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Bibliographic Record Display

• Author/Creator:Paper, David.
  
• Title:Data Science Fundamentals for Python and MongoDB [electronic resource] / by David Paper.
  
• ISBN:9781484235973
  
• Publication:Berkeley, CA : Apress : Imprint: Apress, 2018.
  
• Physical Description:1 online resource (XIII, 214 p.) 117 illus.
  
• Links:Online book
  

• Yale Holdings

  Holdings Record Display

  • Location:Yale Internet Resource
    Information about Off Campus Access
    
    
  • Call Number: None
    
  • Status:No information available 
  • Notes: Online resource
    

   
• Local Notes:Access is available to the Yale community.
  
• Access and use:Access restricted by licensing agreement.
• Summary:Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn: Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data.
  
• In_:Springer eBooks
  
• Variant and related titles:Springer ebooks.
  
• Other formats:Printed edition:
  
• Format:Book
• Contents:1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data.
  
• Subjects:Computer science.
  
• Also listed under:SpringerLink (Online service)