种子简介

种子名称： [GigaCourse.Com] Udemy - Python Data Science with Pandas Master 12 Advanced Projects
文件类型： 视频
文件数目： 176个文件
文件大小： 4.09 GB
收录时间： 2023-3-11 14:02
已经下载： 3次
资源热度： 213
最近下载： 2024-12-27 12:28

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:69ddcb2cd58dbd0cec877fe10358bbd02c2b9d28&dn=[GigaCourse.Com] Udemy - Python Data Science with Pandas Master 12 Advanced Projects 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[GigaCourse.Com] Udemy - Python Data Science with Pandas Master 12 Advanced Projects.torrent

1 - Getting Started/1 - Course Overview don´t skip.mp429.14MB

1 - Getting Started/2 - Tips How to get the most out of this Course don´t skip.mp435.68MB

1 - Getting Started/4 - How to download and install Anaconda for Python coding.mp445.63MB

1 - Getting Started/5 - Jupyter Notebooks let´s get started.mp444.06MB

1 - Getting Started/6 - How to work with Jupyter Notebooks.mp441.13MB

10 - Project 9 Data Import Web Scraping APIs & Wrappers US Stocks/107 - Project Overview.mp42.86MB

10 - Project 9 Data Import Web Scraping APIs & Wrappers US Stocks/109 - Web Scraping the Dow Jones Constituents.mp428.02MB

10 - Project 9 Data Import Web Scraping APIs & Wrappers US Stocks/110 - Normalizing Unicode Strings and Getting the Ticker Symbols.mp431.31MB

10 - Project 9 Data Import Web Scraping APIs & Wrappers US Stocks/111 - Download and Installation of an API Wrapper Package.mp423.81MB

10 - Project 9 Data Import Web Scraping APIs & Wrappers US Stocks/112 - Loading and Saving Historical Stock Prices.mp424.69MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/113 - Project Overview.mp41.94MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/115 - Importing the Data.mp411.89MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/116 - Data Visualization & Returns.mp421.05MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/117 - Backtesting a simple Momentum Strategy.mp426.68MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/118 - Backtesting a simple Contrarian Strategy.mp414.98MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/119 - More complex Strategies & Backtesting vs Fitting.mp428.56MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/120 - Simple Moving Averages SMA.mp413.29MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/121 - Backtesting Simple Moving Averages SMA Strategies.mp426.29MB

11 - Project 10 Finance Stack Backtesting Investment Strategies US Stocks/122 - Backtesting the Perfect Strategy in case you can predict the future.mp416.3MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/123 - Project Overview.mp48.97MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/125 - Importing & Merging the Data.mp424.06MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/126 - Transforming the Data.mp464.29MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/127 - Explanatory Data Analysis Risk Return & Correlations.mp460.57MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/128 - Index Tracking Introduction.mp430.73MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/129 - Index Tracking Selecting the Tracking Stocks.mp423.35MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/130 - Index Tracking A simple Tracking Portfolio.mp442.87MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/131 - Index Tracking The optimal Tracking Portfolio.mp435.3MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/132 - Forward Testing Part 1.mp432.75MB

12 - Project 11 Finance Stack Index Tracking and Forward Testing US Stocks/133 - Forward Testing Part 2.mp434.43MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/134 - Project Overview.mp44.25MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/136 - Project Brief for SelfCoders.mp468.09MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/137 - Data Import and first Inspection.mp413.87MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/138 - Merging and Concatenating.mp424.4MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/139 - Data Cleaning Part 1.mp429.98MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/140 - Data Cleaning Part 2.mp416.29MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/141 - What are the most successful countries of all times.mp425.24MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/142 - Do GDP Population and Politics matter.mp433.14MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/143 - Statistical Analysis and Hypothesis Testing with scipy.mp450.88MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/144 - Aggregating and Ranking.mp426.61MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/145 - Summer Games vs Winter Games does Geographical Location matter.mp423.22MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/146 - Men vs Women do Culture & Religion matter.mp415.01MB

13 - Project 12 Explanatory Data Analysis and Seaborn Visualization Olympic Games/147 - Do Traditions matter.mp431.44MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/148 - Intro and Overview.mp411.09MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/151 - Important Recap Pandas Display Options Changed in Version 025.mp418.96MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/152 - Info method new and extended output.mp47.52MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/153 - NEW Extension dtypes nullable dtypes Why do we need them.mp415.56MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/154 - Creating the NEW extension dtypes with convertdtypes.mp419.11MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/155 - NEW pdNA value for missing values.mp422.15MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/156 - The NEW nullable Int64Dtype.mp415.49MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/157 - The NEW StringDtype.mp424.36MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/158 - The NEW nullable BooleanDtype.mp412.79MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/159 - Addition of the ignoreindex parameter.mp413.38MB

14 - Extra Project Prepare yourself for the Future Pandas Version 10/160 - Removal of prior Version Deprecations.mp440.29MB

15 - Appendix Pandas Crash Course/161 - Intro to Tabular Data Pandas.mp410.45MB

15 - Appendix Pandas Crash Course/163 - Create your very first Pandas DataFrame from csv.mp455.52MB

15 - Appendix Pandas Crash Course/164 - Pandas Display Options and the methods head & tail.mp421.89MB

15 - Appendix Pandas Crash Course/165 - First Data Inspection.mp443.21MB

15 - Appendix Pandas Crash Course/166 - Builtin Functions Attributes and Methods with Pandas.mp426.42MB

15 - Appendix Pandas Crash Course/167 - Selecting Columns.mp420.59MB

15 - Appendix Pandas Crash Course/168 - Selecting one Column with the dot notation.mp44.31MB

15 - Appendix Pandas Crash Course/169 - Zerobased Indexing and Negative Indexing.mp46.02MB

15 - Appendix Pandas Crash Course/170 - Selecting Rows with iloc positionbased indexing.mp460.99MB

15 - Appendix Pandas Crash Course/171 - Slicing Rows and Columns with iloc positionbased indexing.mp419.12MB

15 - Appendix Pandas Crash Course/173 - Selecting Rows with loc labelbased indexing.mp411.26MB

15 - Appendix Pandas Crash Course/174 - Slicing Rows and Columns with loc labelbased indexing.mp455.67MB

15 - Appendix Pandas Crash Course/176 - First Steps with Pandas Series.mp414.35MB

15 - Appendix Pandas Crash Course/177 - Analyzing Numerical Series with unique nunique and valuecounts.mp457.18MB

15 - Appendix Pandas Crash Course/178 - Analyzing nonnumerical Series with unique nunique valuecounts.mp418.88MB

15 - Appendix Pandas Crash Course/179 - Sorting of Series and Introduction to the inplace parameter.mp417.01MB

15 - Appendix Pandas Crash Course/180 - Filtering DataFrames by one Condition.mp423.54MB

15 - Appendix Pandas Crash Course/181 - Filtering DataFrames by many Conditions AND.mp49.92MB

15 - Appendix Pandas Crash Course/182 - Filtering DataFrames by many Conditions OR.mp412.93MB

15 - Appendix Pandas Crash Course/183 - Creating Columns based on other Columns.mp416.01MB

15 - Appendix Pandas Crash Course/184 - Userdefined Functions with apply map and applymap.mp433.13MB

15 - Appendix Pandas Crash Course/185 - Data Visualization with Matplotlib.mp451.35MB

15 - Appendix Pandas Crash Course/186 - GroupBy an Introduction.mp43.98MB

15 - Appendix Pandas Crash Course/187 - Understanding the GroupBy Object.mp422.15MB

15 - Appendix Pandas Crash Course/188 - Splitting with many Keys.mp431.75MB

15 - Appendix Pandas Crash Course/189 - splitapplycombine explained.mp431.61MB

15 - Appendix Pandas Crash Course/190 - splitapplycombine applied.mp446.6MB

15 - Appendix Pandas Crash Course/191 - Data with DateTime Information Part 1.mp419.87MB

15 - Appendix Pandas Crash Course/192 - Data with DateTime Information Part 2.mp437.7MB

15 - Appendix Pandas Crash Course/193 - Data with DateTime Information Part 3.mp415.58MB

15 - Appendix Pandas Crash Course/194 - Data with DateTime Information Part 4.mp437.26MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/10 - Data Import from csv file and first Inspection.mp463.07MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/11 - The best and the worst movies Part 1.mp430.86MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/12 - The best and the worst movies Part 2.mp421.93MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/13 - Which Movie would you like to see next.mp441.04MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/14 - What are the most common Words in Movie Titles Taglines and Overviews.mp457.66MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/15 - Are Franchises more successful.mp419.1MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/16 - What are the most successful Franchises.mp427.4MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/17 - The most successful Directors.mp421.73MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/18 - The most successful Actors Part 1.mp443.28MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/19 - The most successful Actors Part 2.mp424.38MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/7 - Project Overview.mp414.8MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/8 - Downloads Project 1.mp49.84MB

2 - Project 1 Explanatory Data Analysis & Data Presentation Movies Dataset/9 - Project Brief for SelfCoders.mp414.22MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/21 - Project Overview.mp46.04MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/22 - What is JSON.mp45.86MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/24 - Project Brief for SelfCoders.mp412.28MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/25 - Importing Data from JSON files.mp456.53MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/26 - JSON and OrientationFormats.mp436.25MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/27 - What is an API The Movie Database API.mp442.88MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/28 - Working with APIs and JSON Part 1.mp432.07MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/29 - How to work with your own APIKEY.mp46.81MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/30 - Working with APIs and JSON Part 2.mp425.54MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/31 - Importing and Storing the Movies Dataset Best Practice.mp435.62MB

3 - Project 2 Data Import Working with APIs and JSON Movies Dataset/32 - Importing and Storing the Movies Dataset Real World Scenario.mp411.92MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/33 - Project Overview.mp42.88MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/35 - Project Brief for SelfCoders.mp426.26MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/36 - First Steps.mp412.68MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/37 - Dropping irrelevant Columns.mp49.06MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/38 - How to handle stringified JSON columns Part 1.mp431.01MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/39 - How to handle stringified JSON columns Part 2.mp414.74MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/40 - How to flatten nested Columns.mp441.2MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/41 - How to clean Numerical Columns Part 1.mp426.29MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/42 - How to clean Numerical Columns Part 2.mp424.5MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/43 - How to clean Columns with DateTime Information.mp411.63MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/44 - How to clean String Text Columns.mp424.91MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/45 - How to remove Duplicates.mp417.11MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/46 - Handling Missing Values & Removing ObervationsRows.mp427.45MB

4 - Project 3 Data Cleaning Tidy up messy Datasets Movies Dataset/47 - Final Steps.mp419.78MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/48 - Project Overview.mp42.01MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/50 - Project Brief for SelfCoders.mp410.8MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/51 - Getting the Datasets.mp417.77MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/52 - Preparing the Data for Merge.mp46.69MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/53 - Merging the Data Left Join.mp413.97MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/54 - Cleaning and Transforming the new Cast Column.mp433.73MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/55 - Cleaning and Transforming the new Crew Column.mp426.43MB

5 - Project 4 Merging Cleaning & Transforming Data Movies Dataset/56 - Final Steps.mp43.4MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/57 - Project Overview.mp42.41MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/58 - What is a Database SQL.mp410.6MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/60 - Project Brief for SelfCoders.mp422.11MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/61 - How to create an SQLite Database.mp410.2MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/62 - How to load Data from DataFrames into an SQLite Database.mp448.22MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/63 - How to load Data from SQLite Databases into DataFrames.mp416.46MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/64 - Some simple SQL Queries.mp428.81MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/65 - Some more SQL Queries.mp433.16MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/66 - Join Queries.mp422.84MB

6 - Project 5 Working with Pandas and SQL Databases Movies Dataset/67 - Final Case Study.mp430.29MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/68 - Project Overview.mp41.94MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/70 - Project Brief for SelfCoders Part 1.mp411.09MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/71 - Getting the Data from the Web.mp47.76MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/72 - Importing one File & Understanding the Data Structure easy case.mp417.75MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/73 - Importing & merging many Files easy case.mp427.43MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/74 - Final Steps.mp44.45MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/75 - Project Brief for SelfCoders Part 2.mp413.77MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/76 - Importing one File & Understanding the Data Structure complex case.mp45.26MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/77 - The glob module.mp49.45MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/78 - Importing & merging many Files complex case.mp47.89MB

7 - Project 6 Importing & Concatenating many files Baby Names Dataset/79 - Excursus Saving Memory Categorical Features.mp46.27MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/80 - Project Overview.mp42.28MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/82 - Project Brief for SelfCoders.mp442.46MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/83 - First Inspection The most popular Names in 2018.mp412.2MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/84 - Evergreen Names 1880 2018.mp48MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/85 - Advanced Data Aggregation.mp416.14MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/86 - What are the most popular Names of all Times.mp411.53MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/87 - General Trends over Time 1880 2018.mp415.11MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/88 - Creating the Features Popularity and Rank.mp417.44MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/89 - Visualizing Name Trends over Time.mp442.43MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/90 - Why does a Name´s Popularity suddenly change Part 1.mp420.65MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/91 - Why does a Name´s Popularity suddenly change Part 2.mp427.88MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/92 - Persistant vs SpikeFade Names.mp428.02MB

8 - Project 7 Explanatory Data Analysis & Advanced Visualization Baby Names/93 - Most Popular Unisex Names.mp426.59MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/100 - Advanced Explanatory Data Analyis with Seaborn.mp441.06MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/101 - Feature Engineering Part 1.mp415.53MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/102 - Feature Engineering Part 2.mp410.96MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/103 - Splitting the Data into Train and Test Set.mp416.75MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/104 - Training the ML Model Random Forest.mp423.31MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/105 - Evaluating the Model on the Test Set.mp412.54MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/106 - Feature Importance.mp45.92MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/94 - Project Overview.mp43.28MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/96 - Project Brief for SelfCoders.mp433.32MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/97 - Data Import and first Inspection.mp429.39MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/98 - Data Cleaning and Creating additional Features.mp424.36MB

9 - Project 8 Data Preprocessing & Feature Engineering for Machine Learning/99 - Which Factors influence House Prices.mp472.42MB