
126
Closer Look at Pandas Bar Plots
07:30
127
EXERCISE: Pandas Plotting Challenge #1
08:02
128
Pandas Histograms
03:12
129
Box Plots
05:09
130
Pandas Line Plots
05:35
131
EXERCISE: Pandas Plotting Challenge #2
04:06
132
Pandas Scatter Plots
03:00
133
Multiple Plots On The Same Axes
05:12
134
UFOS Plotting Challenge!
07:14
135
EXERCISE: Pandas Plotting Challenge #3
03:57
136
Pandas Automatic Subplots
07:40
137
Manual Subplots With Pandas
06:27
138
EXERCISE: Pandas Plotting Challenge #4
11:23
139
EXERCISE: Pandas Plotting Challenge #5
10:35
140
Exporting Figures With savefig()
02:38
141
Introducing Groupby
05:42
142
Exploring Groups
09:42
143
Split-Apply-Combine
09:36
144
Using The Agg Method
07:42
145
Agg with Custom Functions
05:29
146
Named Aggregation
04:26
147
Groupby With Multiple Columns
07:13
148
Creating a MultiIndex With set_index
06:03
149
Sorting A MultiIndex
08:29
150
Using .loc[] With A MultiIndex
10:13
151
Cross Sections With The XS Method
02:31
152
get_level_values()
08:11
153
Hierarchical Columns
05:07
154
Stack() and Unstack()
03:49
155
Plotting With Unstack()
07:59
156
Grouping By Index
05:08
157
The String Datatype Vs. Object Datatype
07:04
158
Upper(), Lower(), and Capitalize()
04:10
159
Indexing String Series With []
05:54
160
Stripping Whitespace With Strip()
03:59
161
Splitting Text Values With Split()
06:58
162
Replacing Portions of Strings With Replace()
07:01
163
Testing Strings With Contains()
04:01
164
Applying Functions To Series
07:56
165
Apply() With Lambdas & Arguments
04:53
166
Apply() w/ DataFrames: Columns
04:13
167
Apply() w/ DataFrames: Rows
06:47
168
The Series Map() Method
02:57
169
The ApplyMap() Method
03:52
170
Concatenating Series
05:20
171
Concatenating Series By Index
04:10
172
Inner vs. Outer Joins
03:46
173
Concatenating DataFrames By Columns
04:48
174
Concatenating DataFrames By Index
03:04
175
The DataFrame Merge() Method
04:35
176
Merge() w/ Left, Right, Inner, & Outer Joins
06:12
177
Merge() On and Suffixes Arguments
09:42
178
Intro to Seaborn
08:15
179
The Helpful load_dataset() method
04:19
180
Seaborn Scatterplots
10:18
181
Seaborn Lineplots
12:27
182
The relplot() Method
09:19
183
Resizing Seaborn Plots: Aspect & Height
07:05
184
Seaborn Histograms
06:19
185
KDE Plots
02:45
186
Bivariate Distribution Plots
05:44
187
Rugplots
05:56
188
The Amazing displot() Method
06:59
189
Countplot
04:01
190
Strip & Swarm Plots
09:17
191
Boxplots
09:30
192
Boxenplots
02:24
193
Violinplots
04:48
194
Barplots
08:56
195
The Big Boy Catplot Method
08:30
196
Changing Seaborn Themes
04:29
197
Customizing Styles with set_style()
05:45
198
Altering Spines With despine()
02:53
199
Changing Color Palettes
09:15
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Frequently asked questions

What prior knowledge is required before taking this course?
Before enrolling in this course, you should have a basic understanding of programming concepts. Familiarity with Python will be beneficial as the course involves using Python libraries like Pandas, Matplotlib, and Seaborn for data analysis and visualization. The course includes lessons on setting up and using Jupyter Notebook, so no prior experience with Jupyter is necessary.
What projects or exercises will I work on during this course?
The course includes several exercises that help reinforce the concepts taught. Students will work on exercises involving Jupyter Notebook, DataFrames, basic DataFrame methods, Series and plotting, and indexes and sorting. These exercises are designed to provide practical experience in data analysis using real-world datasets like the House Sales dataset, the Titanic Passenger dataset, and the World Happiness Index dataset.
Who is the target audience for this course?
This course is suitable for individuals looking to enhance their data analysis and visualization skills. It is targeted towards data science enthusiasts, professionals in finance, machine learning practitioners, web developers, and anyone in tech-adjacent fields who wants to add data analysis to their skill set.
How does the scope of this course compare to similar courses?
This course covers a wide range of topics related to data analysis and visualization with Python, focusing on Pandas, Matplotlib, and Seaborn. Unlike some courses that may cover these tools as part of a broader curriculum, this course goes into detail about their application in data manipulation and visualization, offering exercises and real-world datasets to practice with.
What specific tools and platforms are used in this course?
The course primarily utilizes Python libraries such as Pandas for data manipulation, Matplotlib and Seaborn for visualization, and Jupyter Notebook as the interactive environment for coding. The installation of these tools is covered with walkthroughs for both Mac and Windows users, and the course materials are downloadable for hands-on practice.
What topics are not covered in this course?
This course focuses on data analysis and visualization using Pandas, Matplotlib, and Seaborn. It does not cover advanced machine learning techniques, deep learning, or other data science tools outside of these specific libraries. The course is designed to provide a solid foundation in data analysis and visualization rather than a comprehensive data science education.
What is the expected time commitment to complete this course?
The course consists of 199 lessons, and while the total runtime is not specified, students should expect to dedicate several hours per week to complete the coursework, including lectures, exercises, and practice with datasets. The course is structured in manageable segments to facilitate gradual learning without overwhelming students.