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

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name:	Udemy \| Build an NBA Fantasy Projection Model in Python with Pandas [FTU]
说明: 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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类别:	Tutorials
语言:	English
总大小:	2.18 GB
哈希信息:	1DD14E7E3CC63D0CE5C7EA47B429BF52353A0553
增加:	Prom3th3uS
加入的日期:	2019-09-24 04:37:00
洪流地位:	Torrent Verified

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文件列表:

文件	大小
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url	377.00 B
0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url	328.00 B
0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	286.00 B
0. Websites you may like/3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles and more... etc.url	163.00 B
0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url	239.00 B
0. Websites you may like/How you can help Team-FTU.txt	229.00 B
1. Introduction/1. Course Intro.mp4	45.60 MB
1. Introduction/1. Course Intro.vtt	7.98 kB
1. Introduction/2. Installation.mp4	21.77 MB
1. Introduction/2. Installation.vtt	4.51 kB
1. Introduction/3. Virtual Environments.mp4	24.72 MB
1. Introduction/3. Virtual Environments.vtt	4.53 kB
1. Introduction/4. Modules & Packages.mp4	41.31 MB
1. Introduction/4. Modules & Packages.vtt	5.54 kB
1. Introduction/5. Resources.html	140.00 B
1. Introduction/5.1 Cleaning the Glass.html	90.00 B
1. Introduction/5.2 Homebrew.html	77.00 B
1. Introduction/5.3 Github.html	104.00 B
1. Introduction/5.4 Xcode.html	77.00 B
1. Introduction/5.5 Pyenv.html	104.00 B
2. Jupyter Notebook/1. Setting Up Jupyter Notebook.mp4	29.41 MB
2. Jupyter Notebook/1. Setting Up Jupyter Notebook.vtt	6.73 kB
2. Jupyter Notebook/2. Cell Types & Kernels.mp4	19.22 MB
2. Jupyter Notebook/2. Cell Types & Kernels.vtt	4.38 kB
2. Jupyter Notebook/3. Running Code.mp4	18.68 MB
2. Jupyter Notebook/3. Running Code.vtt	4.18 kB
2. Jupyter Notebook/4. Resources.html	140.00 B
2. Jupyter Notebook/4.1 Getting Started with Jupyter Notebook for Python.html	168.00 B
2. Jupyter Notebook/4.2 Jupyter Notebook for Beginners.html	117.00 B
2. Jupyter Notebook/4.3 Project Jupyter.html	81.00 B
3. Python Fundamentals/1. Python Overview.mp4	2.87 MB
3. Python Fundamentals/1. Python Overview.vtt	1.00 kB
3. Python Fundamentals/10. Arrays.mp4	14.17 MB
3. Python Fundamentals/10. Arrays.vtt	2.83 kB
3. Python Fundamentals/11. Resources.html	140.00 B
3. Python Fundamentals/11.1 Python For Loops.html	114.00 B
3. Python Fundamentals/11.2 Data Science from Scratch First Principles with Python.html	136.00 B
3. Python Fundamentals/11.3 Automate the Boring Stuff with Python.html	96.00 B
3. Python Fundamentals/2. Importing Modules.mp4	15.77 MB
3. Python Fundamentals/2. Importing Modules.vtt	3.21 kB
3. Python Fundamentals/3. Printing.mp4	10.59 MB
3. Python Fundamentals/3. Printing.vtt	2.18 kB
3. Python Fundamentals/4. Variables & Raw Input.mp4	10.18 MB
3. Python Fundamentals/4. Variables & Raw Input.vtt	2.11 kB
3. Python Fundamentals/5. Lists.mp4	29.80 MB
3. Python Fundamentals/5. Lists.vtt	5.67 kB
3. Python Fundamentals/6. Dictionaries.mp4	18.44 MB
3. Python Fundamentals/6. Dictionaries.vtt	3.18 kB
3. Python Fundamentals/7. For Loops.mp4	23.59 MB
3. Python Fundamentals/7. For Loops.vtt	5.00 kB
3. Python Fundamentals/8. If Else Statements.mp4	28.34 MB
3. Python Fundamentals/8. If Else Statements.vtt	6.39 kB
3. Python Fundamentals/9. Functions.mp4	17.03 MB
3. Python Fundamentals/9. Functions.vtt	3.36 kB
4. Pandas Building Blocks/1. Pandas Intro.mp4	13.32 MB
4. Pandas Building Blocks/1. Pandas Intro.vtt	2.04 kB
4. Pandas Building Blocks/10. Adding & Dropping Rows.mp4	43.76 MB
4. Pandas Building Blocks/10. Adding & Dropping Rows.vtt	7.72 kB
4. Pandas Building Blocks/11. Inplace Parameter.mp4	53.08 MB
4. Pandas Building Blocks/11. Inplace Parameter.vtt	5.41 kB
4. Pandas Building Blocks/12. Sorting Dataframes.mp4	55.56 MB
4. Pandas Building Blocks/12. Sorting Dataframes.vtt	6.26 kB
4. Pandas Building Blocks/13. Filtering Dataframes.mp4	92.03 MB
4. Pandas Building Blocks/13. Filtering Dataframes.vtt	9.27 kB
4. Pandas Building Blocks/14. Groupby.mp4	51.10 MB
4. Pandas Building Blocks/14. Groupby.vtt	6.81 kB
4. Pandas Building Blocks/15. Concatenate & Append.mp4	34.74 MB
4. Pandas Building Blocks/15. Concatenate & Append.vtt	5.53 kB
4. Pandas Building Blocks/16. Merging & Joining.mp4	89.37 MB
4. Pandas Building Blocks/16. Merging & Joining.vtt	11.48 kB
4. Pandas Building Blocks/17. Iterating Over Dataframes.mp4	17.54 MB
4. Pandas Building Blocks/17. Iterating Over Dataframes.vtt	3.01 kB
4. Pandas Building Blocks/18. Applying Functions.mp4	61.68 MB
4. Pandas Building Blocks/18. Applying Functions.vtt	8.58 kB
4. Pandas Building Blocks/19. Arrays.mp4	20.92 MB
4. Pandas Building Blocks/19. Arrays.vtt	4.59 kB
4. Pandas Building Blocks/2. Dataframes & Series.mp4	8.36 MB
4. Pandas Building Blocks/2. Dataframes & Series.vtt	1.94 kB
4. Pandas Building Blocks/20. Resources.html	140.00 B
4. Pandas Building Blocks/20.1 Join & Merge Pandas Dataframes - Chris Albon.html	134.00 B
4. Pandas Building Blocks/20.2 Using iloc, loc, & ix to select rows and columns in Pandas DataFrames.html	149.00 B
4. Pandas Building Blocks/20.3 A Quick Introduction to the “Pandas” Python Library.html	154.00 B
4. Pandas Building Blocks/20.4 Python Numpy Array Tutorial.html	127.00 B
4. Pandas Building Blocks/20.5 Dropping Rows of Data Using Pandas.html	114.00 B
4. Pandas Building Blocks/20.6 Intro to Pandas Data Structures.html	134.00 B
4. Pandas Building Blocks/20.7 Python Pandas Dataframes.html	133.00 B
4. Pandas Building Blocks/20.8 Selecting Subset of Data in Pandas - Ted Petrou.html	140.00 B
4. Pandas Building Blocks/3. Creating a Dataframe.mp4	39.96 MB
4. Pandas Building Blocks/3. Creating a Dataframe.vtt	6.45 kB
4. Pandas Building Blocks/4. Reading a CSV File.mp4	37.62 MB
4. Pandas Building Blocks/4. Reading a CSV File.vtt	6.03 kB
4. Pandas Building Blocks/5. Attributes & Methods.mp4	58.02 MB
4. Pandas Building Blocks/5. Attributes & Methods.vtt	8.73 kB
4. Pandas Building Blocks/6. Selecting Columns.mp4	46.84 MB
4. Pandas Building Blocks/6. Selecting Columns.vtt	8.94 kB
4. Pandas Building Blocks/7. Adding & Deleting Columns.mp4	79.89 MB
4. Pandas Building Blocks/7. Adding & Deleting Columns.vtt	8.24 kB
4. Pandas Building Blocks/8. Renaming Columns.mp4	26.76 MB
4. Pandas Building Blocks/8. Renaming Columns.vtt	4.56 kB
4. Pandas Building Blocks/9. Selecting Rows.mp4	30.76 MB
4. Pandas Building Blocks/9. Selecting Rows.vtt	4.42 kB
5. Building Our Model/1. Projection Model Overview.mp4	24.05 MB
5. Building Our Model/1. Projection Model Overview.vtt	5.04 kB
5. Building Our Model/10. Player Comparison Function.mp4	76.44 MB
5. Building Our Model/10. Player Comparison Function.vtt	10.82 kB
5. Building Our Model/11. Projecting 2018-19 Season Stats.mp4	73.21 MB
5. Building Our Model/11. Projecting 2018-19 Season Stats.vtt	10.13 kB
5. Building Our Model/12. Resources.html	140.00 B
5. Building Our Model/12.1 A Quick Introduction to K-Nearest Neighbors Algorithm.html	155.00 B
5. Building Our Model/12.2 NBA Math FATS Model.html	92.00 B
5. Building Our Model/12.3 Tutorial K Nearest Neighbors in Python.html	121.00 B
5. Building Our Model/12.4 Comparing NBA Stars to their Statistical Contemporaries An Application of Machine Learning.html	185.00 B
5. Building Our Model/2. Cleaning NBA Data.mp4	51.10 MB
5. Building Our Model/2. Cleaning NBA Data.vtt	10.29 kB
5. Building Our Model/3. Normalizing Season Data.mp4	70.34 MB
5. Building Our Model/3. Normalizing Season Data.vtt	10.65 kB
5. Building Our Model/4. Player Distance Function.mp4	38.56 MB
5. Building Our Model/4. Player Distance Function.vtt	6.84 kB
5. Building Our Model/5. Find Player Function.mp4	43.50 MB
5. Building Our Model/5. Find Player Function.vtt	5.34 kB
5. Building Our Model/6. Calculating Player Similarity.mp4	63.83 MB
5. Building Our Model/6. Calculating Player Similarity.vtt	8.94 kB
5. Building Our Model/7. Comparing Multiple Players in a For Loop.mp4	27.23 MB
5. Building Our Model/7. Comparing Multiple Players in a For Loop.vtt	4.22 kB
5. Building Our Model/8. Weighting Stat Columns.mp4	69.53 MB
5. Building Our Model/8. Weighting Stat Columns.vtt	13.11 kB
5. Building Our Model/9. Weighted Avg. Using Multiple Players Next Season.mp4	44.39 MB
5. Building Our Model/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
6. Measuring Our Model/1. Using RSME to Evaluate Our Model.vtt	10.61 kB
6. Measuring Our Model/2. Comparing to Competitors Pt. 1.mp4	62.67 MB
6. Measuring Our Model/2. Comparing to Competitors Pt. 1.vtt	9.88 kB
6. Measuring Our Model/3. Comparing to Competitors Pt. 2.mp4	39.93 MB
6. Measuring Our Model/3. Comparing to Competitors Pt. 2.vtt	5.29 kB
6. Measuring Our Model/4. Adjusting Variables.mp4	14.65 MB
6. Measuring Our Model/4. Adjusting Variables.vtt	2.11 kB
6. Measuring Our Model/5. Resources.html	140.00 B
6. Measuring Our Model/5.1 Python Machine Learning Tutorial Predicting Airbnb Prices.html	117.00 B
7. Winning Your Fantasy League/1. Converting to Fantasy Points.mp4	49.62 MB
7. Winning Your Fantasy League/1. Converting to Fantasy Points.vtt	6.61 kB
7. Winning Your Fantasy League/2. Value Based Drafting.mp4	19.56 MB
7. Winning Your Fantasy League/2. Value Based Drafting.vtt	5.11 kB
7. Winning Your Fantasy League/3. Getting Our Baseline Numbers.mp4	59.93 MB
7. Winning Your Fantasy League/3. Getting Our Baseline Numbers.vtt	10.70 kB
7. Winning Your Fantasy League/4. Draft Preparation.mp4	66.51 MB
7. Winning Your Fantasy League/4. Draft Preparation.vtt	6.53 kB
7. Winning Your Fantasy League/5. Wrapping Up & Model Blind Spots.mp4	28.37 MB
7. Winning Your Fantasy League/5. Wrapping Up & Model Blind Spots.vtt	3.87 kB
7. Winning Your Fantasy League/6. Resources.html	140.00 B
7. Winning Your Fantasy League/6.1 Value over replacement player.html	120.00 B
7. Winning Your Fantasy League/6.2 Win Your Snake Draft Calculating “Value Over Replacement”.html	149.00 B
7. Winning Your Fantasy League/6.3 Optimizing draft strategies in fantasy football.html	152.00 B
7. Winning Your Fantasy League/6.4 The Principles of VBD Revisited.html	108.00 B

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