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Data Science Fundamentals Pocket Primer
Data Science Fundamentals Pocket Primer

Data Science Fundamentals Pocket Primer: An Essential Guide to Data Science Concepts and Techniques

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Profile Icon Mercury Learning and Information Profile Icon Oswald Campesato
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$59.99
eBook Jul 2024 450 pages 1st Edition
eBook
$59.99
Arrow left icon
Profile Icon Mercury Learning and Information Profile Icon Oswald Campesato
Arrow right icon
$59.99
eBook Jul 2024 450 pages 1st Edition
eBook
$59.99
eBook
$59.99

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Key benefits

  • Comprehensive coverage of data science fundamentals
  • Practical examples and exercises for hands-on learning
  • Diverse tools and techniques, including Python, NumPy, Pandas, R, and data visualization

Description

This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. Understanding data science is crucial in today's data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills. The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.

Who is this book for?

This book is ideal for beginners and intermediate learners in data science, including students, professionals, and enthusiasts. Basic programming knowledge is beneficial but not mandatory. The book assumes no prior expertise in data science, making it accessible to a broad audience.

What you will learn

  • Understand and preprocess various types of data
  • Apply probability and statistical methods
  • Utilize linear algebra in data science applications
  • Implement Python for data manipulation and analysis
  • Use NumPy and Pandas for efficient data handling
  • Visualize data effectively using various tools

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 30, 2024
Length: 450 pages
Edition : 1st
Language : English
ISBN-13 : 9781836645825
Category :
Concepts :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : Jul 30, 2024
Length: 450 pages
Edition : 1st
Language : English
ISBN-13 : 9781836645825
Category :
Concepts :

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Table of Contents

11 Chapters
CHAPTER 1 WORKING WITH DATA Chevron down icon Chevron up icon
CHAPTER 2 INTRO TO PROBABILITY AND STATISTICS Chevron down icon Chevron up icon
CHAPTER 3 LINEAR ALGEBRA CONCEPTS Chevron down icon Chevron up icon
CHAPTER 4 INTRODUCTION TO PYTHON Chevron down icon Chevron up icon
CHAPTER 4 INTRODUCTION TO PYTHON
NOTE
TOOLS FOR PYTHON
NOTE
PYTHON INSTALLATION
NOTE
SETTING THE PATH ENVIRONMENT VARIABLE (WINDOWS ONLY)
LAUNCHING PYTHON ON YOUR MACHINE
PYTHON IDENTIFIERS
NOTE
LINES, INDENTATIONS, AND MULTI-LINES
QUOTATION AND COMMENTS IN PYTHON
SAVING YOUR CODE IN A MODULE
SOME STANDARD MODULES IN PYTHON
THE HELP() AND DIR() FUNCTIONS
COMPILE TIME AND RUNTIME CODE CHECKING
SIMPLE DATA TYPES IN PYTHON
WORKING WITH NUMBERS
NOTE
UNICODE AND UTF-8
WORKING WITH UNICODE
WORKING WITH STRINGS
UNINITIALIZED VARIABLES AND THE VALUE NONE IN PYTHON
SLICING AND SPLICING STRINGS
SEARCH AND REPLACE A STRING IN OTHER STRINGS
REMOVE LEADING AND TRAILING CHARACTERS
PRINTING TEXT WITHOUT NEWLINE CHARACTERS
TEXT ALIGNMENT
WORKING WITH DATES
EXCEPTION HANDLING IN PYTHON
HANDLING USER INPUT
NOTE
COMMAND-LINE ARGUMENTS
PRECEDENCE OF OPERATORS IN PYTHON
PYTHON RESERVED WORDS
WORKING WITH LOOPS IN PYTHON
NESTED LOOPS
THE SPLIT() FUNCTION WITH FOR LOOPS
USING THE SPLIT() FUNCTION TO COMPARE WORDS
USING THE SPLIT() FUNCTION TO PRINT JUSTIFIED TEXT
USING THE SPLIT() FUNCTION TO PRINT FIXED WIDTH TEXT
USING THE SPLIT() FUNCTION TO COMPARE TEXT STRINGS
USING THE SPLIT() FUNCTION TO DISPLAY CHARACTERS IN A STRING
THE JOIN() FUNCTION
PYTHON WHILE LOOPS
CONDITIONAL LOGIC IN PYTHON
THE BREAK/CONTINUE/PASS STATEMENTS
COMPARISON AND BOOLEAN OPERATORS
LOCAL AND GLOBAL VARIABLES
SCOPE OF VARIABLES
PASS BY REFERENCE VERSUS VALUE
ARGUMENTS AND PARAMETERS
USING A WHILE LOOP TO FIND THE DIVISORS OF A NUMBER
USER-DEFINED FUNCTIONS IN PYTHON
SPECIFYING DEFAULT VALUES IN A FUNCTION
FUNCTIONS WITH A VARIABLE NUMBER OF ARGUMENTS
LAMBDA EXPRESSIONS
RECURSION
WORKING WITH LISTS
SORTING LISTS OF NUMBERS AND STRINGS
EXPRESSIONS IN LISTS
CONCATENATING A LIST OF WORDS
THE PYTHON RANGE() FUNCTION
ARRAYS AND THE APPEND() FUNCTION
WORKING WITH LISTS AND THE SPLIT() FUNCTION
COUNTING WORDS IN A LIST
ITERATING THROUGH PAIRS OF LISTS
OTHER LIST-RELATED FUNCTIONS
WORKING WITH VECTORS
WORKING WITH MATRICES
QUEUES
TUPLES (IMMUTABLE LISTS)
NOTE
SETS
NOTE
DICTIONARIES
NOTE
DICTIONARY FUNCTIONS AND METHODS
DICTIONARY FORMATTING
ORDERED DICTIONARIES
OTHER SEQUENCE TYPES IN PYTHON
MUTABLE AND IMMUTABLE TYPES IN PYTHON
THE TYPE() FUNCTION
SUMMARY
CHAPTER 5 INTRODUCTION TO NUMPY Chevron down icon Chevron up icon
CHAPTER 6 INTRODUCTION TO PANDAS Chevron down icon Chevron up icon
CHAPTER 7 INTRODUCTION TO R Chevron down icon Chevron up icon
CHAPTER 7 INTRODUCTION TO R
WHAT IS R?
VARIABLE NAMES, OPERATORS, AND DATA TYPES IN R
WORKING WITH STRINGS IN R
WORKING WITH VECTORS IN R
WORKING WITH LISTS IN R
WORKING WITH MATRICES IN R (1)
WORKING WITH MATRICES IN R (2)
WORKING WITH MATRICES IN R (3)
WORKING WITH MATRICES IN R (4)
WORKING WITH MATRICES IN R (5)
UPDATING MATRIX ELEMENTS
LOGICAL CONSTRAINTS AND MATRICES
WORKING WITH MATRICES IN R (6)
COMBINING VECTORS, MATRICES, AND LISTS IN R
WORKING WITH DATES IN R
THE SEQ FUNCTION IN R
BASIC CONDITIONAL LOGIC
COMPOUND CONDITIONAL LOGIC
WORKING WITH USER INPUT
A TRY/CATCH BLOCK IN R
LINEAR REGRESSION IN R
WORKING WITH SIMPLE LOOPS IN R
WORKING WITH NESTED LOOPS IN R
WORKING WITH WHILE LOOPS IN R
WORKING WITH CONDITIONAL LOGIC IN R
ADD A SEQUENCE OF NUMBERS IN R
CHECK IF A NUMBER IS PRIME IN R
CHECK IF NUMBERS IN AN ARRAY ARE PRIME IN R
CHECK FOR LEAP YEARS IN R
WELL-FORMED TRIANGLE VALUES IN R
WHAT ARE FACTORS IN R?
WHAT ARE DATA FRAMES IN R?
WORKING WITH DATA FRAMES IN R (1)
WORKING WITH DATA FRAMES IN R (2)
WORKING WITH DATA FRAMES IN R (3)
SORT A DATA FRAME BY COLUMN
READING EXCEL FILES IN R
READING SQLITE TABLES IN R
READING TEXT FILES IN R
SAVING AND RESTORING OBJECTS IN R
DATA VISUALIZATION IN R
WORKING WITH BAR CHARTS IN R (1)
WORKING WITH BAR CHARTS IN R (2)
WORKING WITH LINE GRAPHS IN R
WORKING WITH FUNCTIONS IN R
MATH-RELATED FUNCTIONS IN R
SOME OPERATORS AND SET FUNCTIONS IN R
THE “APPLY FAMILY” OF BUILT-IN FUNCTIONS
THE DPLYR PACKAGE IN R
THE PIPE OPERATOR %>%
WORKING WITH CSV FILES IN R
WORKING WITH XML IN R
READING AN XML DOCUMENT INTO AN R DATA FRAME
WORKING WITH JSON IN R
READING A JSON FILE INTO AN R DATA FRAME
STATISTICAL FUNCTIONS IN R
SUMMARY FUNCTIONS IN R
DEFINING A CUSTOM FUNCTION IN R
RECURSION IN R
CALCULATING FACTORIAL VALUES IN R (NON-RECURSIVE)
CALCULATING FACTORIAL VALUES IN R (RECURSIVE)
CALCULATING FIBONACCI NUMBERS IN R (NON-RECURSIVE)
CALCULATING FIBONACCI NUMBERS IN R (RECURSIVE)
CONVERT A DECIMAL INTEGER TO A BINARY INTEGER IN R
CALCULATING THE GCD OF TWO INTEGERS IN R
CALCULATING THE LCM OF TWO INTEGERS IN R
SUMMARY
CHAPTER 8 REGULAR EXPRESSIONS Chevron down icon Chevron up icon
CHAPTER 9 SQL AND NOSQL Chevron down icon Chevron up icon
CHAPTER 10 DATA VISUALIZATION Chevron down icon Chevron up icon
INDEX Chevron down icon Chevron up icon
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