Data Science from Scratch: First Principles with Python 2nd Edition ISBN: 9781492041139
Original price was: $65.95.$44.99Current price is: $44.99.
Product Details
- Condition: New
- Publisher: O’Reilly Media
- Language: English
- Paperback: 403 pages
- ISBN: 9781492041139
- Item Weight: 1.4 pounds
- Dimensions: 6.9 x 0.9 x 9.1 inches
Master the principles of statistics science by way of constructing device from the floor up. This 2d version of Joel Grus’s conventional guide makes use of Python 3.7 to teach you the mathematics, records, and programming good judgment inside the again of modern algorithms. It’s the proper “tough road” to becoming a in reality proficient data scientist.
Key Features
- “From Scratch” Philosophy: Unlike other books that rely on “black field” libraries, this guide teaches you to place into effect algorithms from first principles.
- Updated for Python 3.7: All code examples were modernized to mirror current-day Python syntax and exceptional practices.
- Comprehensive Curriculum: Covers the essential “trifecta” of facts generation: linear algebra, information, and threat.
- Crash Courses Included: Features fast-hearth introductions to Python and the mathematics important to apprehend the deep learning and NLP chapters.
- O’Reilly Pedigree: Published through O’Reilly Media, making sure outstanding technical enhancing and clean, concise factors.
Why You Should Buy This Book
In a international in which most people definitely “import” a library to remedy a hassle, the without a doubt treasured engineers are folks who apprehend how the underlying math works. You should buy this book if you need to transport beyond being a “user” and turn out to be a “author.”
By stripping away the abstraction of libraries like Scikit-Learn or NumPy, Joel Grus forces you to apprehend the “why” in the lower back of the “how.” This book doesn’t simply teach you data era; it builds your technical intuition, making you higher at debugging, optimizing, and interviewing for excessive-level roles.
Use Cases
- Self-Taught Learners: An ideal roadmap for programmers who want to transition into data technological knowledge without enrolling in a proper bootcamp.
- Interview Preparation: The “from scratch” implementations are best for passing technical interviews that require coding algorithms on a whiteboard.
- Academic Supplement: A awesome partner for college college students who locate their theoretical textbooks too abstract and need arms-on coding examples.
- Reference Guide: Keep it in your desk to quick brush up on the good judgment behind good enough-nearest associates, Naive Bayes, or neural networks.
Format Details
- Condition: Brand New
- Binding: Paperback
- Length: 403 Pages
- Weight: 1.Four kilos (strong but transportable)
- Dimensions: 6.9 x 0.9 x 9.1 inches
- Language: English
Edition Info
- Edition: 2nd Edition (Significant replace from the particular)
- Publisher: O’Reilly Media
- ISBN: 978-1492041139
- Focus: Core thoughts, Python 3.7, and current-day statistics technological know-how techniques such as Deep Learning.
Author Info
Joel Grus is a famous determine within the statistics technology community, presently serving as a studies engineer on the Allen Institute for AI. Formerly a software engineer at Google and a records scientist at Farecast, he brings a totally unique “hacker’s mind-set” to facts technological know-how. Joel is well-known for his witty, no-nonsense technique to technical writing and his capability to simplify complicated mathematical ideas for software program software developers.
Frequently Asked Questions (FAQs)
Q1. Do I need to be a math genius to look at this book?
No, however you need to have a few aptitude for it. The book includes “crash guides” within the essential math, however the content material cloth moves rapid. It’s designed for someone who’s comfortable with primary algebra and proper judgment.
Q2. Why ought to I code from scratch as opposed to the usage of libraries like NumPy?
Using a library is top notch for manufacturing, however it’s horrible for studying. By constructing a library from scratch, you examine precisely wherein the “magic” comes from, which allows you apprehend the limitations and strengths of the tools you use at paintings.
Q3. Is this book updated for the brand new version of Python?
Yes! This 2nd edition become in particular rewritten to utilize Python 3.7, making sure that the code is modern, clean, and kind-annotated.
Shipping, Return & Exchange
Shipping & Delivery:
– Normal Delivery: Estimated delivery time is 5 to 7 business days from the date of shipment.
– Express Delivery: Estimated delivery time is 3 to 5 business days from the date of shipment.
Returns & Exchange:
– Please refer to our Return and Exchange Policy for more details.
| Weight | 1.4 lbs |
|---|---|
| Dimensions | 17.526 × 2.286 × 23.114 in |
Adrian Holloway –
A great book for understanding the fundamentals of data science by building concepts from the ground up. The Python examples make the learning process very practical.
Leonard Prescott –
Well explained and beginner friendly. It focuses on core ideas instead of relying only on libraries, which helps build real understanding.
Vincent Carver –
An informative and engaging guide to core data science principles. It’s a dependable starting point for anyone entering the field.
Trevor Bainbridge –
This book explains statistics, machine learning, and programming concepts in a very clear way. The hands-on approach makes it easier to connect theory with practice.
Harold Winslow –
A solid guide for programmers who want to move into data science. The explanations are clear and supported by useful code examples.
Curtis Halvorsen –
Practical and informative. It shows how algorithms work internally, which is very helpful for deeper learning.
Douglas Whitaker –
A well-structured introduction to analytical thinking and modeling. The step-by-step coding approach makes complex topics manageable.
Gregory Telford –
Very useful for building a strong foundation in statistics and machine learning concepts. The examples help reinforce learning effectively.