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Foundations of Data Science: A Practical Introduction to Data Science with Python
- By Jonathan Dinu
- Published Jul 2, 2030 by Addison-Wesley Professional.
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Description
- Copyright 2031
- Dimensions: 7" x 9-1/8"
- Edition: 1st
- EPUB (Watermarked)
- ISBN-10: 0-13-439739-8
- ISBN-13: 978-0-13-439739-9
Data science underlies Amazon's product recommender, LinkedIn's People You Know feature, Pandora's personalized radio stations, Stripe's fraud detectors, and the incredible insights arising from the world's increasingly ubiquitous sensors. In the future, the world's most interesting and impactful problems will be solved with data science. But right now, there's a shortage of data scientists in every industry, traditional schools can't teach students fast enough, and much of the knowledge data scientists need remains trapped in large tech companies.
This comprehensive, practical tutorial is the solution. Drawing on his experience building Zipfian Academy's immersive 12-week data science training program, Jonathan Dinu brings together all you need to teach yourself data science, and successfully enter the profession.
First, Dinu helps you internalize the data science "mindset": that virtually anything can be quantified, and once you have data, you can harvest amazing insights through statistical analysis and machine learning. He illuminates data science as it really is: a holistic, interdisciplinary process that encompasses the collection, processing, and communication of data: all that data scientists do, say, and believe.
With this foundation in place, he teaches core data science skills through hands-on Python and SQL-based exercises integrated with a full book-length case study. Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems. You'll walk through:
- Building basic and advanced models
- Performing exploratory data analysis
- Using data analysis to acquire and retain users or customers
- Making predictions with regression
- Using machine learning techniques
- Working with unsupervised learning and NLP
- Communicating with data
- Performing social network analyses
- Working with data at scale
- Getting started with Hadoop, Spark and other advanced tools
- Recognizing where common approaches break down, and how to overcome real world constraints
- Taking your next steps in your study and career
Well-crafted appendices provide reference material on everything from the basics of Python and SQL to the essentials of probability, statistics, and linear algebra -- even preparing for your data science job interview!
Sample Content
Table of Contents
Preface: What is Data Science?
1. Diving In: Your First Model
2. EDA, EDA, EDA!
3. Acquiring and Retaining Users
4. Making Predictions: Introduction to Regression
5. Introduction to Machine Learning
6. Unsupervised Learning
7. Natural Language Processing
8. Communicating with Data
9. Social Network Analysis
10. Advanced Modeling
11. Data at Scale
12. Data Products: Putting it All Together
Afterword: What's on the Horizon