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Udemy | Build an NBA Fantasy Projection Model in Python with Pandas [FTU]

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Udemy | Build an NBA Fantasy Projection Model in Python with Pandas [FTU] (Size: 2.18 GB)
  0. Websites you may like
  0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url 377 B
  1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url 328 B
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  4. (FTUApps.com) Download Cracked Developers Applications For Free.url 239 B
  How you can help Team-FTU.txt 229 B
  1. Introduction
  1. Course Intro.mp4 45.6 MB
  1. Course Intro.vtt 7.98 KB
  2. Installation.mp4 21.77 MB
  2. Installation.vtt 4.51 KB
  3. Virtual Environments.mp4 24.72 MB
  3. Virtual Environments.vtt 4.53 KB
  4. Modules & Packages.mp4 41.31 MB
  4. Modules & Packages.vtt 5.54 KB
  5. Resources.html 140 B
  5.1 Cleaning the Glass.html 90 B
  5.2 Homebrew.html 77 B
  5.3 Github.html 104 B
  5.4 Xcode.html 77 B
  5.5 Pyenv.html 104 B
  2. Jupyter Notebook
  1. Setting Up Jupyter Notebook.mp4 29.41 MB
  1. Setting Up Jupyter Notebook.vtt 6.73 KB
  2. Cell Types & Kernels.mp4 19.22 MB
  2. Cell Types & Kernels.vtt 4.38 KB
  3. Running Code.mp4 18.68 MB
  3. Running Code.vtt 4.18 KB
  4. Resources.html 140 B
  4.1 Getting Started with Jupyter Notebook for Python.html 168 B
  4.2 Jupyter Notebook for Beginners.html 117 B
  4.3 Project Jupyter.html 81 B
  3. Python Fundamentals
  1. Python Overview.mp4 2.87 MB
  1. Python Overview.vtt 1 KB
  10. Arrays.mp4 14.17 MB
  10. Arrays.vtt 2.83 KB
  11. Resources.html 140 B
  11.1 Python For Loops.html 114 B
  11.2 Data Science from Scratch First Principles with Python.html 136 B
  11.3 Automate the Boring Stuff with Python.html 96 B
  2. Importing Modules.mp4 15.77 MB
  2. Importing Modules.vtt 3.21 KB
  3. Printing.mp4 10.59 MB
  3. Printing.vtt 2.18 KB
  4. Variables & Raw Input.mp4 10.18 MB
  4. Variables & Raw Input.vtt 2.11 KB
  5. Lists.mp4 29.8 MB
  5. Lists.vtt 5.67 KB
  6. Dictionaries.mp4 18.44 MB
  6. Dictionaries.vtt 3.18 KB
  7. For Loops.mp4 23.59 MB
  7. For Loops.vtt 5 KB
  8. If Else Statements.mp4 28.34 MB
  8. If Else Statements.vtt 6.39 KB
  9. Functions.mp4 17.03 MB
  9. Functions.vtt 3.36 KB
  4. Pandas Building Blocks
  1. Pandas Intro.mp4 13.32 MB
  1. Pandas Intro.vtt 2.04 KB
  10. Adding & Dropping Rows.mp4 43.76 MB
  10. Adding & Dropping Rows.vtt 7.72 KB
  11. Inplace Parameter.mp4 53.08 MB
  11. Inplace Parameter.vtt 5.41 KB
  12. Sorting Dataframes.mp4 55.56 MB
  12. Sorting Dataframes.vtt 6.26 KB
  13. Filtering Dataframes.mp4 92.03 MB
  13. Filtering Dataframes.vtt 9.27 KB
  14. Groupby.mp4 51.1 MB
  14. Groupby.vtt 6.81 KB
  15. Concatenate & Append.mp4 34.74 MB
  15. Concatenate & Append.vtt 5.53 KB
  16. Merging & Joining.mp4 89.37 MB
  16. Merging & Joining.vtt 11.48 KB
  17. Iterating Over Dataframes.mp4 17.54 MB
  17. Iterating Over Dataframes.vtt 3.01 KB
  18. Applying Functions.mp4 61.68 MB
  18. Applying Functions.vtt 8.58 KB
  19. Arrays.mp4 20.92 MB
  19. Arrays.vtt 4.59 KB
  2. Dataframes & Series.mp4 8.36 MB
  2. Dataframes & Series.vtt 1.94 KB
  20. Resources.html 140 B
  20.1 Join & Merge Pandas Dataframes - Chris Albon.html 134 B
  20.2 Using iloc, loc, & ix to select rows and columns in Pandas DataFrames.html 149 B
  20.3 A Quick Introduction to the “Pandas” Python Library.html 154 B
  20.4 Python Numpy Array Tutorial.html 127 B
  20.5 Dropping Rows of Data Using Pandas.html 114 B
  20.6 Intro to Pandas Data Structures.html 134 B
  20.7 Python Pandas Dataframes.html 133 B
  20.8 Selecting Subset of Data in Pandas - Ted Petrou.html 140 B
  3. Creating a Dataframe.mp4 39.96 MB
  3. Creating a Dataframe.vtt 6.45 KB
  4. Reading a CSV File.mp4 37.62 MB
  4. Reading a CSV File.vtt 6.03 KB
  5. Attributes & Methods.mp4 58.02 MB
  5. Attributes & Methods.vtt 8.73 KB
  6. Selecting Columns.mp4 46.84 MB
  6. Selecting Columns.vtt 8.94 KB
  7. Adding & Deleting Columns.mp4 79.89 MB
  7. Adding & Deleting Columns.vtt 8.24 KB
  8. Renaming Columns.mp4 26.76 MB
  8. Renaming Columns.vtt 4.56 KB
  9. Selecting Rows.mp4 30.76 MB
  9. Selecting Rows.vtt 4.42 KB
  5. Building Our Model
  1. Projection Model Overview.mp4 24.05 MB
  1. Projection Model Overview.vtt 5.04 KB
  10. Player Comparison Function.mp4 76.44 MB
  10. Player Comparison Function.vtt 10.82 KB
  11. Projecting 2018-19 Season Stats.mp4 73.21 MB
  11. Projecting 2018-19 Season Stats.vtt 10.13 KB
  12. Resources.html 140 B
  12.1 A Quick Introduction to K-Nearest Neighbors Algorithm.html 155 B
  12.2 NBA Math FATS Model.html 92 B
  12.3 Tutorial K Nearest Neighbors in Python.html 121 B
  12.4 Comparing NBA Stars to their Statistical Contemporaries An Application of Machine Learning.html 185 B
  2. Cleaning NBA Data.mp4 51.1 MB
  2. Cleaning NBA Data.vtt 10.29 KB
  3. Normalizing Season Data.mp4 70.34 MB
  3. Normalizing Season Data.vtt 10.65 KB
  4. Player Distance Function.mp4 38.56 MB
  4. Player Distance Function.vtt 6.84 KB
  5. Find Player Function.mp4 43.5 MB
  5. Find Player Function.vtt 5.34 KB
  6. Calculating Player Similarity.mp4 63.83 MB
  6. Calculating Player Similarity.vtt 8.94 KB
  7. Comparing Multiple Players in a For Loop.mp4 27.23 MB
  7. Comparing Multiple Players in a For Loop.vtt 4.22 KB
  8. Weighting Stat Columns.mp4 69.53 MB
  8. Weighting Stat Columns.vtt 13.11 KB
  9. Weighted Avg. Using Multiple Players Next Season.mp4 44.39 MB
  9. Weighted Avg. Using Multiple Players Next Season.vtt 7.42 KB
  6. Measuring Our Model
  1. Using RSME to Evaluate Our Model.mp4 75.44 MB
  1. Using RSME to Evaluate Our Model.vtt 10.61 KB
  2. Comparing to Competitors Pt. 1.mp4 62.67 MB
  2. Comparing to Competitors Pt. 1.vtt 9.88 KB
  3. Comparing to Competitors Pt. 2.mp4 39.93 MB
  3. Comparing to Competitors Pt. 2.vtt 5.29 KB
  4. Adjusting Variables.mp4 14.65 MB
  4. Adjusting Variables.vtt 2.11 KB
  5. Resources.html 140 B
  5.1 Python Machine Learning Tutorial Predicting Airbnb Prices.html 117 B
  7. Winning Your Fantasy League
  1. Converting to Fantasy Points.mp4 49.62 MB
  1. Converting to Fantasy Points.vtt 6.61 KB
  2. Value Based Drafting.mp4 19.56 MB
  2. Value Based Drafting.vtt 5.11 KB
  3. Getting Our Baseline Numbers.mp4 59.93 MB
  3. Getting Our Baseline Numbers.vtt 10.7 KB
  4. Draft Preparation.mp4 66.51 MB
  4. Draft Preparation.vtt 6.53 KB
  5. Wrapping Up & Model Blind Spots.mp4 28.37 MB
  5. Wrapping Up & Model Blind Spots.vtt 3.87 KB
  6. Resources.html 140 B
  6.1 Value over replacement player.html 120 B
  6.2 Win Your Snake Draft Calculating “Value Over Replacement”.html 149 B
  6.3 Optimizing draft strategies in fantasy football.html 152 B
  6.4 The Principles of VBD Revisited.html 108 B

Description


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Learn the Fundamentals of Pandas using NBA Stats

Created by : John Mannelly
Last updated : 9/2019
Language : English
Caption (CC) : Included
Torrent Contains : 153 Files, 8 Folders
Course Source : https://www.udemy.com/course/build-a-nba-fantasy-projection-model-in-python-with-pandas/

What you'll learn

• How to build a fantasy basketball projection model in a Jupyter Notebook
• Pandas library basics for data analysis & data manipulation
• Fundamentals of the Python programming language
• How to win your fantasy league using value based drafting

Course content
all 63 lectures 06:04:35

Requirements

• Ability to get around the terminal window
• Basic understanding of Python programming language
• macOS operating system (sorry Windows users)
• Passion to learn!

Description

What is this Course?

Let me start off by saying that my first love has always been the NBA and my second love is coding. As such, I think this class will be a lot of fun for passionate NBA fans who also happen to be aspiring coders. This is the premier Udemy class out there that uses strictly NBA stats as data to help wrap your head around concepts in the python programming language.

While I have found it helpful to read textbooks and watch online tutorials to get a better understanding of the basics for any subject, nothing beats project-based learning. Actually getting your hands dirty and running into real problems that require specific solutions has been my ideal way to learn something new.

With that being said, the hardest question typically is, what project should I focus on? From my personal experience, I’ve found it beneficial to focus on something you are passionate about. To find that something, just think of what you frequently pay attention to in your spare time, when no one is paying you...to pay attention to it. For me, that something is the NBA. I’m a proud subscriber to League Pass. It didn’t take long for me to realize that using NBA stats was going to be the best way for me to learn how to code.

"For one, sports has served as an entry point to data analysis for many. Sports is interesting and has great data relative to other fields, so it can teach skills and methods of thought that are then more broadly applicable. Personally, I learned how to program, a skill that has been enormously valuable to me, specifically to analyze basketball stats. And I'm far from the only story like this." -Ben Falk, Cleaning The Glass

The Project

Using the NBA to learn how to code sounds like a good start, but it it still missing a key piece to turn it into an actual project. That key piece is a goal. Tiago Forte defines a project as, “a series of tasks linked to a goal, with a deadline.”

So what is our goal? Well, for those of you that have played fantasy basketball before, you may have learned how important the draft is. Your team’s success is often times linked directly to your success in the draft. And your success in the draft is often linked to how effectively you can project player stats for that upcoming year. If you know Lebron James is going to score more fantasy points than Anthony Davis then you will want Lebron James on your fantasy team.

After blindly turning to the internet for many consecutive years to use projection models that weren’t made by the oafs at ESPN or Yahoo, it dawned on me that said models had to come from someone’s brain. My thinking from there was, “what’s stopping me from building my own projection model?”

Alas! We have our class project! We are going to build an NBA Fantasy Projection model so you can win your NBA Fantasy League! And how will we do that? By learning to code!

What Will You Learn?

This is another reminder that everything I’ve done to date has been a combination of self-teaching and learning from a friend who also happens to be a talented engineer.

For our purposes, we are going to focus on Python. I’ve been hooked on it ever since I took the class Automate the Boring Stuff with Python. It's undoubtedly a popular programming language so I think it will be beneficial for many years to come.

This class is not meant to be an introduction to programming or python, so my assumption is that you understand some basics. This class is geared more towards helping you apply Python programming to an actual project to help you better retain information while having fun within the process.

Since this class is primarily focused on data (in the form of NBA stats), we will need to manipulate the data in various ways. To help with this, we’ll use the Pandas library. Pandas is extremely powerful and can be used in more ways than just building NBA fantasy projection models so I think you will find it extremely helpful to learn more about.

In his book, Jake VanderPlas describes Pandas as, “a newer package built on top of NumPy, and provides an efficient implementation of a DataFrame. dataFrames are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data. As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.” Said another way, Pandas is SQL and Excel on steroids!

By the end of this course you will be ready to win your NBA fantasy league by building the best fantasy projection model using Python and more specifically Pandas. All of this will be done using a Jupyter Notebook so you can share your work and improve on it over the years.

Who this course is for :

• Beginner Python Programmers looking to learn the Pandas library
• NBA Fantasy Basketball Players.



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