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# Data Science From Scratch: Comprehensive Beginners Guide To Learn Data Science From Scratch

ePUB
• eBook:
Data Science From Scratch: Comprehensive Beginners Guide To Learn Data Science From Scratch
• Author:
Henry George
• Edition:
-
• Categories:
• Data:
January 24, 2020
• ISBN:
B08465RGYR
• ISBN-13:
9798603778440
• Language:
English
• Pages:
198 pages
• Format:
ePUB

Book Description
Data Science is a booming profession right now, with tech companies publishing job adverts every day requesting skilled data scientists. The right time to take advantage of this opportunity is now! Learn Data Science From Scratch. This book is a comprehensive guide for beginners who want to learn the fundamental principles of data science. It teaches Python programming, the mathematical aspect of Data Science, and Machine learning in such an easy way that it makes creating algorithms look effortless.
Programming in Python is definitely not child’s play, but reading this book with instill you with enough skill to write advanced data science programs. It covers the basic principles of the modules, libraries, and toolkits necessary for data science and shows you how to master and use them to their maximum capacity.
This book help instill confidence in you so that you’ll be comfortable with the mathematical and statistical aspects of programming and will guide you on how to apply it to data science.
Each chapter in the book contains practical examples that show you how to apply what you learn in the real world.
The world is overflowing with data. Data Science From Scratch will show you how to transform data into a format that’s appropriate for analysis, inspect the data, create and test hypotheses, and at the end of the day convert the data into knowledge and information.

#### Content

Chapter 1: Introduction to Data and Programming.
Chapter 2: Python 101
Chapter 3: Python Data Types
Chapter 4: In-built Python Features
Chapter 5: Basic Operators
Chapter 6: Conditional Statements and Loops
Chapter 7: Python Data Types Continued
Chapter 8: Modules and Exceptions
Chapter 9: Data Mining
Chapter 10: Data Visualization
Chapter 11: Linear Algebra
Chapter 12: Statistics
Chapter 13: Probability
Chapter 14: Machine Learning

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