Practical data science with Python 3 : synthesizing actionable insights from data / Ervin Varga
- Author
- Varga, Ervin (Professional software engineer)
- Published
- New York : Apress, [2019]
- Copyright Date
- ©2019
- Physical Description
- 1 online resource : illustrations
Access Online
- Contents
- Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction to Data Science; Main Phases of a Data Science Project; Brown Cow Model Case Study; Big Data; Big Data Example: MOOC Platforms; How to Learn Data Science; Domain Knowledge Attainment-Example; Programming Skills Attainment-Example; Overview of the Anaconda Ecosystem; Managing Packages and Environments; Sharing and Reproducing Environments; Summary; References; Chapter 2: Data Engineering; E-Commerce Customer Segmentation: Case Study; Creating a Project in Spyder, Downloading the DatasetExploring the Dataset; Finding Associations Between Features; Incorporating Custom Features; Automating the Steps; Inspecting Results; Persisting Results; Parquet Engines; Restructuring Code to Cope with Large CSV Files; Public Data Sources; Summary; References; Chapter 3: Software Engineering; Characteristics of a Large-Scale Software System; Software Engineering Knowledge Areas; Rules, Principles, Conventions, and Standards; Context Awareness and Communicative Abilities; Reducing Cyclomatic Complexity; Cone of Uncertainty and Having Time to Ask, Fixing a Bug and Knowing How to AskA Better Fix; Scenario 1: The Developer Doesn't Speak the Language of Business; Scenario 2: The Developer Does Speak the Language of Business; A More Advanced Fix; Scenario 1: The Developer Doesn't Speak the Language of Business; Scenario 2: The Developer Does Speak the Language of Business; Handling Legacy Code; Understanding Bug-Free Code; Understanding Faulty Code; The Importance of APIs; Fervent Flexibility Hurts Your API; The Socio-* Pieces of Software Production; Funny Elevator Case Study; First Optimization Attempt; Second Optimization Attempt, Teammate- and Business-Friendly VariantSummary; References; Chapter 4: Documenting Your Work; JupyterLab in Action; Experimenting with Code Execution; Managing the Kernel; Connecting to a Notebook's Kernel; Descending Ball Project; Problem Specification; Model Definition; Path Finder's Implementation; Interaction with the Simulator; Test Automation; Refactoring the Simulator's Notebook; Document Structure; Wikipedia Edits Project; Abstract; Motivation; Drawbacks; Conclusion; Summary; References; Chapter 5: Data Processing; Augmented Descending Ball Project; Version 1.1, and Boundaries and MovementPath Finding Engine; Retrospective of Version 1.1; Version 1.2; Enhancing the Input Subsystem; Enhancing the Output Subsystem; Retrospective of Version 1.2; Version 1.3; Establishing the Baseline; Performance Optimization; Retrospective of Version 1.3; Abstractions vs. Latent Features; Compressing the Ratings Matrix; Summary; References; Chapter 6: Data Visualization; Visualizing Temperature Data Case Study; Showing Stations on a Map; Plotting Temperatures; Closest Pair Case Study; Version 1.0; Version 2.0; Analysis of the Running Time; Version 3.0
- Summary
- Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.
- Subject(s)
- ISBN
- 9781484248591 (electronic bk.)
1484248597 (electronic bk.)
1484248589
9781484248584
9781484248607 (print)
1484248600 - Digital File Characteristics
- text file
PDF - Bibliography Note
- Includes bibliographical references and index.
View MARC record | catkey: 37451905