Python Fundamentals: with GenAI, 2nd Edition

Lesson 08 – Strings – A Deeper Look
1 Lesson overview
2 Formatting Strings–Presentation Types
3 Self Check
4 Formatting Strings–Field Widths and Alignment
5 Self Check
6 Formatting Strings–Numeric Formatting
7 Self Check
8 Formatting Strings–String’s format Method
9 Self Check
10 Concatenating and Repeating Strings
11 Self Check
12 Stripping Whitespace from Strings
13 Self Check
14 Changing Character Case
15 Self Check
16 Comparison Operators for Strings
17 Searching for Substrings
18 Self Check
19 Replacing Substrings
20 Self Check
21 Splitting and Joining Strings
22 Self Check
23 Characters and Character-Testing Methods
24 Raw Strings
25 Introduction to Regular Expressions
26 re Module and Function fullmatch Part 1–Matching Literal Characters
27 re Module and Function fullmatch Part 2–Metacharacters, Character Classes and Quantifiers
28 re Module and Function fullmatch Part 3–Custom Character Classes
29 re Module and Function fullmatch Part 1–Quantifiers
30 Self Check
31 Replacing Substrings and Splitting Strings
32 Self Check
33 Other Search Functions; Accessing Matches–Function search – Finding the First Match Anywhere in a St
34 Other Search Functions; Accessing Matches–Ignoring Case with the Optional flags Keyword Argument
35 Other Search Functions; Accessing Matches–Metacharacters that Restrict Matches to the Beginning or E
36 Other Search Functions; Accessing Matches–Functions findall and finditer – Finding All Matches in a
37 Other Search Functions; Accessing Matches–Capturing Substrings in a Match
38 Self Check
39 Intro to Data Science – Pandas, Regular Expressions and Data Munging Part 1 – Introduction
40 Intro to Data Science – Pandas, Regular Expressions and Data Munging Part 3 – Data Validation
41 Intro to Data Science – Pandas, Regular Expressions and Data Munging Part 4 – Reformatting Your Data
42 Self Check

Lesson 09 – Files and Exceptions
43 Lesson overview
44 Files
45 Text-File Processing–Writing to a Text File – Introducing the with Statement
46 Self Check
47 Text-File Processing–Reading Data from a Text File
48 Self Check
49 Updating Text Files
50 Self Check
51 Serialization with JSON–JSON Data Format
52 Serialization with JSON–Serializing an Object to JSON
53 Serialization with JSON–Deserializing a JSON Object into Python
54 Serialization with JSON–Displaying JSON Text
55 Self Check
56 File Open Modes
57 Handling Exceptions
58 Division by Zero and Invalid Input
59 try Statements
60 Self Check
61 finally Clause
62 Self Check
63 Explicitly Raising an Exception
64 Stack Unwinding and Tracebacks
65 Intro to Data Science – Working with CSV Files–Python Standard Library Module csv
66 Self Check
67 Intro to Data Science – Working with CSV Files–Reading CSV Files into Pandas DataFrames
68 Intro to Data Science – Working with CSV Files–Reading the Titanic Disaster Dataset
69 Intro to Data Science – Working with CSV Files–Simple Data Analysis with the Titanic Disaster Dataset
70 Intro to Data Science – Working with CSV Files–Passenger Age Histogram

Lesson 10 – Object-Oriented Programming
71 Lesson overview
72 Custom Class Account–Test-Driving Class Account
73 Custom Class Account–Account Class Definition
74 Self Check
75 Controlling Access to Attributes
76 Properties for Data Access–Test-Driving Class Time
77 Properties for Data Access–Class Time Definition
78 Self Check
79 Properties for Data Access–Class Time Definition Notes
80 Simulating Private Attributes
81 Case Study – Card Shuffling and Dealing Simulation–Test Driving Classes Card and DeckOfCards
82 Case Study – Card Shuffling and Dealing Simulation–Class Card and an Introduction to Class Attri
83 Case Study – Card Shuffling and Dealing Simulation–Class DeckOfCards
84 Case Study – Card Shuffling and Dealing Simulation–Displaying Card Images with Matplotlib
85 Self Check
86 Inheritance – Base Classes and Subclasses
87 Building an Inheritance Hierarchy and Introducing Polymorphism–Base Class CommissionEmployee
88 Building an Inheritance Hierarchy and Introducing Polymorphism–Sublass SalariedCommissionEmploye
89 Building an Inheritance Hierarchy and Introducing Polymorphism–Processing CommissionEmployees an
90 Duck Typing and Polymorphism
91 Operator Overloading
92 Test-Driving Class Complex
93 Class Complex Definition
94 Self Check
95 Named Tuples
96 A Brief Intro to Python 3.7’s New Data Classes
97 A Brief Intro to Python 3.7’s New Data Classes–Creating a Card Data Class
98 A Brief Intro to Python 3.7’s New Data Classes–Using the Card Data Class
99 Self Check
100 A Brief Intro to Python 3.7’s New Data Classes–Advantages Over Named Tuples and Traditional Clas
101 Unit Testing with Docstrings and doctest
102 Self Check
103 Namespaces and Scopes
104 Intro to Data Science – Time Series and Simple Linear Regression–Introduction
105 Intro to Data Science – Time Series and Simple Linear Regression–Components of the Simple Linear
106 Intro to Data Science – Time Series and Simple Linear Regression–Loading the Average High Temper
107 Intro to Data Science – Time Series and Simple Linear Regression–Cleaning the Data
108 Intro to Data Science – Time Series and Simple Linear Regression–Calculating Basic Descriptive S
109 Intro to Data Science – Time Series and Simple Linear Regression–Forecasting Future January Aver
110 Intro to Data Science – Time Series and Simple Linear Regression–Plotting the Average High Tempe

Lesson 11 – Natural Language Processing
111 Lesson overview
112 Introduction
113 TextBlob
114 Create a TextBlob
115 Tokenizing Text into Sentences and Words
116 Parts-of-Speech Tagging
117 Extracting Noun Phrases
118 Sentiment Analysis with TextBlob’s Default Sentiment Analyzer
119 Sentiment Analysis with the NaiveBayesAnalyzer
120 Language Detection and Translation
121 Inflection – Pluralization and Singularization
122 Spell Checking and Correction
123 Normalization – Stemming and Lemmatization
124 Word Frequencies
125 Getting Definitions, Synonyms and Antonyms from WordNet
126 Deleting Stop Word
127 n-grams
128 Visualizing Word Frequencies with Pandas
129 Visualizing Word Frequencies with Word Clouds
130 Readability Assessment with Textatistic
131 Named Entity Recognition with spaCy
132 Similarity Detection with spaCy

Lesson 12 – Data Mining Twitter
133 Lesson overview
134 Introduction
135 Overview of the Twitter APIs
136 Creating a Twitter Developer Account
137 Getting Twitter Credentials–Creating an App
138 What’s in a Tweet
139 Tweepy
140 Authenticating with Twitter Via Tweepy
141 Getting Information About a Twitter Account
142 Self Check
143 Introduction to Tweepy Cursors – Getting an Account’s Followers and Friends
144 Determining an Account’s Followers
145 Self Check
146 Determining Whom an Account Follows
147 Getting a User’s Recent Tweets
148 Self Check
149 Searching Recent Tweets
150 Self Check
151 Spotting Trends – Twitter Trends API
152 Places with Trending Topics
153 Getting a List of Trending Topics
154 Self Check
155 Create a Word Cloud from Trending Topics
156 Self Check
157 CleaningPreprocessing Tweets for Analysis
158 Twitter Streaming API
159 Creating a Subclass of StreamListener
160 Initiating Stream Processing
161 Twitter Restrictions Note
162 Tweet Sentiment Analysis
163 Geocoding and Mapping
164 Getting and Mapping the Tweets
165 Utility Functions in tweetutilities.py and Class LocationListener

Lesson 13 – IBM Watson(r) and Cognitive Computing
166 Lesson overview
167 Introduction to Watson
168 IBM Cloud Account and Cloud Console
169 Watson Services – Watson Assistant Demo
170 Watson Services – Visual Recognition
171 Watson Services – Speech to Text
172 Watson Services – Text to Speech
173 Watson Services – Language Translator
174 Watson Services – Natural Language Understanding
175 Watson Services – Personality Insights
176 Additional Services and Tools
177 Watson Developer Cloud Python SDK
178 Case Study – Traveler’s Companion Translation App
179 Before You run the App
180 Before You run the App – Registering for the Speech to Text Service
181 Before You run the App – Registering for the Text to Speech Service
182 Before You run the App – Registering for the Language Translator Service
183 Test-Driving the App
184 SimpleLanguageTranslator.py Script Walkthrough
185 SimpleLanguageTranslator.py Script Walkthrough – Importing Watson SDK Classes from the
186 SimpleLanguageTranslator.py Script Walkthrough – Other Imported Modules
187 SimpleLanguageTranslator.py Script Walkthrough – Main Program – Function run translator
188 SimpleLanguageTranslator.py Script Walkthrough – Function speech to text
189 SimpleLanguageTranslator.py Script Walkthrough – Function translate
190 SimpleLanguageTranslator.py Script Walkthrough – Function text to speech
191 SimpleLanguageTranslator.py Script Walkthrough – Function record audio
192 SimpleLanguageTranslator.py Script Walkthrough – Function play audio
193 Watson Resources

Lesson 14, Machine Learning – Classification, Regression and Clustering
194 Lesson overview
195 Introduction to Machine Learning
196 Case Study – Classification with k-Nearest Neighbors and the Digi
197 k-Nearest Neighbors Algorithm
198 k-Nearest Neighbors Algorithm – Hyperparameters and Hyperparamete
199 Loading the Dataset
200 Loading the Dataset – Displaying the Description
201 Loading the Dataset – Checking the Sample and Target Sizes
202 Loading the Dataset – A Sample Digit Image
203 Loading the Dataset – Preparing the Data for Use with Scikit-Lear
204 Visualizing the Data
205 Splitting the Data for Training and Testing
206 Creating the Model
207 Training the Model
208 Predicting Digit Classes
209 Case Study – Classification with k-Nearest Neighbors and the Digi
210 Metrics for Model Accuracy – Estimator Method score
211 Metrics for Model Accuracy – Confusion Matrix
212 Metrics for Model Accuracy – Classification Report
213 Metrics for Model Accuracy – Visualizing the Confusion Matrix
214 K-Fold Cross-Validation
215 Running Multiple Models to Find the Best One
216 Hyperparameter Tuning
217 Case Study – Time Series and Simple Linear Regression
218 Loading the Average High Temperatures into a DataFrame
219 Splitting the Data for Training and Testing
220 Training the Model
221 Testing the Model
222 Predicting Future Temperatures and Estimating Past Temperatures
223 Visualizing the Dataset with the Regression Line
224 OverfittingUnderfitting
225 Case Study – Multiple Linear Regression with the California Housi
226 Loading the Dataset
227 Exploring the Data with Pandas
228 Visualizing the Features
229 Splitting the Data for Training and Testing
230 Training the Model
231 Testing the Model
232 Visualizing the Expected vs. Predicted Prices
233 Regression Model Metrics
234 Choosing the Best Model
235 Case Study – Unsupervised Machine Learning, Part 1–Dimensionalit
236 Loading the Digits Dataset
237 Creating a TSNE Estimator for Dimensionality Reduction
238 Transforming the Digits Dataset’s Features into Two Dimensions
239 Visualizing the Reduced Data
240 Visualizing the Reduced Data with Different Colors for Each Digit
241 Visualizing the Reduced Data in 3D
242 Case Study – Unsupervised Machine Learning, Part 2–k-Means
243 Loading the Iris Dataset
244 Exploring the Iris Dataset – Descriptive Statistics with Pandas
245 Visualizing the Dataset with a Seaborn pairplot
246 Using a KMeans Estimator
247 Dimensionality Reduction with Principal Component Analysis
248 Choosing the Best Clustering Estimator

Lesson 15 – Deep Learning
249 Lesson overview
250 Introduction
251 Deep Learning Applications
252 Deep Learning Demos
253 Keras Resources
254 Keras Built-In Datasets
255 Custom Anaconda Environments
256 Neural Networks
257 Tensors
258 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset
259 Reproducibility in Keras and Deep Learning
260 Basic Keras Neural Network
261 Loading the MNIST Dataset
262 Data Exploration
263 Visualizing Digits
264 Reshaping the Image Data
265 Normalizing the Image Data
266 One-Hot Encoding – Converting the Labels From Integers to Categorical Data
267 Creating the Neural Network
268 Adding Layers to the Network
269 Convolution
270 Adding a Conv2D Convolution Layer to Our Model
271 Dimensionality of the First Convolution Layer’s Output
272 Overfitting
273 Adding a Pooling Layer
274 Adding Another Convolutional Layer and Pooling Layer
275 Flattening the Results to One Dimension with a Keras Flatten Layer
276 Adding a Dense Layer to Reduce the Number of Features
277 Adding Another Dense Layer to Produce the Final Output
278 Printing the Model’s Summary
279 Visualizing a Model’s Structure
280 Compiling the Model
281 Training and Evaluating the Model
282 Evaluating the Model on Unseen Data
283 Making Predictions
284 Locating the Incorrect Predictions
285 Visualizing Incorrect Predictions
286 Displaying the Probabilities for Several Incorrect Predictions
287 Saving and Loading a Model
288 Visualizing Neural Network Training with TensorBoard
289 ConvnetJS – Browser-Based Deep-Learning Training and Visualization
290 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset
291 Loading the IMDb Movie Reviews Dataset
292 Data Exploration
293 Movie Review Encodings and Decoding a Review
294 Data Preparation
295 Creating the Neural Network
296 Adding an Embedding Layer
297 Adding an LSTM Layer
298 Adding a Dense Output Layer
299 Compiling the Model and Displaying the Summary
300 Training and Evaluating the Model (1 of 2)
301 Training and Evaluating the Model (2 of 2)
302 Tuning Deep Learning Models

Lesson 16 – Big Data – Hadoop, Spark, NoSQL and IoT
303 Lesson overview
304 Introduction–Databases
305 Introduction–Apache Hadoop and Apache Spark
306 Introduction–Internet of Things
307 Introduction–Experience Cloud and Desktop Big-Data Software
308 Introduction–Big Data Sources
309 Relational Databases and Structured Query Language (SQL)
310 A books Database
311 SELECT Queries
312 WHERE Clause
313 ORDER BY Clause
314 Merging Data from Multiple Tables – INNER JOIN
315 INSERT INTO Statement
316 UPDATE Statement
317 DELETE FROM Statement
318 NoSQL and NewSQL Big-Data Databases – A Brief Tour
319 NoSQL Key–Value Databases
320 NoSQL Document Databases
321 NoSQL Columnar Databases
322 NoSQL Graph Databases
323 NewSQL Databases
324 Case Study – A MongoDB JSON Document Database
325 Creating the MongoDB Atlas Cluster
326 Streaming Tweets into MongoDB
327 Hadoop
328 Hadoop Overview
329 Summarizing Word Lengths in Romeo and Juliet via MapReduce
330 Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight – Part 1
331 Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight – Part 2
332 Hadoop Streaming
333 Implementing the Mapper
334 Implementing the Reducer
335 Preparing to Run the MapReduce Example
336 Running the MapReduce Job
337 Spark Overview
338 Docker and the Jupyter Docker Stacks
339 Word Count with Spark
340 Spark Word Count on Microsoft Azure
341 Spark Streaming – Counting Twitter Hashtags Using the pysparknotebook Docker Stack
342 Streaming Tweets to a Socket
343 Summarizing Tweet Hashtags; Introducing Spark SQL
344 Internet of Things and Dashboards
345 Publish and Subscribe
346 Visualizing a PubNub Sample Live Stream with a Freeboard Dashboard
347 Simulating an Internet-Connected Thermostat in Python and Creating a Dashbboard in Fr
348 Creating a Python PubNub Subscriber

Lesson 18 – Building API-Based Python GenAI Applications
349 Building API-Based Python GenAI Applications – Overview
350 Note – This Lesson Is Under Development
351 Introduction
352 OpenAI APIs
353 OpenAI APIs — OpenAI Developer Account and API Key
354 Text Generation Via the Responses API – Overview
355 Text Summarization
356 Text Summarization – GenAI Prompting Exercises
357 Sentiment Analysis
358 Vision – Accessible Image Descriptions
359 Language Detection and Translation
360 Language Detection and Translation – GenAI Prompting Exercises
361 Code Generation
362 Code Generation – Other AI Code Capabilities
363 Code Generation – GenAI Prompting Exercises
364 Named Entity Recognition (NER) and Structured Outputs
365 Named Entity Recognition (NER) and Structured Outputs – Code and Prompt Exercise
366 Speech Recognition and Speech Synthesis – Overview
367 English Speech-to-Text for Audio Transcription
368 English Speech-to-Text for Audio Transcription – Generative AI Prompt Exercises
369 Text-To-Speech
370 Text-To-Speech – Generative AI Prompting and Coding Exercises
371 Image Generation – Overview
372 Image Generation — Metadata Embedded in Images
373 Image Generation — Generative AI Prompting Exercises
374 Image Style Transfer – Overview
375 Style Transfer via the Images API’s Edit Capability and a Style-Transfer Prompt
376 Style Transfer Via the Responses API’s Image Generation Tool
377 Image Style Transfer – Generative AI Prompting Exercises
378 Generating Closed Captions from a Video’s Audio Track
379 Content Moderation
380 Content Moderation – Generative AI Prompting Exercise
381 Sora Video Generation
382 Closing note

Part 1
383 Introduction to Python Fundamentals – Part 1

Part 2
384 Introduction to Python Fundamentals – Part 2

Part 3
385 Introduction to Python Fundamentals – Part 3

Part 4
386 Introduction to Python Fundamentals – Part 4

Part 5
387 Introduction to Python Fundamentals – Part 5

Recursion, Searching, Sorting and Big O
388 Overview – Recursion, Searching, Sorting and Big O
389 Factorials
390 Recursive Factorial Example—Recursive Problem-Solving
391 Recursive Fibonacci Series Example
392 Recursive Fibonacci Series Example—Complexity Issues
393 Recursive Fibonacci Series Example—Memoization to Increase Recursion Performance
394 Recursion vs. Iteration
395 Searching and Sorting
396 Linear Search
397 Efficiency of Algorithms – Big O
398 Efficiency of Algorithms – Big O—O(1) Algorithms
399 Efficiency of Algorithms – Big O—O(n) Algorithms
400 Efficiency of Algorithms – Big O—O(n^2) Algorithms
401 Efficiency of Algorithms – Big O—Big O of the Linear Search
402 Binary Search Overview
403 Binary Search Implementation
404 Big O of the Binary Search
405 Sorting Algorithms
406 Selection Sort Overview
407 Selection Sort Implementation
408 Big O of the Selection Sort
409 Insertion Sort Overview
410 Insertion Sort Implementation
411 Big O of the Insertion Sort
412 Merge Sort Overview
413 Merge Sort Implementation
414 Big O of the Merge Sort
415 Big O Summary

Watch This First! Python Fundamentals, 2e Sneak Peek
416 Watch This First! Python Fundamentals, 2e Sneak Peek

[New in 2024] What’s New in Python
417 Overview – What’s New in Python
418 Introduction – New Features Related to Lesson 3
419 Format Strings (f-strings)
420 Multiple Selection with the match…case Statement
421 Assignment Expressions—The -= Walrus Operator
422 statistics Function mode Update and Function multimode
423 Introduction – New Features Related to Lesson 4
424 Pattern Matching with match…case
425 Positional-Only Parameters
426 Enums for Creating Named Constants
427 Introduction – New Features Related to Lesson 5
428 Self-Documenting f-strings
429 Starred Unpacking Expressions in for Statements
430 Comprehension Inlining for Better Performance
431 Optional Length-Checking for zip
432 Introduction – New Features Related to Lesson 6
433 Dictionary Union Operators
434 Keyword-Only Parameters and Arbitrary Keyword Arguments
435 Introduction – New Features Related to Lesson 8
436 Removing String Prefixes and Suffixes
437 Named Unicode Characters in Regular Expressions
438 Introduction – New Features Related to Lesson 9
439 Adding Notes to Exceptions
440 Exception Groups and except
441 Introduction – New Features Related to Lesson 10
442 Type Annotations for kwargs
443 Type Union Operator for Type Hints
444 Built-in Collection Types in Type Hints
445 @override Decorator for Overridden Methods in Subclasses

[Updated in 2025] Before You Begin
446 Lesson overview
447 Getting the Code Examples
448 Structure of the examples Folder
449 Installing Anaconda
450 Updating Anaconda
451 Package managers
452 Creating a Custom Environment
453 Getting your questions answered
454 Keeping in Touch

[Updated in 2025] Lesson 01 – Test-Driving IPython and Jupyter Notebooks
455 Lesson overview
456 Using IPython Interactive Mode as a Calculator
457 Executing a Python Program Using the IPython Interpreter
458 Writing and Executing Code in a Jupyter Notebook—Overview
459 Writing and Executing Code in a Jupyter Notebook—Opening Jupyter
460 Writing and Executing Code in a Jupyter Notebook—Creating a New
461 Writing and Executing Code in a Jupyter Notebook—Adding and Exec
462 Writing and Executing Code in a Jupyter Notebook—Opening and Exe
463 Writing and Executing Code in a Jupyter Notebook—Closing Jupyter
464 Writing and Executing Code in a Jupyter Notebook—Google Colab

[Updated in 2025] Lesson 02 – Intro to Python Programming
465 Lesson overview
466 Variables and Assignment Statements
467 Variables and Assignment Statements – Self Check
468 Variables and Assignment Statements — Generative AI Prompts
469 Arithmetic
470 Self Check
471 Arithmetic — Generative AI Prompts
472 Function print and an Intro to Single- and Double-Quoted Strings
473 Self Check
474 Function print and an Intro to Single- and Double-Quoted Strings — Generative A
475 Triple-Quoted Strings
476 Self Check
477 Triple-Quoted Strings — Generative AI Prompts
478 Getting Input from the User
479 Self Check
480 Getting Input from the User — Generative AI Prompts
481 Decision Making – The if Statement and Comparison Operators
482 Self Check
483 Decision Making – The if Statement and Comparison Operators — Generative AI Pro
484 Objects and Dynamic Typing
485 Self Check
486 Objects and Dynamic Typing — Generative AI Prompts
487 Objects-Natural Case Study – Creating and Using Objects of the Python str Class
488 Self Check
489 Objects-Natural Case Study – Creating and Using Objects of the Python Class — G
490 Intro to Data Science – Basic Descriptive Statistics
491 Self Check
492 Intro to Data Science – Basic Descriptive Statistics — Generative AI Prompts

[Updated in 2026] Lesson 03 – Control Statements
493 Lesson overview – Control Statements
494 Keywords
495 if Statement
496 Checkpoint – if Statement
497 GenAI Prompt Exercise – if Statement
498 if…else and if…elif…else Statements
499 Checkpoint – if…else and if…elif…else Statements
500 GenAI Prompt Exercise – if…else and if…elif…else Statements
501 match…case Selection Statement
502 Checkpoint – match…case Selection Statement
503 GenAI Prompt Exercises – match…case Selection Statement
504 while Statement
505 Checkpoint – while Statement
506 GenAI Prompt Exercises – while Statement
507 for Statement; Iterables, Lists and Iterators; Built-in range Function
508 Checkpoint – for Statement; Iterables, Lists and Iterators; Built-in range Function
509 GenAI Prompt Exercises – for Statement; Iterables, Lists and Iterators; Built-in range F
510 Augmented Assignments
511 Checkpoint – Augmented Assignments
512 GenAI Prompt Exercises – Augmented Assignments
513 Sequence-Controlled Iteration
514 Checkpoint – Sequence-Controlled Iteration
515 GenAI Prompt Exercises – Sequence-Controlled Iteration
516 Sentinel-Controlled Iteration
517 Checkpoint – Sentinel-Controlled Iteration
518 Built-In Function range – A Deeper Look
519 Checkpoint – Built-In Function range – A Deeper Look
520 GenAI Prompt Exercises – Built-In Function range – A Deeper Look
521 break and continue Statements
522 GenAI Prompt Exercises – break and continue Statements
523 Boolean Operators and, or and not
524 Checkpoint – Boolean Operators and, or and not
525 GenAI Prompt Exercise – Boolean Operators and, or and not
526 Using Type Decimal for Monetary Amounts
527 Using Type Decimal for Monetary Amounts – Compound-Interest Problem
528 Checkpoint – Using Type Decimal for Monetary Amounts
529 GenAI Prompt Exercises – Using Type Decimal for Monetary Amounts
530 Intro to Data Science – Measures of Central Tendency—Mean, Median and Mode
531 Checkpoint – Intro to Data Science – Measures of Central Tendency—Mean, Median and Mode
532 GenAI Prompt Exercises – – Intro to Data Science – Measures of Central Tendency—Mean, Me

[Updated in 2026] Lesson 04 – Functions
533 Lesson overview – Functions
534 Defining Functions
535 CheckPoint – Defining Functions
536 GenAI Prompt Exercises – Defining Functions
537 Functions with Multiple Parameters
538 CheckPoint – Functions with Multiple Parameters
539 GenAI Prompt Exercises – Functions with Multiple Parameters
540 Random-Number Generation – Rolling a Six-Sided Die
541 Random-Number Generation – Rolling a Six-Sided Die 6,000,000 Times
542 Random-Number Generation – Seeding the Random-Number Generator for Reproducibility
543 Checkpoint – Random-Number Generation
544 GenAI Prompt Exercises – Random-Number Generation
545 Case Study – A Game of Chance
546 Checkpoint – Case Study – A Game of Chance
547 GenAI Prompt Exercises – Case Study – A Game of Chance
548 Python Standard Library
549 GenAI Prompt Exercises – Python Standard Library
550 math Module Functions
551 GenAI Prompt Exercises – math Module Functions
552 Using IPython Tab Completion for Discovery
553 GenAI Prompt Exercises – Using IPython Tab Completion for Discovery
554 Default Parameter Values
555 GenAI Prompt Exercises – Default Parameter Values
556 Keyword Arguments
557 GenAI Prompt Exercises – Keyword Arguments
558 Arbitrary Argument Lists
559 Checkpoint – Arbitrary Argument Lists
560 GenAI Prompt Exercises – Arbitrary Argument Lists
561 Scope Rules
562 GenAI Prompt Exercises – Scope Rules
563 import – A Deeper Look
564 GenAI Prompt Exercises – import – A Deeper Look
565 Passing Arguments to Functions – A Deeper Look
566 GenAI Prompt Exercises – Passing Arguments to Functions – A Deeper Look
567 Intro to Functional Programming
568 Objects-Natural Case Study – Dates and Times
569 Checkpoint – Objects-Natural Case Study – Dates and Times
570 GenAI Prompt Exercises – Objects-Natural Case Study – Dates and Times
571 Intro to Data Science – Measures of Dispersion
572 GenAI Prompt Exercises – Intro to Data Science – Measures of Dispersion

[Updated in 2026] Lesson 05 – Sequences – Lists and Tuples
573 Lesson overview—Sequences – Lists and Tuples
574 Lists
575 GenAI Prompt Exercises – Lists
576 Tuples
577 Checkpoint – Tuples
578 GenAI Prompt Exercises – Tuples
579 Unpacking Sequences
580 Creating a primitive bar chart
581 Checkpoint – Unpacking Sequences
582 GenAI Prompt Exercises – Unpacking Sequences
583 Sequence Slicing
584 Checkpoint – Sequence Slicing
585 GenAI Prompt Exercises – Sequence Slicing
586 del Statement
587 Checkpoint – del Statement
588 GenAI Prompt Exercises – del Statement
589 Passing Lists to Functions
590 Checkpoint – Passing Lists to Functions
591 GenAI Prompt Exercises – Passing Lists to Functions
592 Sorting Lists
593 Checkpoint – Sorting Lists
594 GenAI Prompt Exercises – Sorting Lists
595 Searching Sequences
596 Checkpoint – Searching Sequences
597 GenAI Prompt Exercises – Searching Sequences
598 Other List Methods
599 Checkpoint – Other List Methods
600 Simulating Stacks with Lists
601 List Comprehensions
602 Checkpoint – List Comprehensions
603 GenAI Prompt Exercises – List Comprehensions
604 Generator Expressions
605 Generator Functions
606 Checkpoint – Generator Expressions
607 GenAI Prompt Exercises – Generator Expressions and Generator Functions
608 Reductions and an Intro to lambda Expressions
609 Checkpoint – Reductions and an Intro to lambda Expressions
610 GenAI Prompt Exercises – Reductions and an Intro to Expressions
611 Other Sequence Processing Functions
612 Checkpoint – Other Sequence Processing Functions
613 GenAI Prompt Exercises – Other Sequence Processing Functions
614 Two-Dimensional Lists
615 Checkpoint – Two-Dimensional Lists
616 GenAI Prompt Exercises – Two-Dimensional Lists
617 Pattern-Matching in match…case Statements
618 GenAI Prompt Exercise – Pattern-Matching in match…case Statements
619 Objects-Natural Intro to Data Science – Simulation and Static Visualizations
620 Sample Graphs
621 RollDie.py – Visualizing Die-Roll Frequencies and Percentages
622 Checkpoint – Simulation and Static Visualizations
623 GenAI Prompt Exercise – Simulation and Static Visualizations

[Updated in 2026] Lesson 06 – Dictionaries and Sets
624 Lesson overview – Dictionaries and Sets
625 Dictionaries
626 GenAI Prompt Exercises – Dictionaries
627 Creating a Dictionary
628 Checkpoint – Creating a Dictionary
629 GenAI Prompt Exercises – Creating a Dictionary
630 Iterating through a Key–Value Pairs
631 Checkpoint – Iterating through a Key–Value Pairs
632 Basic Dictionary Operarations
633 Checkpoint – Basic Dictionary Operarations
634 GenAI Prompt Exercises – Basic Dictionary Operarations
635 Dictionary Methods keys and values
636 Checkpoint – Dictionary Methods keys and values
637 GenAI Prompt Exercises – Dictionary Methods keys and values
638 Dictionary Comparisons
639 GenAI Prompt Exercise – Dictionary Comparisons
640 Application – Dictionary of Student Grades
641 GenAI Prompt Exercises – Dictionary of Student Grades
642 Application – Dictionary of Word Counts
643 Python Standard Library Module collections
644 Checkpoint – Python Standard Library Module collections
645 GenAI Prompt Exercises – Dictionary of Word Counts
646 Dictionary Method update and the Dictionary Union Operators
647 Arbitrary Keyword Arguments and Keyword-Only Parameters
648 GenAI Prompt Exercises – Arbitrary Keyword Arguments and Keyword-Only Parameters
649 Dictionary Comprehensions
650 Checkpoint – Dictionary Comprehensions
651 GenAI Prompt Exercises – Dictionary Comprehensions
652 Sets
653 Checkpoint – Sets
654 GenAI Prompt Exercises – Sets
655 Comparing Sets
656 Checkpoint – Comparing Sets
657 Mathematical Set Operations
658 Checkpoint – Mathematical Set Operations
659 GenAI Prompt Exercises – Mathematical Set Operations
660 Mutable Set Operators and Methods
661 GenAI Prompt Exercises – Mutable Set Operators and Methods
662 Set Comprehensions
663 GenAI Prompt Exercises – Set Comprehensions
664 Objects-Natural Intro to Data Science – Dynamic Visualizations—How Dynamic Visualizat
665 Objects-Natural Intro to Data Science – Dynamic Visualizations—Implementing Dynamic V
666 Objects-Natural Intro to Data Science – Dynamic Visualizations—functools Module’s par
667 GenAI Prompt Exercises – functools Module’s partial Function

[Updated in 2026] Lesson 07 – Arrays
668 Lesson overview – Arrays
669 Intro to Arrays
670 Creating arrays from Existing Data
671 Checkpoint – Creating arrays from Existing Data
672 GenAI Prompt Exercises – Creating arrays from Existing Data
673 array Attributes
674 Checkpoint – array Attributes
675 GenAI Prompt Exercises – array Attributes
676 Filling arrays with Specific Values
677 GenAI Prompt Exercises – Filling arrays with Specific Values
678 Creating arrays from Ranges
679 Checkpoint – Creating arrays from Ranges
680 GenAI Prompt Exercises – Creating arrays from Ranges
681 Profiling List vs. array Performance with timeit
682 Profiling List vs. array Performance with timeit ― Other IPython Magics
683 Checkpoint – Profiling List vs. array Performance with timeit
684 GenAI Prompt Exercises – Profiling List vs. array Performance with timeit
685 array Operators
686 Checkpoint – array Operators
687 GenAI Prompt Exercises – array Operators
688 NumPy Calculation Methods
689 Checkpoint – NumPy Calculation Methods
690 GenAI Prompt Exercises – NumPy Calculation Methods
691 Universal Functions
692 Checkpoint – Universal Functions
693 GenAI Prompt Exercises – Universal Functions
694 Indexing and Slicing
695 Checkpoint – Indexing and Slicing
696 GenAI Prompt Exercises – Indexing and Slicing
697 Views – Shallow Copies
698 GenAI Prompt Exercises – Views – Shallow Copies
699 Deep Copies
700 GenAI Prompt Exercises – Deep Copies
701 Reshaping and Transposing Arrays
702 Checkpoint – Reshaping and Transposing Arrays
703 GenAI Prompt Exercises – Reshaping and Transposing Arrays
704 Objects Natural Intro to Data Science – pandas Series and DataFrames
705 pandas Series
706 Checkpoint – pandas Series
707 GenAI Prompt Exercises – pandas Series
708 pandas DataFrames
709 Checkpoint – pandas DataFrames
710 GenAI Prompt Exercises – pandas DataFrames