17 OCT 2024 - Welcome Back to TorrentFunk! Get your pirate hat back out. Streaming is dying and torrents are the new trend. Account Registration works again and so do Torrent Uploads. We invite you all to start uploading torrents again!
TORRENT DETAILS
[FreeTutorials.Us] [UDEMY] Build An NBA Fantasy Projection Model In Python With Pandas [FTU]
TORRENT SUMMARY
Status:
All the torrents in this section have been verified by our verification system
For More Udemy Free Courses >>> https://freetutorials.us/ For more Lynda and other Courses >>> https://www.freecoursesonline.me/ Forum for discussion >>> https://1hack.us/
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.
VISITOR COMMENTS (0 )
FILE LIST
Filename
Size
0. Websites you may like/How you can help Team-FTU.txt
229 B
1. Introduction/1. Course Intro.mp4
45.6 MB
1. Introduction/1. Course Intro.vtt
8 KB
1. Introduction/2. Installation.mp4
21.8 MB
1. Introduction/2. Installation.vtt
4.5 KB
1. Introduction/3. Virtual Environments.mp4
24.7 MB
1. Introduction/3. Virtual Environments.vtt
4.5 KB
1. Introduction/4. Modules & Packages.mp4
41.3 MB
1. Introduction/4. Modules & Packages.vtt
5.5 KB
1. Introduction/5. Resources.html
140 B
1. Introduction/5.1 Cleaning the Glass.html
90 B
1. Introduction/5.2 Homebrew.html
77 B
1. Introduction/5.3 Github.html
104 B
1. Introduction/5.4 Xcode.html
77 B
1. Introduction/5.5 Pyenv.html
104 B
2. Jupyter Notebook/1. Setting Up Jupyter Notebook.mp4
29.4 MB
2. Jupyter Notebook/1. Setting Up Jupyter Notebook.vtt
6.7 KB
2. Jupyter Notebook/2. Cell Types & Kernels.mp4
19.2 MB
2. Jupyter Notebook/2. Cell Types & Kernels.vtt
4.4 KB
2. Jupyter Notebook/3. Running Code.mp4
18.7 MB
2. Jupyter Notebook/3. Running Code.vtt
4.2 KB
2. Jupyter Notebook/4. Resources.html
140 B
2. Jupyter Notebook/4.1 Getting Started with Jupyter Notebook for Python.html
168 B
2. Jupyter Notebook/4.2 Jupyter Notebook for Beginners.html
117 B
2. Jupyter Notebook/4.3 Project Jupyter.html
81 B
3. Python Fundamentals/1. Python Overview.mp4
2.9 MB
3. Python Fundamentals/1. Python Overview.vtt
1 KB
3. Python Fundamentals/10. Arrays.mp4
14.2 MB
3. Python Fundamentals/10. Arrays.vtt
2.8 KB
3. Python Fundamentals/11. Resources.html
140 B
3. Python Fundamentals/11.1 Python For Loops.html
114 B
3. Python Fundamentals/11.2 Data Science from Scratch First Principles with Python.html
136 B
3. Python Fundamentals/11.3 Automate the Boring Stuff with Python.html
96 B
3. Python Fundamentals/2. Importing Modules.mp4
15.8 MB
3. Python Fundamentals/2. Importing Modules.vtt
3.2 KB
3. Python Fundamentals/3. Printing.mp4
10.6 MB
3. Python Fundamentals/3. Printing.vtt
2.2 KB
3. Python Fundamentals/4. Variables & Raw Input.mp4
10.2 MB
3. Python Fundamentals/4. Variables & Raw Input.vtt
2.1 KB
3. Python Fundamentals/5. Lists.mp4
29.8 MB
3. Python Fundamentals/5. Lists.vtt
5.7 KB
3. Python Fundamentals/6. Dictionaries.mp4
18.4 MB
3. Python Fundamentals/6. Dictionaries.vtt
3.2 KB
3. Python Fundamentals/7. For Loops.mp4
23.6 MB
3. Python Fundamentals/7. For Loops.vtt
5 KB
3. Python Fundamentals/8. If Else Statements.mp4
28.3 MB
3. Python Fundamentals/8. If Else Statements.vtt
6.4 KB
3. Python Fundamentals/9. Functions.mp4
17 MB
3. Python Fundamentals/9. Functions.vtt
3.4 KB
4. Pandas Building Blocks/1. Pandas Intro.mp4
13.3 MB
4. Pandas Building Blocks/1. Pandas Intro.vtt
2 KB
4. Pandas Building Blocks/10. Adding & Dropping Rows.mp4
43.8 MB
4. Pandas Building Blocks/10. Adding & Dropping Rows.vtt
7.7 KB
4. Pandas Building Blocks/11. Inplace Parameter.mp4
53.1 MB
4. Pandas Building Blocks/11. Inplace Parameter.vtt
5.4 KB
4. Pandas Building Blocks/12. Sorting Dataframes.mp4
55.6 MB
4. Pandas Building Blocks/12. Sorting Dataframes.vtt
6.3 KB
4. Pandas Building Blocks/13. Filtering Dataframes.mp4
92 MB
4. Pandas Building Blocks/13. Filtering Dataframes.vtt
9.3 KB
4. Pandas Building Blocks/14. Groupby.mp4
51.1 MB
4. Pandas Building Blocks/14. Groupby.vtt
6.8 KB
4. Pandas Building Blocks/15. Concatenate & Append.mp4
34.7 MB
4. Pandas Building Blocks/15. Concatenate & Append.vtt
5.5 KB
4. Pandas Building Blocks/16. Merging & Joining.mp4
89.4 MB
4. Pandas Building Blocks/16. Merging & Joining.vtt
11.5 KB
4. Pandas Building Blocks/17. Iterating Over Dataframes.mp4
17.5 MB
4. Pandas Building Blocks/17. Iterating Over Dataframes.vtt
3 KB
4. Pandas Building Blocks/18. Applying Functions.mp4
61.7 MB
4. Pandas Building Blocks/18. Applying Functions.vtt
8.6 KB
4. Pandas Building Blocks/19. Arrays.mp4
20.9 MB
4. Pandas Building Blocks/19. Arrays.vtt
4.6 KB
4. Pandas Building Blocks/2. Dataframes & Series.mp4
8.4 MB
4. Pandas Building Blocks/2. Dataframes & Series.vtt
1.9 KB
4. Pandas Building Blocks/20. Resources.html
140 B
4. Pandas Building Blocks/20.1 Join & Merge Pandas Dataframes - Chris Albon.html
134 B
4. Pandas Building Blocks/20.2 Using iloc, loc, & ix to select rows and columns in Pandas DataFrames.html
149 B
4. Pandas Building Blocks/20.3 A Quick Introduction to the “Pandas” Python Library.html
154 B
4. Pandas Building Blocks/20.4 Python Numpy Array Tutorial.html
127 B
4. Pandas Building Blocks/20.5 Dropping Rows of Data Using Pandas.html
114 B
4. Pandas Building Blocks/20.6 Intro to Pandas Data Structures.html
134 B
4. Pandas Building Blocks/20.7 Python Pandas Dataframes.html
133 B
4. Pandas Building Blocks/20.8 Selecting Subset of Data in Pandas - Ted Petrou.html
140 B
4. Pandas Building Blocks/3. Creating a Dataframe.mp4
40 MB
4. Pandas Building Blocks/3. Creating a Dataframe.vtt
6.5 KB
4. Pandas Building Blocks/4. Reading a CSV File.mp4
37.6 MB
4. Pandas Building Blocks/4. Reading a CSV File.vtt
6 KB
4. Pandas Building Blocks/5. Attributes & Methods.mp4
58 MB
4. Pandas Building Blocks/5. Attributes & Methods.vtt
8.7 KB
4. Pandas Building Blocks/6. Selecting Columns.mp4
46.8 MB
4. Pandas Building Blocks/6. Selecting Columns.vtt
8.9 KB
4. Pandas Building Blocks/7. Adding & Deleting Columns.mp4
79.9 MB
4. Pandas Building Blocks/7. Adding & Deleting Columns.vtt
![[FreeTutorials.Us] [UDEMY] Build an NBA Fantasy Projection Model in Python with Pandas [FTU]](https://f.t0r.site/cover/23643816.webp)
