[GitHub]Agile Data Science 2.0

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1. Agile Data Science 2.0
   
2. Building Full-Stack Data Analytics Applications with Spark
   
3. By Russell Jurney
   
4. Publisher: O'Reilly Media
   
5. Release Date: June 2017
   
6. Pages: 352
   
7. Read on Safari with a 10-day trial
   
8. Start your free trial
   
9. Buy on Amazon
   
10. Where’s the cart? Now you can get everything on Safari. To purchase books, visit Amazon or your favorite retailer. Questions? See our FAQ or contact customer service:
    
11. 1-800-889-8969 / 707-827-7019
    
12. support@oreilly.com
    
13. Download Example Code
    
14. View/Submit Errata
    
15. Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.
    
16. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization.
    
17. Build value from your data in a series of agile sprints, using the data-value pyramid
    
18. Extract features for statistical models from a single dataset
    
19. Visualize data with charts, and expose different aspects through interactive reports
    
20. Use historical data to predict the future via classification and regression
    
21. Translate predictions into actions
    
22. Get feedback from users after each sprint to keep your project on track

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关键词：Data Science Science GitHub Agile Data

1. # Load the text file using the SparkContext
   
2. csv_lines = sc.textFile("../data/example.csv")
   
3. 
   
4. # Map the data to split the lines into a list
   
5. data = csv_lines.map(lambda line: line.split(","))
   
6. 
   
7. # Collect the dataset into local RAM
   
8. data.collect()

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1. Creating Objects from CSV
   
2. Using a function with a map operation to create objects (dicts) as records...
   
3. In [3]:
   
4. # Turn the CSV lines into objects
   
5. def csv_to_record(line):
   
6.   parts = line.split(",")
   
7.   record = {
   
8.     "name": parts[0],
   
9.     "company": parts[1],
   
10.     "title": parts[2]
    
11.   }
    
12.   return record
    
13. 
    
14. # Apply the function to every record
    
15. records = csv_lines.map(csv_to_record)
    
16. 
    
17. # Inspect the first item in the dataset
    
18. records.first()

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1. GroupBy
   
2. Using the groupBy operator to count the number of jobs per person...
   
3. In [4]:
   
4. # Group the records by the name of the person
   
5. grouped_records = records.groupBy(lambda x: x["name"])
   
6. 
   
7. # Show the first group
   
8. grouped_records.first()
   
9. 
   
10. # Count the groups
    
11. job_counts = grouped_records.map(
    
12.   lambda x: {
    
13.     "name": x[0],
    
14.     "job_count": len(x[1])
    
15.   }
    
16. )
    
17. 
    
18. job_counts.first()
    
19. 
    
20. job_counts.collect()

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