Coding All-in-One For Dummies
Published on 20. June 2017
Book
Paperback/Softback
792 pages
978-1-119-36302-6 (ISBN)
Description
See all the things coding can accomplish
The demand for people with coding know-how exceeds the number of people who understand the languages that power technology. Coding All-in-One For Dummies gives you an ideal place to start when you're ready to add this valuable asset to your professional repertoire. Whether you need to learn how coding works to build a web page or an application or see how coding drives the data revolution, this resource introduces the languages and processes you'll need to know.
Peek inside to quickly learn the basics of simple web languages, then move on to start thinking like a professional coder and using languages that power big applications. Take a look inside for the steps to get started with updating a website, creating the next great mobile app, or exploring the world of data science. Whether you're looking for a complete beginner's guide or a trusted resource for when you encounter problems with coding, there's something for you!
* Create code for the web
* Get the tools to create a mobile app
* Discover languages that power data science
* See the future of coding with machine learning tools
With the demand for skilled coders at an all-time high, Coding All-in-One For Dummies is here to propel coding newbies to the ranks of professional programmers.
The demand for people with coding know-how exceeds the number of people who understand the languages that power technology. Coding All-in-One For Dummies gives you an ideal place to start when you're ready to add this valuable asset to your professional repertoire. Whether you need to learn how coding works to build a web page or an application or see how coding drives the data revolution, this resource introduces the languages and processes you'll need to know.
Peek inside to quickly learn the basics of simple web languages, then move on to start thinking like a professional coder and using languages that power big applications. Take a look inside for the steps to get started with updating a website, creating the next great mobile app, or exploring the world of data science. Whether you're looking for a complete beginner's guide or a trusted resource for when you encounter problems with coding, there's something for you!
* Create code for the web
* Get the tools to create a mobile app
* Discover languages that power data science
* See the future of coding with machine learning tools
With the demand for skilled coders at an all-time high, Coding All-in-One For Dummies is here to propel coding newbies to the ranks of professional programmers.
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Other editions
New editions
Book
09/2022
Wiley
€36.50
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Additional editions
Nikhil Abraham
Coding All-in-One For Dummies
E-Book
04/2017
Standards Information Network
€27.99
Available for download
Nikhil Abraham
Coding All-in-One For Dummies
E-Book
04/2017
Standards Information Network
€27.99
Available for download
Person
Nikhil Abraham is the CFO of Udacity, an education company that teaches technology skills that help launch or advance a career. Prior to joining Udacity, Nik worked at Codecademy where he taught beginning coders across a variety of professions. He is also author of Coding For Dummies and Getting a Coding Job For Dummies.
Content
Introduction 1
About This Book 2
Foolish Assumptions 2
Icons Used in This Book 3
Beyond the Book 4
Where to Go from Here 4
Book 1: Getting Started with Coding 5
Chapter 1: What Is Coding? 7
Defining What Code Is 8
Understanding What Coding Can Do for You 9
Surveying the Types of Programming Languages 13
Taking a Tour of a Web App Built with Code 16
Chapter 2: Programming for the Web 19
Displaying Web Pages on Your Desktop and Mobile Device 20
Coding Web Applications 26
Coding Mobile Applications 28
Chapter 3: Becoming a Programmer 33
Writing Code Using a Process 33
Picking Tools for the Job 38
Book 2: Career Building with Coding 43
Chapter 1: Exploring Coding Career Paths 45
Augmenting Your Existing Job 46
Finding a New Coding Job 52
Chapter 2: Exploring Undergraduate and Graduate Degrees 59
Getting a College Degree 60
Enrolling in an Advanced Degree Program 65
Interning to Build Credibility 68
Chapter 3: Training on the Job 73
Taking a Work Project to the Next Level 74
Learning on the Job and after Work 75
Freelancing to Build Confidence and Skills 79
Transitioning to a New Role 80
Chapter 4: Coding Career Myths 83
Educational Myths 83
Career Myths 87
Book 3: Basic Web Coding 93
Chapter 1: Exploring Basic HTML 95
What Does HTML Do? 95
Understanding HTML Structure 96
Getting Familiar with Common HTML Tasks and Tags 102
Styling Me Pretty 107
Building Your First Website Using HTML 109
Chapter 2: Getting More Out of HTML 113
Organizing Content on the Page 113
Listing Data 115
Putting Data in Tables 118
Filling Out Forms 124
Practicing More with HTML 127
Chapter 3: Getting Stylish with CSS 129
What Does CSS Do? 129
CSS Structure 131
Common CSS Tasks and Selectors 135
Styling Me Pretty 146
Chapter 4: Next Steps with CSS 151
Styling (More) Elements on Your Page 152
Selecting Elements to Style 157
Aligning and Laying Out Your Elements 163
Writing More Advanced CSS 172
Chapter 5: Building Floating Page Layouts 173
Creating a Basic Two-Column Design 173
Building a Three-Column Design 185
Building a Fixed-Width Layout 193
Building a Centered Fixed-Width Layout 196
Chapter 6: Using Alternative Positioning 201
Working with Absolute Positioning 201
Managing z-index 206
Building a Page Layout with Absolute Positioning 208
Creating a More Flexible Layout 212
Exploring Other Types of Positioning 216
Flexible Box Layout Model 221
Book 4: Advanced Web Coding 231
Chapter 1: Working Faster with Twitter Bootstrap 233
Figuring Out What Bootstrap Does 234
Installing Bootstrap 235
Understanding the Layout Options 236
Coding Basic Web Page Elements 243
Build the Airbnb Home Page 247
Chapter 2: Adding in JavaScript 249
What Does JavaScript Do? 249
Understanding JavaScript Structure 251
Coding Common JavaScript Tasks 253
Writing Your First JavaScript Program 263
Using JavaScript Libraries 267
Chapter 3: Understanding Callbacks and Closures 269
What Are Callbacks? 269
Understanding Closures 274
Using Closures 277
Chapter 4: Embracing AJAX and JSON 279
Working behind the Scenes with AJAX 279
Putting Objects in Motion with JSON 289
Chapter 5: jQuery 295
Writing More and Doing Less 295
Getting Started with jQuery 296
The jQuery Object 297
Is Your Document Ready? 298
Using jQuery Selectors 298
Changing Things with jQuery 300
Events 302
Effects 305
AJAX 309
Book 5: Creating Web Applications 311
Chapter 1: Building Your Own App 313
Building a Location-Based Offer App 313
Following an App Development Process 315
Planning Your First Web Application 316
Exploring the Overall Process 317
Meeting the People Who Bring a Web App to Life 319
Chapter 2: Researching Your First Web Application 325
Dividing the App into Steps 326
Identifying Research Sources 333
Researching the Steps in the McDuck's Offer App 335
Choosing a Solution for Each Step 338
Chapter 3: Coding and Debugging Your First Web Application 341
Getting Ready to Code 342
Coding Your First Web Application 342
Debugging Your App 350
Book 6: Selecting Data Analysis Tools 351
Chapter 1: Wrapping Your Head around Python 353
What Does Python Do? 354
Defining Python Structure 355
Coding Common Python Tasks and Commands 357
Shaping Your Strings 363
Building a Simple Tip Calculator Using Python 365
Chapter 2: Installing a Python Distribution 367
Choosing a Python Distribution with Machine Learning in Mind 368
Installing Python on Linux 371
Installing Python on Mac OS X 372
Installing Python on Windows 374
Downloading the Data Sets and Example Code 378
Chapter 3: Working with Real Data 387
Uploading, Streaming, and Sampling Data 388
Accessing Data in Structured Flat-File Form 392
Reading from a text file 393
Reading CSV delimited format 394
Reading Excel and other Microsoft Office files 396
Sending Data in Unstructured File Form 397
Managing Data from Relational Databases 400
Interacting with Data from NoSQL Databases 401
Accessing Data from the Web 402
Book 7: Evaluating Data 405
Chapter 1: Conditioning Your Data 407
Juggling between NumPy and pandas 408
Validating Your Data 409
Manipulating Categorical Variables 414
Dealing with Dates in Your Data 419
Dealing with Missing Data 421
Slicing and Dicing: Filtering and Selecting Data 424
Concatenating and Transforming 426
Aggregating Data at Any Level 430
Chapter 2: Shaping Data 433
Working with HTML Pages 434
Working with Raw Text 436
Using the Bag of Words Model and Beyond 442
Working with Graph Data 447
Chapter 3: Getting a Crash Course in MatPlotLib 451
Starting with a Graph 452
Setting the Axis, Ticks, Grids 455
Defining the Line Appearance 458
Using Labels, Annotations, and Legends 462
Chapter 4: Visualizing the Data 467
Choosing the Right Graph 468
Creating Advanced Scatterplots 475
Plotting Time Series 478
Plotting Geographical Data 481
Visualizing Graphs 483
Chapter 5: Exploring Data Analysis 489
The EDA Approach 490
Defining Descriptive Statistics for Numeric Data 491
Counting for Categorical Data 495
Creating Applied Visualization for EDA 498
Understanding Correlation 504
Modifying Data Distributions 508
Chapter 6: Exploring Four Simple and Effective Algorithms 511
Guessing the Number: Linear Regression 512
Moving to Logistic Regression 515
Making Things as Simple as Naive Bayes 518
Learning Lazily with Nearest Neighbors 522
Book 8: Essentials of Machine Learning 527
Chapter 1: Introducing How Machines Learn 529
Getting the Real Story about AI 530
Learning in the Age of Big Data 541
Chapter 2: Demystifying the Math behind Machine Learning 553
Working with Data 554
Exploring the World of Probabilities 563
Describing the Use of Statistics 568
Chapter 3: Descending the Right Curve 571
Interpreting Learning as Optimization 572
Exploring Cost Functions 576
Descending the Error Curve 578
Updating by Mini-Batch and Online 581
Chapter 4: Validating Machine Learning 585
Checking Out-of-Sample Errors 586
Getting to Know the Limits of Bias 589
Keeping Model Complexity in Mind 591
Keeping Solutions Balanced 592
Training, Validating, and Testing 595
Resorting to Cross-Validation 596
Looking for Alternatives in Validation 597
Optimizing Cross-Validation Choices 598
Avoiding Sample Bias and Leakage Traps 601
Book 9: Applying Machine Learning 603
Chapter 1: Starting with Simple Learners 605
Discovering the Incredible Perceptron 606
Growing Greedy Classification Trees 610
Taking a Probabilistic Turn 615
Chapter 2: Leveraging Similarity 623
Measuring Similarity between Vectors 624
Using Distances to Locate Clusters 626
Tuning the K-Means Algorithm 630
Searching for Classification by k-Nearest Neighbors 637
Leveraging the Correct K Parameter 638
Chapter 3: Hitting Complexity with Neural Networks 643
Learning and Imitating from Nature 644
Struggling with Overfitting 653
Introducing Deep Learning 657
Chapter 4: Resorting to Ensembles of Learners 661
Leveraging Decision Trees 662
Working with Almost Random Guesses 670
Boosting Smart Predictors 673
Averaging Different Predictors 676
Chapter 5: Real-World Applications 677
Classifying Images 677
Scoring Opinions and Sentiments 691
Using Scoring and Classification 703
Recommending Products and Movies 710
Index 725
About This Book 2
Foolish Assumptions 2
Icons Used in This Book 3
Beyond the Book 4
Where to Go from Here 4
Book 1: Getting Started with Coding 5
Chapter 1: What Is Coding? 7
Defining What Code Is 8
Understanding What Coding Can Do for You 9
Surveying the Types of Programming Languages 13
Taking a Tour of a Web App Built with Code 16
Chapter 2: Programming for the Web 19
Displaying Web Pages on Your Desktop and Mobile Device 20
Coding Web Applications 26
Coding Mobile Applications 28
Chapter 3: Becoming a Programmer 33
Writing Code Using a Process 33
Picking Tools for the Job 38
Book 2: Career Building with Coding 43
Chapter 1: Exploring Coding Career Paths 45
Augmenting Your Existing Job 46
Finding a New Coding Job 52
Chapter 2: Exploring Undergraduate and Graduate Degrees 59
Getting a College Degree 60
Enrolling in an Advanced Degree Program 65
Interning to Build Credibility 68
Chapter 3: Training on the Job 73
Taking a Work Project to the Next Level 74
Learning on the Job and after Work 75
Freelancing to Build Confidence and Skills 79
Transitioning to a New Role 80
Chapter 4: Coding Career Myths 83
Educational Myths 83
Career Myths 87
Book 3: Basic Web Coding 93
Chapter 1: Exploring Basic HTML 95
What Does HTML Do? 95
Understanding HTML Structure 96
Getting Familiar with Common HTML Tasks and Tags 102
Styling Me Pretty 107
Building Your First Website Using HTML 109
Chapter 2: Getting More Out of HTML 113
Organizing Content on the Page 113
Listing Data 115
Putting Data in Tables 118
Filling Out Forms 124
Practicing More with HTML 127
Chapter 3: Getting Stylish with CSS 129
What Does CSS Do? 129
CSS Structure 131
Common CSS Tasks and Selectors 135
Styling Me Pretty 146
Chapter 4: Next Steps with CSS 151
Styling (More) Elements on Your Page 152
Selecting Elements to Style 157
Aligning and Laying Out Your Elements 163
Writing More Advanced CSS 172
Chapter 5: Building Floating Page Layouts 173
Creating a Basic Two-Column Design 173
Building a Three-Column Design 185
Building a Fixed-Width Layout 193
Building a Centered Fixed-Width Layout 196
Chapter 6: Using Alternative Positioning 201
Working with Absolute Positioning 201
Managing z-index 206
Building a Page Layout with Absolute Positioning 208
Creating a More Flexible Layout 212
Exploring Other Types of Positioning 216
Flexible Box Layout Model 221
Book 4: Advanced Web Coding 231
Chapter 1: Working Faster with Twitter Bootstrap 233
Figuring Out What Bootstrap Does 234
Installing Bootstrap 235
Understanding the Layout Options 236
Coding Basic Web Page Elements 243
Build the Airbnb Home Page 247
Chapter 2: Adding in JavaScript 249
What Does JavaScript Do? 249
Understanding JavaScript Structure 251
Coding Common JavaScript Tasks 253
Writing Your First JavaScript Program 263
Using JavaScript Libraries 267
Chapter 3: Understanding Callbacks and Closures 269
What Are Callbacks? 269
Understanding Closures 274
Using Closures 277
Chapter 4: Embracing AJAX and JSON 279
Working behind the Scenes with AJAX 279
Putting Objects in Motion with JSON 289
Chapter 5: jQuery 295
Writing More and Doing Less 295
Getting Started with jQuery 296
The jQuery Object 297
Is Your Document Ready? 298
Using jQuery Selectors 298
Changing Things with jQuery 300
Events 302
Effects 305
AJAX 309
Book 5: Creating Web Applications 311
Chapter 1: Building Your Own App 313
Building a Location-Based Offer App 313
Following an App Development Process 315
Planning Your First Web Application 316
Exploring the Overall Process 317
Meeting the People Who Bring a Web App to Life 319
Chapter 2: Researching Your First Web Application 325
Dividing the App into Steps 326
Identifying Research Sources 333
Researching the Steps in the McDuck's Offer App 335
Choosing a Solution for Each Step 338
Chapter 3: Coding and Debugging Your First Web Application 341
Getting Ready to Code 342
Coding Your First Web Application 342
Debugging Your App 350
Book 6: Selecting Data Analysis Tools 351
Chapter 1: Wrapping Your Head around Python 353
What Does Python Do? 354
Defining Python Structure 355
Coding Common Python Tasks and Commands 357
Shaping Your Strings 363
Building a Simple Tip Calculator Using Python 365
Chapter 2: Installing a Python Distribution 367
Choosing a Python Distribution with Machine Learning in Mind 368
Installing Python on Linux 371
Installing Python on Mac OS X 372
Installing Python on Windows 374
Downloading the Data Sets and Example Code 378
Chapter 3: Working with Real Data 387
Uploading, Streaming, and Sampling Data 388
Accessing Data in Structured Flat-File Form 392
Reading from a text file 393
Reading CSV delimited format 394
Reading Excel and other Microsoft Office files 396
Sending Data in Unstructured File Form 397
Managing Data from Relational Databases 400
Interacting with Data from NoSQL Databases 401
Accessing Data from the Web 402
Book 7: Evaluating Data 405
Chapter 1: Conditioning Your Data 407
Juggling between NumPy and pandas 408
Validating Your Data 409
Manipulating Categorical Variables 414
Dealing with Dates in Your Data 419
Dealing with Missing Data 421
Slicing and Dicing: Filtering and Selecting Data 424
Concatenating and Transforming 426
Aggregating Data at Any Level 430
Chapter 2: Shaping Data 433
Working with HTML Pages 434
Working with Raw Text 436
Using the Bag of Words Model and Beyond 442
Working with Graph Data 447
Chapter 3: Getting a Crash Course in MatPlotLib 451
Starting with a Graph 452
Setting the Axis, Ticks, Grids 455
Defining the Line Appearance 458
Using Labels, Annotations, and Legends 462
Chapter 4: Visualizing the Data 467
Choosing the Right Graph 468
Creating Advanced Scatterplots 475
Plotting Time Series 478
Plotting Geographical Data 481
Visualizing Graphs 483
Chapter 5: Exploring Data Analysis 489
The EDA Approach 490
Defining Descriptive Statistics for Numeric Data 491
Counting for Categorical Data 495
Creating Applied Visualization for EDA 498
Understanding Correlation 504
Modifying Data Distributions 508
Chapter 6: Exploring Four Simple and Effective Algorithms 511
Guessing the Number: Linear Regression 512
Moving to Logistic Regression 515
Making Things as Simple as Naive Bayes 518
Learning Lazily with Nearest Neighbors 522
Book 8: Essentials of Machine Learning 527
Chapter 1: Introducing How Machines Learn 529
Getting the Real Story about AI 530
Learning in the Age of Big Data 541
Chapter 2: Demystifying the Math behind Machine Learning 553
Working with Data 554
Exploring the World of Probabilities 563
Describing the Use of Statistics 568
Chapter 3: Descending the Right Curve 571
Interpreting Learning as Optimization 572
Exploring Cost Functions 576
Descending the Error Curve 578
Updating by Mini-Batch and Online 581
Chapter 4: Validating Machine Learning 585
Checking Out-of-Sample Errors 586
Getting to Know the Limits of Bias 589
Keeping Model Complexity in Mind 591
Keeping Solutions Balanced 592
Training, Validating, and Testing 595
Resorting to Cross-Validation 596
Looking for Alternatives in Validation 597
Optimizing Cross-Validation Choices 598
Avoiding Sample Bias and Leakage Traps 601
Book 9: Applying Machine Learning 603
Chapter 1: Starting with Simple Learners 605
Discovering the Incredible Perceptron 606
Growing Greedy Classification Trees 610
Taking a Probabilistic Turn 615
Chapter 2: Leveraging Similarity 623
Measuring Similarity between Vectors 624
Using Distances to Locate Clusters 626
Tuning the K-Means Algorithm 630
Searching for Classification by k-Nearest Neighbors 637
Leveraging the Correct K Parameter 638
Chapter 3: Hitting Complexity with Neural Networks 643
Learning and Imitating from Nature 644
Struggling with Overfitting 653
Introducing Deep Learning 657
Chapter 4: Resorting to Ensembles of Learners 661
Leveraging Decision Trees 662
Working with Almost Random Guesses 670
Boosting Smart Predictors 673
Averaging Different Predictors 676
Chapter 5: Real-World Applications 677
Classifying Images 677
Scoring Opinions and Sentiments 691
Using Scoring and Classification 703
Recommending Products and Movies 710
Index 725