Mastering Large Datasets with Python, Video Edition

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[ FreeCourseWeb.com ] Mastering Large Datasets with Python, Video Edition
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    • 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 (3.0 MB)
    • 009. Chapter 1. AWS Elastic MapReduce Large datasets in the cloud.mp4 (3.3 MB)
    • 010. Chapter 1. Summary.mp4 (3.0 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 (46.0 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 (4.0 MB)
    • 034. Chapter 5. Exercises.mp4 (3.7 MB)
    • 035. Chapter 5. Summary.mp4 (3.0 MB)
    • 036. Chapter 6. Speeding up map and reduce with advanced parallelization.mp4 (26.0 MB)
    • 037. Chapter 6. Solving the parallel map and reduce paradox.mp4 (49.0 MB)
    • 038. Chapter 6. Summary.mp4 (3.1 MB)
    • 039. Part 2.mp4 (2.0 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 (25.0 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 (26.0 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 (41.0 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 (0.4 KB)

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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Mastering Large Datasets with Python, Video Edition

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