| 001. Part 1. The building blocks of scalable computing.en.srt | 1.1 KB | ||
| 001. Part 1. The building blocks of scalable computing.mp4 | 1.8 MB | ||
| 002. Chapter 1. Why scalable computing matters.en.srt | 27.2 KB | ||
| 002. Chapter 1. Why scalable computing matters.mp4 | 71.8 MB | ||
| 003. Chapter 1. Cooking with DAGs.en.srt | 13.4 KB | ||
| 003. Chapter 1. Cooking with DAGs.mp4 | 20.2 MB | ||
| 004. Chapter 1. Scaling out, concurrency, and recovery.en.srt | 22.4 KB | ||
| 004. Chapter 1. Scaling out, concurrency, and recovery.mp4 | 49.8 MB | ||
| 005. Chapter 1. Introducing a companion dataset.en.srt | 2.7 KB | ||
| 005. Chapter 1. Introducing a companion dataset.mp4 | 6.7 MB | ||
| 006. Chapter 1. Summary.en.srt | 1 KB | ||
| 006. Chapter 1. Summary.mp4 | 3.2 MB | ||
| 007. Chapter 2. Introducing Dask.en.srt | 27.4 KB | ||
| 007. Chapter 2. Introducing Dask.mp4 | 48.9 MB | ||
| 008. Chapter 2. Visualizing DAGs.en.srt | 13.6 KB | ||
| 008. Chapter 2. Visualizing DAGs.mp4 | 29.3 MB | ||
| 009. Chapter 2. Task scheduling.en.srt | 10.4 KB | ||
| 009. Chapter 2. Task scheduling.mp4 | 23.6 MB | ||
| 010. Chapter 2. Summary.en.srt | 716.8 B | ||
| 010. Chapter 2. Summary.mp4 | 1.9 MB | ||
| 011. Part 2. Working with structured data using Dask DataFrames.en.srt | 2.1 KB | ||
| 011. Part 2. Working with structured data using Dask DataFrames.mp4 | 3.6 MB | ||
| 012. Chapter 3. Introducing Dask DataFrames.en.srt | 7.7 KB | ||
| 012. Chapter 3. Introducing Dask DataFrames.mp4 | 16.6 MB | ||
| 013. Chapter 3. Dask and Pandas.en.srt | 16.3 KB | ||
| 013. Chapter 3. Dask and Pandas.mp4 | 36.9 MB | ||
| 014. Chapter 3. Limitations of Dask DataFrames.en.srt | 5.9 KB | ||
| 014. Chapter 3. Limitations of Dask DataFrames.mp4 | 13.5 MB | ||
| 015. Chapter 3. Summary.en.srt | 921.6 B | ||
| 015. Chapter 3. Summary.mp4 | 2.3 MB | ||
| 016. Chapter 4. Loading data into DataFrames.en.srt | 34.8 KB | ||
| 016. Chapter 4. Loading data into DataFrames.mp4 | 78 MB | ||
| 017. Chapter 4. Reading data from relational databases.en.srt | 10.8 KB | ||
| 017. Chapter 4. Reading data from relational databases.mp4 | 19.7 MB | ||
| 018. Chapter 4. Reading data from HDFS and S3.en.srt | 10.8 KB | ||
| 018. Chapter 4. Reading data from HDFS and S3.mp4 | 20.8 MB | ||
| 019. Chapter 4. Reading data in Parquet format.en.srt | 7.7 KB | ||
| 019. Chapter 4. Reading data in Parquet format.mp4 | 14.9 MB | ||
| 020. Chapter 4. Summary.en.srt | 614.4 B | ||
| 020. Chapter 4. Summary.mp4 | 1.2 MB | ||
| 021. Chapter 5. Cleaning and transforming DataFrames.en.srt | 20.4 KB | ||
| 021. Chapter 5. Cleaning and transforming DataFrames.mp4 | 38.9 MB | ||
| 022. Chapter 5. Dealing with missing values.en.srt | 13.1 KB | ||
| 022. Chapter 5. Dealing with missing values.mp4 | 27.7 MB | ||
| 023. Chapter 5. Recoding data.en.srt | 8.7 KB | ||
| 023. Chapter 5. Recoding data.mp4 | 17.7 MB | ||
| 024. Chapter 5. Elementwise operations.en.srt | 7.8 KB | ||
| 024. Chapter 5. Elementwise operations.mp4 | 16.5 MB | ||
| 025. Chapter 5. Filtering and reindexing DataFrames.en.srt | 5.6 KB | ||
| 025. Chapter 5. Filtering and reindexing DataFrames.mp4 | 13.8 MB | ||
| 026. Chapter 5. Joining and concatenating DataFrames.en.srt | 11.3 KB | ||
| 026. Chapter 5. Joining and concatenating DataFrames.mp4 | 21.9 MB | ||
| 027. Chapter 5. Writing data to text files and Parquet files.en.srt | 6.6 KB | ||
| 027. Chapter 5. Writing data to text files and Parquet files.mp4 | 12.3 MB | ||
| 028. Chapter 5. Summary.en.srt | 1.7 KB | ||
| 028. Chapter 5. Summary.mp4 | 5 MB | ||
| 029. Chapter 6. Summarizing and analyzing DataFrames.en.srt | 24.6 KB | ||
| 029. Chapter 6. Summarizing and analyzing DataFrames.mp4 | 51.9 MB | ||
| 030. Chapter 6. Built-In aggregate functions.en.srt | 20 KB | ||
| 030. Chapter 6. Built-In aggregate functions.mp4 | 44.2 MB | ||
| 031. Chapter 6. Custom aggregate functions.en.srt | 37.9 KB | ||
| 031. Chapter 6. Custom aggregate functions.mp4 | 73.8 MB | ||
| 032. Chapter 6. Rolling (window) functions.en.srt | 14 KB | ||
| 032. Chapter 6. Rolling (window) functions.mp4 | 25.1 MB | ||
| 033. Chapter 6. Summary.en.srt | 1.2 KB | ||
| 033. Chapter 6. Summary.mp4 | 3.5 MB | ||
| 034. Chapter 7. Visualizing DataFrames with Seaborn.en.srt | 15 KB | ||
| 034. Chapter 7. Visualizing DataFrames with Seaborn.mp4 | 29.9 MB | ||
| 035. Chapter 7. Visualizing continuous relationships with scatterplot and regplot.en.srt | 13 KB | ||
| 035. Chapter 7. Visualizing continuous relationships with scatterplot and regplot.mp4 | 28.7 MB | ||
| 036. Chapter 7. Visualizing categorical relationships with violinplot.en.srt | 9.8 KB | ||
| 036. Chapter 7. Visualizing categorical relationships with violinplot.mp4 | 15.9 MB | ||
| 037. Chapter 7. Visualizing two categorical relationships with heatmap.en.srt | 7 KB | ||
| 037. Chapter 7. Visualizing two categorical relationships with heatmap.mp4 | 12.1 MB | ||
| 038. Chapter 7. Summary.en.srt | 1.3 KB | ||
| 038. Chapter 7. Summary.mp4 | 3.6 MB | ||
| 039. Chapter 8. Visualizing location data with Datashader.en.srt | 20.4 KB | ||
| 039. Chapter 8. Visualizing location data with Datashader.mp4 | 33 MB | ||
| 040. Chapter 8. Plotting location data as an interactive heatmap.en.srt | 8 KB | ||
| 040. Chapter 8. Plotting location data as an interactive heatmap.mp4 | 21.5 MB | ||
| 041. Chapter 8. Summary.en.srt | 921.6 B | ||
| 041. Chapter 8. Summary.mp4 | 2.3 MB | ||
| 042. Part 3. Extending and deploying Dask.en.srt | 2 KB | ||
| 042. Part 3. Extending and deploying Dask.mp4 | 3.5 MB | ||
| 043. Chapter 9. Working with Bags and Arrays.en.srt | 29.9 KB | ||
| 043. Chapter 9. Working with Bags and Arrays.mp4 | 53.2 MB | ||
| 044. Chapter 9. Transforming, filtering, and folding elements.en.srt | 20 KB | ||
| 044. Chapter 9. Transforming, filtering, and folding elements.mp4 | 40.2 MB | ||
| 045. Chapter 9. Building Arrays and DataFrames from Bags.en.srt | 5.5 KB | ||
| 045. Chapter 9. Building Arrays and DataFrames from Bags.mp4 | 11 MB | ||
| 046. Chapter 9. Using Bags for parallel text analysis with NLTK.en.srt | 13.4 KB | ||
| 046. Chapter 9. Using Bags for parallel text analysis with NLTK.mp4 | 31.9 MB | ||
| 047. Chapter 9. Summary.en.srt | 1.2 KB | ||
| 047. Chapter 9. Summary.mp4 | 3.7 MB | ||
| 048. Chapter 10. Machine learning with Dask-ML.en.srt | 21.9 KB | ||
| 048. Chapter 10. Machine learning with Dask-ML.mp4 | 60.5 MB | ||
| 049. Chapter 10. Evaluating and tuning Dask-ML models.en.srt | 16.2 KB | ||
| 049. Chapter 10. Evaluating and tuning Dask-ML models.mp4 | 44.3 MB | ||
| 050. Chapter 10. Persisting Dask-ML models.en.srt | 5.7 KB | ||
| 050. Chapter 10. Persisting Dask-ML models.mp4 | 9.1 MB | ||
| 051. Chapter 10. Summary.en.srt | 921.6 B | ||
| 051. Chapter 10. Summary.mp4 | 2.8 MB | ||
| 052. Chapter 11. Scaling and deploying Dask.en.srt | 57.8 KB | ||
| 052. Chapter 11. Scaling and deploying Dask.mp4 | 106.4 MB | ||
| 053. Chapter 11. Running and monitoring Dask jobs on a cluster.en.srt | 11.6 KB | ||
| 053. Chapter 11. Running and monitoring Dask jobs on a cluster.mp4 | 22.5 MB | ||
| 054. Chapter 11. Cleaning up the Dask cluster on AWS.en.srt | 4.4 KB | ||
| 054. Chapter 11. Cleaning up the Dask cluster on AWS.mp4 | 9 MB | ||
| 055. Chapter 11. Summary.en.srt | 1 KB | ||
| 055. Chapter 11. Summary.mp4 | 2.7 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 112 total files | |||
Data Science with Python and Dask, Video Edition
https://WebToolTip.com
Released 7/2019
By Jesse Daniel
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 7h 58m | Size: 1.33 GB
Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you’re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!
About the Technology
An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.
About the Book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.
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| 779 MB | freecoursewb | 1 week | 1 | 25 | |
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Udemy - Data Science for Sports - Sports Analytics and Visualization Posted by
freecoursewb in Other
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| 2.3 GB | freecoursewb | 4 months | 7 | 2 |
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