Algorithms Explained: Computational Complexity @DataDaft

DataDaft An overview of computational complexity including the basics of big O notation and common time complexities with examples of each.

Understanding computational complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if you don't implement algorithms yourself, an understanding of computational complexity can help you better apply the tools you use.

updated 5 years ago

Algorithms Explained: Computational Complexity

DataDaft 2021-03-12 | An overview of computational complexity including the basics of big O notation and common time complexities with examples of each.

Understanding computational complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if you don't implement algorithms yourself, an understanding of computational complexity can help you better apply the tools you use.

Data Visualization Tier List: Rating 50 Common Graphs

DataDaft 2022-01-04 | Tier list of 50 common data visualization techniques. Ratings are based upon how useful each plot is for data analysis.

As a tier list, the rankings here are subjective based on my opinions and experience. Also note that this is not an exhaustive list of data visualization techniques: I chose 50 of the more common data visualizations that I see and use, but I may not have included some of your favorites!

Thanks to all the subscribers for contributing to the channel's success in reaching 20,000 subs!

Note: This tier list was made via custom HTML and is therefore not reproducible or sharable as a template.

100M Olympic Medal Winning Times (1896-2020)

DataDaft 2021-08-01 | This plot shows the finishing times of Olympic medalists (Men and Women) in the 100 Meter dash over time. World records lines are included as a reference point. Note that wind-aided finishing times faster than the world record may occur.

* The name for the 2020 Men's gold winner should be Lamont JACOBS (I used his middle name Marcell by mistake while trying to get the video out as soon as possible after results came in).

Data Sources:
Wikipedia
Manual data collection for Tokyo 2020

Plotting:
flourish

Music:
Green Hill Zone (Sonic the Hedgehog) - GaMetal Remix:
youtube.com/watch?v=jbjkvsIrjVQ&ab_channel=GaMetal

Naruto Power Levels Over Time (1 Second = 1 Episode)

DataDaft 2021-06-14 | Naruto power levels from Naruto 1 through Shippuden 500. Every episode is displayed for approximately 1 second. Note that the video only shows power levels relevant to the current timeline (does not include flashbacks) and it skips long spans of filler episodes. Also note that the video shows a character’s current power level. This means a character that is strong but exhausted, injured or suppressing his power might appear lower than expected.

The concept of “power levels” are not a part of Naruto canon like they are in Dragonball, but I thought it would be fun to make a power level plot video similar to my Dragonball video, showing the flow of the story of Naruto over time as expressed through rough character power levels. Many viewers have expressed interest in such a video.

It is not possible to make a direct translation from Dragonball power levels to Naruto, since the concept of strength in the two shows is different. In Dragonball, power is fairly easy to quantify: the stronger a character is, the more speed, durability and more destructive power they have. In Naruto, power is more nuanced. Fights are more strategic and often based upon techniques like ninjutsu and genjutsu instead of brute force, DBZ style taijutsu and energy projection. The power levels here are meant to roughly fit into the DBZ conception of power level, but Naruto characters will generally have lower durability and raw destructive power than DBZ characters at similar power levels, while having more techniques and abilities.

Also note that one character having a higher power level than another does not necessarily mean they would win a fight. Certain characters counter others with abilities and some characters are able to fight well above their power level due to strategy and preparation. Some characters are capable of drastically raising their power levels for short bursts (chidori, rasengan, opening gates, etc.) which allows them to take down opponents with higher base power levels.

General guide to Naruto power level tiers in this video:

Power Level 0: Unconscious/Dead
Power Level 1-10: Normal Person
Power Level 10-100: Genin Level
Power Level 100-200: Chunin Level
Power Level 200-400: Jounin Level
Power Level 400-800: Jounin+ Level (Strong Jounin)
Power Level 800-1500: Kage Level
Power Level 1500-4000: Kage+ Level (Strong Kage)
Power Level 4000-10000: Demigod Level
Power Level 10000+: God Level

About the video:

Data: Custom hand-made data set
Plotting: flourish
Music:

1. Naruto - Strong And Strike (youtube.com/watch?v=57stJGfNN-I)
*Update* This song has been replaced with an audio library track due to a Content ID claim that blocked the video.

2. Kana Boon – Silhouette (youtube.com/watch?v=FswTx00PeOU&ab_channel=TreckMusicRandomTreckMusicRandom)

3. Track: Naruto Main Theme Remix (No Copyrights) Naruto Shippuden Anime Music, OST & Soundtrack [NCFS UPDATE]
Music provided by No Copyrights Free Sounds
Watch: youtu.be/EPhuRrHLPKI
Free Download / Stream: tii.ai/nN43

How To Use yield in Python

DataDaft 2021-05-05 | This video shows the basics of using the yield keyword to create generator functions in Python. Code used in this video is available in the description below.

Video on the basics of generators and generator expressions: youtube.com/watch?v=RvQkEaBMTJc&ab_channel=DataDaft

How to Use Generator Expressions in Python

DataDaft 2021-05-05 | This video goes over the basics of generators and generator expressions in Python. Generators are a useful way for creating sequences of values without storing the values in memory.

Video on using yield to make generator functions: youtube.com/watch?v=XBRlAxdUWDc&ab_channel=DataDaft

Code used in this video is available in the pinned comment below.

How to Use **kwargs in Python

DataDaft 2021-05-04 | This video goes over the basics of using the **kwargs construction in Python to allow a function to take any number of named arguments.

Code used in this video is available in the comments below!

How to Use *args in Python

DataDaft 2021-05-04 | This video goes over the basics of using the *args construction in Python to allow a function to take any number of unnamed arguments.

Code used in this video is available in the comments below!

How to Make (Define) a Function in Python

DataDaft 2021-05-03 | This video shows the basics of making and calling functions in Python and supplying functions with default arguments. Code available in the pinned comment below!

How to Use enumerate() in Python

DataDaft 2021-04-28 | Code available in comment below! This video shows the basics of how to use enumerate() in Python. enumerate() is useful for looping through data structures like lists while having access to each index and the corresponding value for each iteration of the loop.

characters = ["Krillin","Goku", "Vegeta", "Gohan", "Piccolo"]

# enumerate() returns a sequence of (index, item) tuples

list(enumerate(characters))

# Use enumerate() in a for loop to get an item and its index

for index, character in enumerate(characters):
print(index, character)

# Why might you want to use enumerate?
# Example: store index positions of duplicate items

characters = ["Krillin","Goku", "Goku", "Gohan", "Piccolo",
"Krillin","Goku", "Vegeta", "Gohan", "Piccolo",
"Piccolo","Goku", "Vegeta", "Goku", "Piccolo"]

character_map = {character:[] for character in set(characters)}

print(character_map)

# Use enumerate to store the index for each occurence
for index, character in enumerate(characters):
character_map[character].append(index)

character_map

Python Programming Practice: LeetCode #5 Longest Palindromic Substring

DataDaft 2021-04-27 | This episode of Python Programming Practice shows two approaches to LeetCode #5 -- Longest Palindromic Substring
leetcode.com/problems/longest-palindromic-substring

This is a medium difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

How To Make a While Loop in Python

DataDaft 2021-04-26 | This video describes the basics of making while loops in Python. Code available in the comments below!

How to Make a For Loop in Python

DataDaft 2021-04-25 | This video explains the basics of for loops in Python including looping over lists, numerical ranges, the continue keyword and the break keyword.

Game of Thrones: Balance of Power Over Time

DataDaft 2021-04-24 | Data visualization showing the balance of power in Game of Thrones over time . "Balance of Power" in this video essentially means how far a given character is from sitting on the iron throne at any point in time. Note that for all characters other than those actually sitting on the throne, the ranking is subjective. I gave strong precedence to characters with a valid claim to the throne based on lineage, followed by characters with close relations to the current ruler (hand of the king, trusted advisor, etc.) and powerful contenders seeking to claim the throne for themselves.

Data: Custom data set

Track: Game of Thrones Theme | DJD3 & BRANDS Remix
Music provided by "ByeByeCopyright".
Watch: youtu.be/7RkICW700GY
Free Download / Stream: http://bit.ly/GameOfThrones_Remix

Python Programming Practice: LeetCode #6 ZigZag Conversion

DataDaft 2021-04-23 | This episode of Python Programming Practice shows two approaches to LeetCode #6 -- ZigZag Conversion
leetcode.com/problems/zigzag-conversion

This is a medium difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

How to Generate Sequences in R

DataDaft 2021-04-22 | This video shows how to do basic sequence generation in R.

Code used in this video is available in the pinned comment below!

How to Select a Column in R

DataDaft 2021-04-21 | This video shows how to do the basic R operation of selecting columns.

Code used in this video is available in the pinned comment below!

How To Use rep() in R

DataDaft 2021-04-13 | This video shows how to use the rep() in R. rep() is a fast way to populate vectors with a repeated value or repeated sequence. Code used in this video can be seen the pinned comment below!

Python Programming Practice: LeetCode #20 Valid Parentheses

DataDaft 2021-04-09 | This episode of Python Programming Practice shows two approaches to LeetCode #20 -- Valid Parentheses
leetcode.com/problems/valid-parentheses

This is an easy difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

Python Programming Practice: LeetCode #17 Letter Combinations of a Phone Number

DataDaft 2021-04-08 | This episode of Python Programming Practice shows an approach to LeetCode #17 - Letter Combinations of a Phone Number
leetcode.com/problems/letter-combinations-of-a-phone-number

This is a medium difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

Python Programming Practice: LeetCode #13 Roman to Integer

DataDaft 2021-04-07 | This episode of Python Programming Practice shows two approaches to LeetCode LeetCode - #13 Roman to Integer
leetcode.com/problems/roman-to-integer

This is an easy difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

Python Programming Practice: LeetCode #69 Sqrt(x)

DataDaft 2021-04-05 | This episode of Python Programming Practice shows two approaches to LeetCode LeetCode - #69 Sqrt(x)
leetcode.com/problems/sqrtx

This is an easy difficulty problem.

Note: on line 7, the code should be "return x"!

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

Python Programming Practice: LeetCode #66 Plus One

DataDaft 2021-04-02 | This episode of Python Programming Practice shows two approaches to LeetCode #66 - Plus One
leetcode.com/problems/plus-one

This is an easy difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

Python Programming Practice: LeetCode #58 Length of Last Word

DataDaft 2021-04-01 | This episode of Python Programming Practice shows two approaches to LeetCode #58 - Length of Last Word:
leetcode.com/problems/length-of-last-word

This is an easy difficulty problem.

Note that the intent of this series to help viewers think through approaches to problems, not to provide copy/paste access to code that will pass LeetCode tests.

How To Get Absolute Values in Python

DataDaft 2021-03-26 | abs() is a Python built in function that finds the absolute value of a number. abs() can be combined with map() to get the absolute values of all numbers in a list and abs() can be run on numpy arrays and pandas columns.

Code used in this video:

# Check absolute value with abs()
x = -5
abs(x)

# Calculate the absolute value of every element in a list

x = [4, 5, -3, -6, -2, 6]

[*map(abs, x)]

# Calculate the absolute vaule of an array

import numpy as np

x = np.array(x)

abs(x)

# Get the absolute value of a Pandas column
import pandas as pd

df = pd.DataFrame({"col1":x})

print(df)

abs(df["col1"])

* Note: YouTube does not allow greater than or less than symbols in the text description, so the code above will not be exactly the same as the code shown in the video! I will use Unicode large ＜ and ＞ symbols in place of the standard sized ones.

How To Use all() and any() in Python

DataDaft 2021-03-25 | any() and all() are Python built in functions that let you check whether some iterable object like al ist contains any true values or all true values, respectively. This can be a useful operation when you want to check whether some large sequence conforms to a logical check. any() and all() also treat 0's as false and 1's as true, so they can be run on lists, arrays and pandas data frame columns encoded as indicator variables to check whether any or all of the values are equal to 1.

Code used in the video:

# Check if all elements of an iterable are true with all()

x = [True, True, True]

y = [True, False, True]

z = [False, False, False]

print(all(x))
print(all(y))
print(all(z))

# Check if any elements of an iterable are true with any()

print(any(x))
print(any(y))
print(all(z))

# any() and all() work on 0s and 1s
# 0 = False
# 1 = True

x = [0, 0, 0, 1]

print(any(x))
print(all(x))

# Useful when used with logical operations
import numpy as np

power_level_list = np.array([1000, 4000, 150, 9001, 1500])

any(power_level_list ＞ 9000)

* Note: YouTube does not allow greater than or less than symbols in the text description, so the code above will not be exactly the same as the code shown in the video! I will use Unicode large ＜ and ＞ symbols in place of the standard sized ones.

How To Use zip() in Python

DataDaft 2021-03-24 | zip() is a Python built in that lets you combine two or more iterable objects like lists into groups of tuples. zip() is a convenient way of grouping data in multiple lists together in an elementwise fashion.

*Note: Longitude is misspelled in the video! I corrected this in the code below.*

Code used in the video:

# Use zip() to combine iterables like lists into tuples (elementwise)
# Useful for making separate lists into tuples

latitude = [4,5,6,4,6,2,4,5,6]
longitude = [6,3,6,3,4,5,6,8,5]

[*zip(latitude, longitude)]

# Can operate on more than 2 inputs
altitude = [12,41,15,16,15,23,14,51,61]

[*zip(latitude, longitude, altitude)]

# Zip will only continue up to the length of the shortest input

short = [1,2,3,4]
long = [1,2,3,4,5,6,7,8]

[*zip(short, long)]

# If you want to keep all items, use itertools.zip_longest
from itertools import zip_longest

short = [1,2,3,4]
long = [1,2,3,4,5,6,7,8]

[*zip_longest(short, long, fillvalue=None)]

* Note: YouTube does not allow greater than or less than symbols in the text description, so the code above will not be exactly the same as the code shown in the video! I will use Unicode large ＜ and ＞ symbols in place of the standard sized ones.

How To Use map() in Python

DataDaft 2021-03-24 | map() is a Python built in that lets you apply or "map" a function to each element in a list or iterable object. map() is one of the most useful Python built in functions for data analytics, since you will often be working with large sequence data types that need to be transformed in various ways.

Code used in the video:

# Map lets you apply a function to each element of a list

power_level_list = [1000, 4000, 16000, 150, 9001, 1500]

def greater_than_9000(x):
if x ＞ 9000:
return True
return False

mapped = map(greater_than_9000, power_level_list)

print(mapped)

# Print each mapping

for m in mapped:
print(m)

# Extract mapping into new list

mapped_list = [*map(greater_than_9000, power_level_list)]

mapped_list

# Use map with a lambda function

[*map(lambda x: x ＞ 9000, power_level_list)]

* Note: YouTube does not allow greater than or less than symbols in the text description, so the code above will not be exactly the same as the code shown in the video! I will use Unicode large ＜ and ＞ symbols in place of the standard sized ones. .

Videos on Pandas map() operations:

How To Use apply() In Pandas (Python)
youtube.com/watch?v=smPLY_5gVv4&ab_channel=DataDaft

How To Use applymap() In Pandas (Python)
youtube.com/watch?v=azF6O_mzaCI&ab_channel=DataDaft

Algorithms Explained: Memory Complexity

DataDaft 2021-03-18 | An overview of memory (space) complexity including the basics of big O notation and common space complexities with examples of each.

Understanding memory complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if you don't implement algorithms yourself, an understanding of memory complexity can help you better apply the tools you use.

Algorithms Explained: Computational Complexity:
youtube.com/watch?v=47GRtdHOKMg&ab_channel=DataDaft

Algorithms Explained: What is an Algorithm?

DataDaft 2021-03-11 | This video defines what an algorithm is, distinguishes algorithms from recipes and functions and gives some examples of algorithms.

This is the first video in an "Algorithms Explained" series that discusses algorithms at a conceptual level. Videos in this series that discuss specific algorithms may also include or link to concrete code implementations, likely in Python.

Tallest Buildings In History To Scale (1 pixel = 1 meter)

DataDaft 2021-02-26 | Top 10 tallest buildings in history over time (3200BC to 2021) using to-scale building images where 1 pixel on the screen is equal to 1 meter when viewed in full 1080p quality.

Building height for this video is generally "architectural height", meaning the tallest of roof & spire height. The data does not count antennas, which can make certain buildings appear taller than others even if they rank lower. Also note that communication towers and other non-building, non-ancient structures were not included in the plot.

Note that the data set only has one image per building but some buildings change height over time either due to damage or additions. In those cases the building's size may not exactly align to scale with others.

Also note that years in which the top 10 buildings in the world did not change were excluded from the plot. There were some years (and long periods) in history with no new tall buildings in the top 10; skipping those years was the only good way I found to keep the graph moving and interesting.

Data: Custom data set compiled from various sources including:
The Skyscraper Center (skyscrapercenter.com)
SkyscraperPage.com
Wikipedia

Images:
SkyscraperPage.com (Special thanks for to-scale drawings!)

Plotting:
No existing data visualization tools were capable of making this plot the way I wanted, so I made this as a custom data viz in the browser with JavaScript.

Music:
Toccata and Fugue in D Minor Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 3.0 License
http://creativecommons.org/licenses/by/3.0

Introduction to R: Random Forests

DataDaft 2021-02-12 | This lesson covers the basics of random forests in R.

This is lesson 30 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Random Forests
kaggle.com/hamelg/intro-to-r-part-30-Random-Forests

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: Decision Trees

DataDaft 2021-02-12 | This lesson covers the basics of decision trees in R.

This is lesson 29 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Decision Trees
kaggle.com/hamelg/intro-to-r-part-29-Decision-Trees

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: Logistic Regression

DataDaft 2021-02-05 | This lesson covers the basics of logistic regression in R.

This is lesson 28 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Logistic Regression
kaggle.com/hamelg/intro-to-r-part-28-Logistic-Regression

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: Linear Regression

DataDaft 2021-02-04 | This lesson covers the basics of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models.

This is lesson 27 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Linear Regression
kaggle.com/hamelg/intro-to-r-part-27-Linear-Regression

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: ANOVA

DataDaft 2021-02-03 | This is lesson 26 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: ANOVA
kaggle.com/hamelg/intro-to-r-part-26-ANOVA

This lesson covers the ANOVA test for comparing whether the mean of a numeric variable differs depending on the value one a categorical variable. This lesson covers one-way ANOVA tests for comparing across a single categorical variable, and two-way ANOVA tests for comparing across two categorical variables and their interactions.

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: Chi-Squared Tests

DataDaft 2021-02-02 | This is lesson 25 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Chi-Squared Tests
kaggle.com/hamelg/intro-to-r-part-25-Chi-Squared-Tests

This lesson covers the chi-squared goodness of fit test and the chi-squared test of independence. The goodness of fit test checks whether a categorical variable in a sample differs from a population. The test of independence tests whether two categorical variables are independent of one another.

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: T-Tests (Hypothesis Testing)

DataDaft 2021-02-01 | This is lesson 24 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Hypothesis Testing
kaggle.com/hamelg/intro-to-r-part-24-Hypothesis-Testing

This lesson covers statistical hypothesis testing and the t-test. The t-test is a foundational statistical inference test that is the building block of many common data science techniques, such as A/B testing.

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

Introduction to R: Confidence Intervals

DataDaft 2021-01-29 | This is lesson 23 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Confidence Intervals
kaggle.com/hamelg/intro-to-r-part-23-confidence-intervals

This lesson covers point estimates and confidence intervals as basic statistical techniques to gain insight into populations of interest through sample data.

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

The Meta MOOC: Getting the Most out of Online Learning

DataDaft 2021-01-18 | This is a self-contained mini course sharing tips and strategies for online learning based on my experiences taking online courses over the past 10 years.

The web is the best place to learn about programming and data science. My hope is that the content presented in this video will be useful to those interested in data science, since MOOCs are a great source of technical learning content, while also being accessible and useful to people who may be interested in online learning in general.

Chapters:
0:00 Introduction
0:37 Pros and Cons of Online Learning
6:42 My MOOC Background
7:50 Online Learning Platforms
12:44 Learning Objective
16:00 Finding & Choosing Courses
21:52 To Pay or Not to Pay
25:21 Learning Strategy
29:41 Reading Content
31:30 Lecture Content
34:40 Interactive Content
36:36 Assignments
38:27 Exams & Grades
40:12 Collaboration
42:35 Getting Help
45:33 Dropping Courses
48:00 MOOC Credentials
1:00:14 Retention and Application
1:03:06 Conclusion

If you are interested in learning programming and/or data analytics consider checking out my Python and R playlists:

Python: youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc
R: youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

To search for online courses, I recommend Class Central:
classcentral.com

Python Programming Practice: LeetCode #16 3Sum Closest

DataDaft 2021-01-12 | In this episode of Python Programming Practice: LeetCode #16 -- 3Sum Closest

Link to the problem here:
leetcode.com/problems/3sum-closest

This video builds upon the solution to 3 Sum covered in this video:
youtube.com/watch?v=hNRS81I1OZ8&ab_channel=DataDaft

If you don't know Python, you can learn the basics of Python for data analysis by following along with this YouTube guide I created: youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Python Programming Practice is a series focused on teaching practical coding skills by solving exercises on popular coding websites. Note that the solutions seen here may not be the most efficient possible.

I am not going to provide the full code in the video description for this series, since copy and pasting solutions is not in the spirit of doing coding exercises. It is intended that the video will help you think about problems, approaches and how to structure solutions so that you are able to code up a working solution yourself.

Python Programming Practice: Leetcode #15 3Sum

DataDaft 2021-01-08 | In this episode of Python Programming Practice: LeetCode #15 -- 3Sum

Link to the problem here:
leetcode.com/problems/3sum

If you don't know Python, you can learn the basics of Python for data analysis by following along with this YouTube guide I created: youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Python Programming Practice is a series focused on teaching practical coding skills by solving exercises on popular coding websites. Note that the solutions seen here may not be the most efficient possible.

I am not going to provide the full code in the video description for this series, since copy and pasting solutions is not in the spirit of doing coding exercises. It is intended that the video will help you think about problems, approaches and how to structure solutions so that you are able to code up a working solution yourself. .

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only

Top 150 One Piece Characters by Panel Appearances (Through Chapter 1000)

DataDaft 2021-01-04 | To celebrate the 1000th chapter of One Piece, I created this data visualization showing the top 150 one piece characters ranked by the number of manga panels that they have appeared in throughout the series through chapter 1000.

This is a channel devoted to all things data science from fun data viz videos like this one, to technical coding guides and how-to videos. If you liked this video consider subscribing! ► youtube.com/c/DataDaft?sub_confirmation=1

Data Set:
Pieced together from several online sources as well as manual collection from the One Piece manga for certain parts of the Wano arc. Please note that the data set is likely not 100% accurate as getting an accurate count on all panels is difficult and I'm relying on counts other people have made in the past. Also note that certain characters that appeared in some arcs before Wano but who were ranked outside the top ~170 and have subsequently reappeared may not appear on this chart despite possibly having enough panels that they should appear. Characters this might apply to include: Roger, Drake and Apoo. Also note that some Wano-only characters that do not appear in the video will likely have enough panels to rank in the top 150 by the end of the Wano arc. Characters this could apply to include: Yamato, Page One, Ulti and King.

Main sources:
reddit.com/r/OnePiece/comments/ae53et/top_50_characters_with_most_manga_panels_time
sporcle.com/games/Arturo30/one-piece-characters-by-panel-appearances-1
aminoapps.com/c/one-piece/page/blog/wanos-panel-time-as-of-ch-986/5XPg_58hVuarB55jaweB5bV5XK14lqLEGo

Plotting:
flourish

Music:
Wano Theme - Edit by Sanji G66 (youtube.com/watch?v=EEyispEWjsA&t=1s&ab_channel=SanjiG66)

Python for Data Analysis: Random Forests

DataDaft 2020-12-31 | This video covers the basics of random forests and how to make random forest models for classification in Python.

Subscribe:
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This is lesson 30 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Random Forests
kaggle.com/hamelg/python-for-data-30-random-forests

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index

Introduction to R Playlist:
youtube.com/playlist?list=PLiC1doDIe9rDjk9tSOIUZJU4s5NpEyYtE

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only

Python for Data Analysis: Decision Trees

DataDaft 2020-12-30 | This video covers the basics of decision trees and how to make decision trees for classification in Python.

Subscribe:
► youtube.com/c/DataDaft?sub_confirmation=1

This is lesson 29 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Decision Trees
kaggle.com/hamelg/python-for-data-29-decision-trees

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only

Python for Data Analysis: Logistic Regression

DataDaft 2020-12-29 | This video covers the basics of logistic regression and how to perform logistic regression in Python.

Subscribe:
► youtube.com/c/DataDaft?sub_confirmation=1

This is lesson 28 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Logistic Regression
kaggle.com/hamelg/python-for-data-28-logistic-regression

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only

Python for Data Analysis: Linear Regression

DataDaft 2020-12-21 | This video covers the basics of linear regression and how to perform linear regression in Python.

Subscribe:
► youtube.com/c/DataDaft?sub_confirmation=1

This is lesson 27 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Linear Regression
kaggle.com/hamelg/python-for-data-27-linear-regression

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index

Python for Data Analysis: ANOVA

DataDaft 2020-12-17 | This video covers the basics of how to perform ANOVA tests in Python.

Subscribe:
► youtube.com/c/DataDaft?sub_confirmation=1

This is lesson 26 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: ANOVA
kaggle.com/hamelg/python-for-data-26-anova

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only

Python for Data Analysis: Chi-Squared Tests

DataDaft 2020-12-16 | This video covers the basics of how to perform chi-squared tests in Python.

Subscribe:
► youtube.com/c/DataDaft?sub_confirmation=1

This is lesson 25 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Chi-Squared Tests
kaggle.com/hamelg/python-for-data-25-chi-squared-tests

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index .

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only

Python for Data Analysis: Hypothesis Testing and T-Tests

DataDaft 2020-12-15 | This video covers the basics of statistical hypothesis testing and t-tests in Python. This video explains the basics of statistical hypothesis testing and shows how to run one-way, two-way and paired t-tests in Python.

Subscribe:
► youtube.com/c/DataDaft?sub_confirmation=1

This is lesson 24 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Hypothesis Testing and T-Tests
kaggle.com/hamelg/python-for-data-24-hypothesis-testing

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
kaggle.com/hamelg/python-for-data-analysis-index .

⭐ Kite is a free AI-powered coding assistant that integrates with popular editors and IDEs to give you smart code completions and docs while you’re typing. It is a cool application of machine learning that can also help you code faster! Check it out here: kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=datadaft&utm_content=description-only