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Mastering Large Datasets with Python, Video Edition

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Mastering Large Datasets with Python, Video Edition (Size: 1.1 GB)
  001. Part 1.mp4 1.6 MB
  002. Chapter 1. Introduction.mp4 7.4 MB
  003. Chapter 1. Why large datasets.mp4 7.4 MB
  004. Chapter 1. What is parallel computing.mp4 15.6 MB
  005. Chapter 1. The map and reduce style.mp4 14.6 MB
  006. Chapter 1. Distributed computing for speed and scale.mp4 6.7 MB
  007. Chapter 1. Hadoop A distributed framework for map and reduce.mp4 6.2 MB
  008. Chapter 1. Spark for high-powered map, reduce, and more.mp4 3 MB
  009. Chapter 1. AWS Elastic MapReduce Large datasets in the cloud.mp4 3.3 MB
  010. Chapter 1. Summary.mp4 3 MB
  011. Chapter 2. Accelerating large dataset work Map and parallel computing.mp4 28.9 MB
  012. Chapter 2. Parallel processing.mp4 57.4 MB
  013. Chapter 2. Putting it all together Scraping a Wikipedia network.mp4 33.8 MB
  014. Chapter 2. Exercises.mp4 6.6 MB
  015. Chapter 2. Summary.mp4 2.8 MB
  016. Chapter 3. Function pipelines for mapping complex transformations.mp4 10.9 MB
  017. Chapter 3. Unmasking hacker communications.mp4 27.9 MB
  018. Chapter 3. Twitter demographic projections.mp4 46 MB
  019. Chapter 3. Exercises.mp4 4.9 MB
  020. Chapter 3. Summary.mp4 2.7 MB
  021. Chapter 4. Processing large datasets with lazy workflows.mp4 6.3 MB
  022. Chapter 4. Some lazy functions to know.mp4 12.6 MB
  023. Chapter 4. Understanding iterators The magic behind lazy Python.mp4 18.5 MB
  024. Chapter 4. The poetry puzzle Lazily processing a large dataset.mp4 29.8 MB
  025. Chapter 4. Lazy simulations Simulating fishing villages.mp4 27.2 MB
  026. Chapter 4. Exercises.mp4 6.2 MB
  027. Chapter 4. Summary.mp4 4.1 MB
  028. Chapter 5. Accumulation operations with reduce.mp4 6.6 MB
  029. Chapter 5. The three parts of reduce.mp4 25.1 MB
  030. Chapter 5. Reductions you re familiar with.mp4 10.3 MB
  031. Chapter 5. Using map and reduce together.mp4 12.4 MB
  032. Chapter 5. Analyzing car trends with reduce.mp4 22.1 MB
  033. Chapter 5. Speeding up map and reduce.mp4 4 MB
  034. Chapter 5. Exercises.mp4 3.7 MB
  035. Chapter 5. Summary.mp4 3 MB
  036. Chapter 6. Speeding up map and reduce with advanced parallelization.mp4 26 MB
  037. Chapter 6. Solving the parallel map and reduce paradox.mp4 49 MB
  038. Chapter 6. Summary.mp4 3.1 MB
  039. Part 2.mp4 2 MB
  040. Chapter 7. Processing truly big datasets with Hadoop and Spark.mp4 11.9 MB
  041. Chapter 7. Hadoop for batch processing.mp4 13.5 MB
  042. Chapter 7. Using Hadoop to find high-scoring words.mp4 14.5 MB
  043. Chapter 7. Spark for interactive workflows.mp4 18.6 MB
  044. Chapter 7. Document word scores in Spark.mp4 19.8 MB
  045. Chapter 7. Exercises.mp4 3.5 MB
  046. Chapter 7. Summary.mp4 2.7 MB
  047. Chapter 8. Best practices for large data with Apache Streaming and mrjob.mp4 9.9 MB
  048. Chapter 8. Tennis analytics with Hadoop.mp4 28.3 MB
  049. Chapter 8. mrjob for Pythonic Hadoop streaming.mp4 25 MB
  050. Chapter 8. Tennis match analysis with mrjob.mp4 24.7 MB
  051. Chapter 8. Exercises.mp4 2.8 MB
  052. Chapter 8. Summary.mp4 3.2 MB
  053. Chapter 9. PageRank with map and reduce in PySpark.mp4 32.6 MB
  054. Chapter 9. Tennis rankings with Elo and PageRank in PySpark.mp4 51.1 MB
  055. Chapter 9. Exercises.mp4 4.7 MB
  056. Chapter 9. Summary.mp4 3.1 MB
  057. Chapter 10. Faster decision-making with machine learning and PySpark.mp4 26 MB
  058. Chapter 10. Machine learning basics with decision tree classifiers.mp4 40.7 MB
  059. Chapter 10. Fast random forest classifications in PySpark.mp4 15.4 MB
  060. Chapter 10. Summary.mp4 2.9 MB
  061. Part 3.mp4 2.1 MB
  062. Chapter 11. Large datasets in the cloud with Amazon Web Services and S3.mp4 23.2 MB
  063. Chapter 11. Storing data in the cloud with S3.mp4 41 MB
  064. Chapter 11. Exercises.mp4 2.4 MB
  065. Chapter 11. Summary.mp4 3.2 MB
  066. Chapter 12. MapReduce in the cloud with Amazon s Elastic MapReduce.mp4 33.2 MB
  067. Chapter 12. Machine learning in the cloud with Spark on EMR.mp4 47.2 MB
  068. Chapter 12. Exercises.mp4 2.5 MB
  069. Chapter 12. Summary.mp4 3.4 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 71 total files

Description


Mastering Large Datasets with Python, Video Edition

https://FreeCourseWeb.com

MP4 | Video: AVC 1920 x 1080 | Audio: AAC 44 Khz 2ch | Duration: 07:43:50 | 1.05 GB
Genre: eLearning | Language: English

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You’ll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project.

About the Technology
Programming techniques that work well on laptop-sized data can slow to a crawl—or fail altogether—when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change.

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