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Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
| Artificial intelligence, machine learning, and deep learning / / Oswald Campesato |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020] |
| Descrizione fisica | 1 online resource (338 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Machine learning |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-68392-465-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467240303321 |
Campesato Oswald
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| Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
| Artificial intelligence, machine learning, and deep learning / / Oswald Campesato |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020] |
| Descrizione fisica | 1 online resource (338 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Machine learning |
| ISBN | 1-68392-465-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910793935203321 |
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Campesato Oswald
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| Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
| Artificial intelligence, machine learning, and deep learning / / Oswald Campesato |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020] |
| Descrizione fisica | 1 online resource (338 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Machine learning |
| ISBN | 1-68392-465-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910809066003321 |
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Campesato Oswald
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| Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bash for Data Scientists
| Bash for Data Scientists |
| Autore | Campesato Oswald |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2022 |
| Descrizione fisica | 1 online resource (293 pages) |
| Disciplina | 005.43 |
| Soggetto topico | COMPUTERS / Programming Languages / Python |
| Soggetto non controllato |
Computer Science
Data Science Pandas Programming Python UNIX awk data mining grep sed |
| ISBN |
9781683929710
1683929713 9781683929727 1683929721 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Bash for Data Scientists -- CONTENTS -- PREFACE -- WHAT IS THE GOAL? -- IS THIS BOOK IS FOR ME AND WHAT WILL I LEARN? -- HOW WERE THE CODE SAMPLES CREATED? -- WHAT YOU NEED TO KNOW FOR THIS BOOK -- WHICH BASH COMMANDS ARE EXCLUDED? -- HOW DO I SET UP A COMMAND SHELL? -- WHAT ARE THE "NEXT STEPS" AFTER FINISHING THIS BOOK? -- CHAPTER 1 INTRODUCTION -- WHAT IS UNIX? -- Available Shell Types -- WHAT IS BASH? -- Getting Help for Bash Commands -- Navigating Around Directories -- The history Command -- LISTING FILENAMES WITH THE LS COMMAND -- DISPLAYING CONTENTS OF FILES -- The cat Command -- The head and tail Commands -- The Pipe Symbol -- The fold Command -- FILE OWNERSHIP: OWNER, GROUP, AND WORLD -- HIDDEN FILES -- HANDLING PROBLEMATIC FILENAMES -- WORKING WITH ENVIRONMENT VARIABLES -- The env Command -- Useful Environment Variables -- Setting the PATH Environment Variable -- Specifying Aliases and Environment Variables -- FINDING EXECUTABLE FILES -- THE printf COMMAND AND THE echo COMMAND -- THE cut COMMAND -- THE echo COMMAND AND WHITESPACES -- COMMAND SUBSTITUTION ("BACK TICK") -- THE PIPE SYMBOL AND MULTIPLE COMMA -- USING A SEMICOLON TO SEPARATE COMMANDS -- THE paste COMMAND -- Inserting Blank Lines with the paste Command -- A SIMPLE USE CASE WITH THE paste COMMAND -- A SIMPLE USE CASE WITH cut AND paste COMMANDS -- WORKING WITH META CHARACTERS -- WORKING WITH CHARACTER CLASSES -- WHAT ABOUT ZSH? -- Switching between bash and zsh -- Configuring zsh -- SUMMARY -- CHAPTER 2 FILES AND DIRECTORIES -- CREATE, COPY, REMOVE, AND MOVE FILES -- Creating Files -- Copying Files -- Copy Files with Command Substitution -- Deleting Files -- Moving Files -- THE BASENAME, DIRNAME, AND FILE COMMANDS -- THE wc COMMAND -- THE more COMMAND AND THE less COMMAND -- THE head COMMAND -- THE tail COMMAND -- FILE COMPARISON COMMANDS -- THE PARTS OF A FILENA.
WORKING WITH FILE PERMISSIONS -- The chmod Command -- The chown Command -- The chgrp Command -- The umask and ulimit Commands -- WORKING WITH DIRECTORIES -- Absolute and Relative Directories -- Absolute and Relative Path Names -- Creating Directories -- Removing Directories -- Changing Directories -- Renaming Directories -- USING QUOTE CHARACTERS -- STREAMS AND REDIRECTION COMMANDS -- METACHARACTERS AND CHARACTER CLASSES -- Digits and Characters -- Working with "^" and "\" and "!" -- FILENAMES AND METACHARACTERS -- SUMMARY -- CHAPTER 3 USEFUL COMMANDS -- THE join COMMAND -- THE fold COMMAND -- THE split COMMAND -- THE sort COMMAND -- THE uniq COMMAND -- HOW TO COMPARE FILES -- THE od COMMAND -- THE tr COMMAND -- A SIMPLE USE CASE -- THE find COMMAND -- THE tee COMMAND -- FILE COMPRESSION COMMANDS -- The tar command -- The cpio Command -- The gzip and gunzip Commands -- The bunzip2 Command -- The zip Command -- COMMANDS FOR zip FILES AND bz FILES -- INTERNAL FIELD SEPARATOR (IFS) -- DATA FROM A RANGE OF COLUMNS IN A DATASET -- WORKING WITH UNEVEN ROWS IN DATASETS -- THE alias COMMAND -- SUMMARY -- CHAPTER 4 CONDITIONAL LOGIC AND LOOPS -- ARITHMETIC OPERATIONS AND OPERATORS -- WORKING WITH ARRAYS -- ARRAYS AND TEXT FILES -- WORKING WITH VARIABLES -- Assigning Values to Variables -- WORKING WITH OPERATORS FOR STRINGS AND NUMBERS -- THE read COMMAND FOR USER INPUT -- THE test COMMAND FOR VARIABLES, FILES, AND DIRECTORIES -- Relational Operators -- Boolean Operators -- String Operators -- File Test Operators -- CONDITIONAL LOGIC WITH if/else STATEMENTS -- THE case/esac STATEMENT -- ARITHMETIC OPERATORS AND COMPARISONS -- WORKING WITH STRINGS IN SHELL SCRIPTS -- Working with Strings -- WORKING WITH LOOPS -- Using a for loop -- WORKING WITH NESTED LOOPS -- USING A while LOOP -- THE while, case, AND if/elif/fi STATEMENTS -- USING AN UNTIL LOOP. USER-DEFINED FUNCTIONS -- CREATING A SIMPLE MENU FROM SHELL COMMANDS -- SUMMARY -- CHAPTER 5 PROCESSING DATASETS WITH GREPAND SED -- WHAT IS THE grep COMMAND? -- METACHARACTERS AND THE grep COMMAND -- ESCAPING METACHARACTERS WITH THE grep COMMAND -- USEFUL OPTIONS FOR THE grep COMMAND -- Character Classes and the grep Command -- WORKING WITH THE -C OPTION IN grep -- MATCHING A RANGE OF LINES -- USING BACK REFERENCES IN THE grep COMMAND -- FINDING EMPTY LINES IN DATASETS -- USING KEYS TO SEARCH DATASETS -- THE BACKSLASH CHARACTER AND THE grep COMMAND -- MULTIPLE MATCHES IN THE GREP COMMAND -- THE grep COMMAND AND THE xargs COMMAND -- Searching zip Files for a String -- CHECKING FOR A UNIQUE KEY VALUE -- Redirecting Error Messages -- THE egrep COMMAND AND fgrep COMMAND -- Displaying "Pure" Words in a Dataset with egrep -- Redirecting Error Messages -- THE egrep COMMAND AND fgrep COMMAND -- Displaying "Pure" Words in a Dataset with egrep -- The fgrep Command -- DELETE ROWS WITH MISSING VALUES -- A SIMPLE USE CASE -- WHAT IS THE sed COMMAND? -- The sed Execution Cycle -- MATCHING STRING PATTERNS USING sed -- SUBSTITUTING STRING PATTERNS USING sed -- Replacing Vowels from a String or a File -- Deleting Multiple Digits and Letters from a String -- SEARCH AND REPLACE WITH sed -- DATASETS WITH MULTIPLE DELIMITERS -- USEFUL SWITCHES IN sed -- WORKING WITH DATASETS -- Printing Lines -- Character Classes and sed -- Removing Control Characters -- COUNTING WORDS IN A DATASET -- BACK REFERENCES IN sed -- ONE-LINE sed COMMANDS -- POPULATE MISSING VALUES WITH THE sed COMMAND -- A DATASET WITH 1,000,000 ROWS -- Numeric Comparisons -- Counting Adjacent Digits -- Average Support Rate -- SUMMARY -- CHAPTER 6 PROCESSING DATASETS WITH AWK -- THE awk COMMAND -- Built-in Variables that Control awk -- How Does the awk Command Work? -- ALIGNING TEXT WITH THE printf COMMAND. CONDITIONAL LOGIC AND CONTROL STATEMENTS -- The while Statement -- A for loop in awk -- A for loop with a break Statement -- The next and continue Statements -- DELETING ALTERNATE LINES IN DATASETS -- MERGING LINES IN DATASETS -- Printing File Contents as a Single Line -- Joining Groups of Lines in a Text File -- Joining Alternate Lines in a Text File -- MATCHING WITH METACHARACTERS AND CHARACTER SETS -- PRINTING LINES USING CONDITIONAL LOGIC -- SPLITTING FILENAMES WITH awk -- WORKING WITH POSTFIX ARITHMETIC OPERATORS -- NUMERIC FUNCTIONS IN awk -- ONE-LINE awk COMMANDS -- USEFUL SHORT awk SCRIPTS -- PRINTING THE WORDS IN A TEXT STRING IN awk -- COUNT OCCURRENCES OF A STRING IN SPECIFIC ROWS -- PRINTING A STRING IN A FIXED NUMBER OF COLUMNS -- PRINTING A DATASET IN A FIXED NUMBER OF COLUMNS -- ALIGNING COLUMNS IN DATASETS -- ALIGNING COLUMNS AND MULTIPLE ROWS IN DATASETS -- DISPLAYING A SUBSET OF COLUMNS IN A TEXT FILE -- SUBSETS OF COLUMN-ALIGNED ROWS IN DATASETS -- COUNTING WORD FREQUENCY IN DATASETS -- DISPLAYING ONLY "PURE" WORDS IN A DATASET -- DELETE ROWS WITH MISSING VALUES -- WORKING WITH MULTI-LINE RECORDS IN AWK -- A SIMPLE USE CASE -- ANOTHER USE CASE -- A DATASET WITH 1,000,000 ROWS -- Counting Adjacent Digits -- Average Support Rate -- SUMMARY -- CHAPTER 7 PROCESSING DATASETS (PANDAS) -- PREREQUISITES FOR THIS CHAPTER -- ANALYZING MISSING DATA -- Causes of Missing Data -- PANDAS, CSV FILES, AND MISSING DATA -- Single Column CSV Files -- Two Column CSV Files -- MISSING DATA AND IMPUTATION -- Counting Missing Data Values -- Drop Redundant Columns -- Remove Duplicate Rows -- Display Duplicate Rows -- Uniformity of Data Values -- Too Many Missing Data Values -- Categorical Data -- Data Inconsistency -- Mean Value Imputation -- Random Value Imputation -- Multiple Imputation -- Matching and Hot Deck Imputation. Is a Zero Value Valid or Invalid? -- SKEWED DATASETS -- CSV FILES WITH MULTI-ROW RECORDS -- COLUMN SUBSET AND ROW SUBRANGE OF THE TITANIC CSV FILE -- DATA NORMALIZATION -- Assigning Classes to Data -- Other Data Cleaning Tasks -- DeepChecks and Data Validation -- HANDLING CATEGORICAL DATA -- Processing Inconsistent Categorical Data -- Mapping Categorical Data to Numeric Values -- Mapping Categorical Data to One Hot Encoded Values -- WORKING WITH CURRENCY -- WORKING WITH DATES -- Find Missing Dates -- Find Unique Dates -- Switch Date Formats -- WORKING WITH IMBALANCED DATASETS -- Data Sampling Techniques -- Removing Noisy Data -- Cost-sensitive Learning -- Detecting Imbalanced Data -- Rebalancing Datasets -- Specify stratify in Data Splits -- WHAT IS SMOTE? -- DATA WRANGLING -- Data Transformation: What Does This Mean? -- A DATASET WITH 1,000,000 ROWS -- Dataset Details -- Numeric Comparisons -- Counting Adjacent Digits -- SAVING CSV DATA TO XML, JSON, AND HTML FILES -- SUMMARY -- CHAPTER 8 NOSQL, SQLITE, AND PYTHON -- NON-RELATIONAL DATABASE SYSTEMS -- Advantages of Non-relational Databases -- WHAT IS NOSQL? -- What is NewSQL? -- RDBMS VERSUS NOSQL: WHICH ONE TO USE? -- Good Data Types for NoSQL -- Some Guidelines for Selecting a Database -- NoSQL Databases -- WHAT IS MONGODB? -- Features of MongoDB -- Installing MongoDB -- Launching MongoDB -- USEFUL MONGO APIS -- Metacharacters in Mongo Queries -- MONGODB COLLECTIONS AND DOCUMENTS -- Document Format in MongoDB -- CREATE A MONGODB COLLECTION -- WORKING WITH MONGODB COLLECTIONS -- Find All Android Phones -- Find All Android Phones in 2018 -- Insert a New Item (Document) -- Update an Existing Item (Document) -- Calculate the Average Price for Each Brand -- Calculate the Average Price for Each Brand in 2019 -- Import Data with mongoimport -- WHAT IS FUGUE? -- WHAT IS COMPASS? -- WHAT IS PYMONGO?. MYSQL, SQLALCHEMY, AND PANDAS. |
| Record Nr. | UNINA-9911006689403321 |
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Campesato Oswald
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| Bloomfield : , : Mercury Learning & Information, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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D3 / / Oswald Campesato
| D3 / / Oswald Campesato |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016 |
| Descrizione fisica | 1 online resource (300 pages) : illustrations |
| Disciplina | 006.312 |
| Collana | Pocket Primer |
| Soggetto topico |
Information visualization - Computer programs
Computer graphics - Computer programs |
| ISBN | 1-942270-69-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795935003321 |
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Campesato Oswald
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| Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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D3 / / Oswald Campesato
| D3 / / Oswald Campesato |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016 |
| Descrizione fisica | 1 online resource (300 pages) : illustrations |
| Disciplina | 006.312 |
| Collana | Pocket Primer |
| Soggetto topico |
Information visualization - Computer programs
Computer graphics - Computer programs |
| ISBN | 1-942270-69-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910829000503321 |
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Campesato Oswald
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| Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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D3 : Data Driven Documents
| D3 : Data Driven Documents |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Bloomfield, : Mercury Learning & Information |
| ISBN | 1-942270-69-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910466464703321 |
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Campesato Oswald
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| Bloomfield, : Mercury Learning & Information | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Structures in Java
| Data Structures in Java |
| Autore | Campesato Oswald |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2023 |
| Descrizione fisica | 1 online resource (248 pages) |
| Disciplina | 005.133 |
| Soggetto topico |
Data structures (Computer science)
Java (Computer program language) COMPUTERS / Programming Languages / Java |
| Soggetto non controllato |
XOR
arrays business combinatorics computer science data analysis queues recursion stacks strings |
| ISBN |
1-68392-953-5
1-68392-954-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Java -- A Very Brief Introduction to Java -- Downloading a Java Release (Short Version) -- Selecting a Version of Java (Detailed Version) -- Java 8 and Java 11 -- Java Version Numbers -- JRE Versus a JDK -- Java Distributions -- Java IDEs -- Data Types, Operators, and Their Precedence -- Java Comments -- Java Operators -- Creating and Compiling Java Classes -- "Hello World" and Working With Numbers -- The Java String Class -- Java Strings With Metacharacters -- The Java New Operator -- Equality of Strings -- Comparing Strings -- Searching for a Substring in Java -- Useful String Methods in Java -- Parsing Strings in Java -- Conditional Logic in Java -- Determining Leap Years -- Finding the Divisors of a Number -- Checking for Palindromes -- Working With Arrays of Strings -- Working With the StringBuilder Class -- Static Methods in Java -- Other Static Types in Java -- Summary -- Chapter 2: Recursion and Combinatorics -- What Is Recursion? -- Arithmetic Series -- Calculating Arithmetic Series (Iterative) -- Calculating Arithmetic Series (Recursive) -- Calculating Partial Arithmetic Series -- Geometric Series -- Calculating a Geometric Series (Iterative) -- Calculating Geometric Series (Recursive) -- Factorial Values -- Calculating Factorial Values (Iterative) -- Calculating Factorial Values (Recursive) -- Calculating Factorial Values (Tail Recursion) -- Fibonacci Numbers -- Calculating Fibonacci Numbers (Recursive) -- Calculating Fibonacci Numbers (Iterative) -- Task: Reverse a String via Recursion -- Task: Check for Balanced Parentheses -- Task: Calculate the Number of Digits -- Task: Determine if a Positive Integer is Prime -- Task: Find the Prime Divisors of a Positive Integer -- Task: Goldbach's Conjecture.
Task: Calculate the GCD (Greatest Common Divisor) -- Task: Calculate the LCM (Lowest Common Multiple) -- What Is Combinatorics? -- Working With Permutations -- Working With Combinations -- The Number of Subsets of a Finite Set -- Task: Subsets Containing a Value Larger Than k -- Summary -- Chapter 3: Strings and Arrays -- Time and Space Complexity -- Task: Maximum and Minimum Powers of an Integer -- Task: Binary Substrings of a Number -- Task: Common Substring of Two Binary Numbers -- Task: Multiply and Divide via Recursion -- Task: Sum of Prime and Composite Numbers -- Task: Count Word Frequencies -- Task: Check if a String Contains Unique Characters -- Task: Insert Characters in a String -- Task: String Permutations -- Task: Check for Palindromes -- Task: Check for Longest Palindrome -- Working With Sequences of Strings -- The Maximum Length of a Repeated Character in a String -- Find a Given Sequence of Characters in a String -- Task: Longest Sequences of Substrings -- The Longest Sequence of Unique Characters -- The Longest Repeated Substring -- Working With 1D Arrays -- Rotate an Array -- Task: Invert Adjacent Array Elements -- Task: Shift Nonzero Elements Leftward -- Task: Sort Array In-Place in O(n) Without a Sort Function -- Task: Generate 0 That Is Three Times More Likely Than a 1 -- Task: Invert Bits in Even and Odd Positions -- Task: Check for Adjacent Set Bits in a Binary Number -- Task: Count Bits in a Range of Numbers -- Task: Find the Right-Most Set Bit in a Number -- Task: The Number of Operations to Make All Characters Equal -- Task: Compute XOR without XOR for Two Binary Numbers -- Task: Swap Adjacent Bits in Two Binary Numbers -- Working With 2D Arrays -- The Transpose of a Matrix -- Summary -- Chapter 4: Search and Sort Algorithms -- Search Algorithms -- Linear Search -- Binary Search Walk-Through. Binary Search (Iterative Solution) -- Binary Search (Recursive Solution) -- Well-Known Sorting Algorithms -- Bubble Sort -- Find Anagrams in a List of Words -- Selection Sort -- Insertion Sort -- Comparison of Sort Algorithms -- Merge Sort -- Merge Sort With a Third Array -- Merge Sort Without a Third Array -- Merge Sort: Shift Elements From End of Lists -- How Does Quick Sort Work? -- Quick Sort Code Sample -- Shell Sort -- Task: Sorted Arrays and the Sum of Two Numbers -- Summary -- Chapter 5: Linked Lists (1) -- Types of Data Structures -- Linear Data Structures -- Nonlinear Data Structures -- Data Structures and Operations -- Operations on Data Structures -- What Are Singly Linked Lists? -- Tradeoffs for Linked Lists -- Singly Linked Lists: Create and Append Operations -- A Node Class for Singly Linked Lists -- Java Code for Appending a Node -- Finding a Node in a Linked List -- Appending a Node in a Linked List -- Finding a Node in a Linked List (Method 2) -- Singly Linked Lists: Update and Delete Operations -- Updating a Node in a Singly Linked List -- Java Code to Update a Node -- Deleting a Node in a Linked List -- Java Code for Deleting a Node -- Java Code for a Circular Linked List -- Java Code for Updating a Circular Linked List -- Working With Doubly Linked Lists (DLL) -- A Node Class for Doubly Linked Lists -- Appending a Node in a Doubly Linked List -- Java Code for Appending a Node -- Java Code for Inserting a New Root Node -- Java Code for Inserting an Intermediate Node -- Traversing the Nodes in a Doubly Linked List -- Updating a Node in a Doubly Linked List -- Java Code to Update a Node -- Deleting a Node in a Doubly Linked List -- Java Code to Delete a Node -- Summary -- Chapter 6: Linked Lists (2) -- Task: Adding Numbers in a Linked List (1) -- Task: Adding Numbers in a Linked List (2) -- Task: Adding Numbers in a Linked List (3). Task: Display the First k Nodes -- Task: Display the Last k Nodes -- Reverse a Singly Linked List via Recursion -- Task: Remove Duplicates -- Task: Concatenate Two Lists -- Task: Merge Two Ordered Linked Lists -- Task: Split an Ordered List Into Two Lists -- Task: Remove a Given Node from a List -- Task: Find the Middle Element in a List -- Task: Reverse a Linked List -- Task: Check for Palindrome in a Linked List -- Summary -- Chapter 7: Queues and Stacks -- What Is a Queue? -- Types of Queues -- Creating a Queue Using an Array List -- Creating a Queue Using an Array List -- Other Types of Queues -- What Is a Stack? -- Use Cases for Stacks -- Operations With Stacks -- Working With Stacks -- Task: Reverse and Print Stack Values -- Task: Display the Min and Max Stack Values (1) -- Task: Reverse a String Using a Stack -- Task: Find Stack Palindromes -- Task: Balanced Parentheses -- Task: Tokenize Arithmetic Expressions -- Task: Classify Tokens in Arithmetic Expressions -- Infix, Prefix, and Postfix Notations -- Summary -- Index. |
| Record Nr. | UNINA-9910861061603321 |
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Campesato Oswald
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| Bloomfield : , : Mercury Learning & Information, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Wrangling Using Pandas, SQL, and Java
| Data Wrangling Using Pandas, SQL, and Java |
| Autore | Campesato Oswald |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2022 |
| Descrizione fisica | 1 online resource (275 pages) |
| Disciplina | 001.642 |
| Soggetto topico |
Computer programming
Pandas |
| ISBN |
9781683929024
1683929020 9781683929031 1683929039 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python easy_install and pip virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-Lines -- Quotation and Comments -- Saving Your Code in a Module -- Some Standard Modules -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers -- Working with Other Bases -- The chr() Function -- The round() Function in Python -- Formatting Numbers in Python -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings in Python -- Uninitialized Variables and the Value None -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text Without NewLine Characters -- Text Alignmet -- Working with Dates -- Converting Strings to Dates -- Exception Handling -- Handling User Input -- Command-Line Arguments -- Summary -- Chapter 2: Working with DataDealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data vs. Continuous Data -- "Binning" Continuous Data -- Scaling Numeric Data via Normalization -- ScalingNumeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- MappingCategorical Data to Numeric Values -- Working with Dates -- Working with Currency -- Working with Outliers and Anomalies --Outlier Detection/Removal -- Finding Outliers with NumPy -- Finding Outliers with Pandas -- Calculating Z-Scores to Find Outliers --Finding Outliers with SkLearn (Optional) -- Working with Missing Data -- Imputing Values: When is Zero a Valid Value? -- Dealing with Imbalanced Datasets -- What is SMOTE? -- SMOTE Extensions -- The Bias-Variance Tradeoff -- Types of Bias in Data -- Analyzing Classifiers (Optional) -- What is LIME? -- What is ANOVA? -- Summary -- Chapter 3: Introduction to Pandas -- What is Pandas? -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- A Pandas Data Frame Example -- Describing a Pandas Data Frame -- Pandas Boolean Data Frames -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Converting Categorical Data to Numeric Data -- Merging and Splitting Columns in Pandas -- Combining Pandas Data Frames -- Data Manipulation with Pandas Data Frames -- Pandas Data Frames and CSV Files -- Useful Options for the Pandas read_csv() Function -- Reading Selected Rows from CSV Files -- Pandas Data Frames and Excel Spreadsheets -- Useful Options for Reading Excel Spreadsheets -- Select, Add, and Delete Columns in Data Frames -- Handling Outliers in Pandas -- Pandas Data Frames and Simple Statistics -- Finding Duplicate Rows in Pandas -- Finding Missing Values in Pandas -- Missing Values in an Iris-Based Dataset -- Sorting Data Frames in Pandas -- Working with groupby() in Pandas -- Aggregate Operations with the titanic.csv Dataset -- Working with apply() and mapapply() in Pandas -- Working with JSON-based Data -- Python Dictionary and JSON -- Python, Pandas, and JSON -- Summary -- Chapter 4: RDBMS and SQL -- What is an RDBMS? -- What Relationships Do Tables Have in an RDBMS? -- Features of an RDBMS -- What is ACID? -- When Do We Need an RDBMS? -- The Importance of Normalization -- A Four-Table RDBMS -- Detailed Table Descriptions -- The customers Table -- The purchase_orders Table -- The line_items Table -- The item_desc Table -- What is SQL? -- DCL, DDL, DQL, DML, and TCL -- SQL Privileges -- Properties of SQL Statements -- The CREATE Keyword -- What about MariaDB? -- Summary -- Index. |
| Record Nr. | UNINA-9911004813803321 |
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Campesato Oswald
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| Bloomfield : , : Mercury Learning & Information, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Dealing with Data Pocket Primer
| Dealing with Data Pocket Primer |
| Autore | Campesato Oswald |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2022 |
| Descrizione fisica | 1 online resource (246 pages) |
| Disciplina | 001.42 |
| Collana | Computing |
| Soggetto topico |
Quantitative research - Reliability
Quantitative research - Data processing |
| Soggetto non controllato |
NLP
Pandas RDBMS SQL computer science data analytics data cleaning data visualization programming python statistics |
| ISBN |
1-5231-4740-7
1-68392-818-0 1-68392-819-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Chapter 1: Introduction to Probability and Statistics -- Chapter 2: Working with Data -- Chapter 3: Introduction to Pandas -- Chapter 4: Introduction to RDBMS and SQL -- Chapter 5: Working with SQL and MySQL -- Chapter 6: NLP and Data Cleaning -- Chapter 7: Data Visualization -- Index |
| Record Nr. | UNINA-9910795724303321 |
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Campesato Oswald
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| Bloomfield : , : Mercury Learning & Information, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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