Nanodegree key: nd025
Version: 5.0.0
Locale: enus
Get handson experience running data pipelines, designing experiments, building recommendation systems, and more.
Content
Part 01 : Welcome to the Nanodegree program

Module 01: Welcome to the Nanodegree program
Part 02 : Introduction to Data Science

Module 01: Introduction to Data Science

Lesson 01: The Data Science Process
In this lesson, you will learn about CRISPDM and how you can apply it to many data science problems.
 Concept 01: Video: Intro
 Concept 02: Video: CRISPDM
 Concept 03: Video: The Data Science Process  Business & Data
 Concept 04: Video: Business & Data Understanding  Example
 Concept 05: Screencast: Using Workspaces
 Concept 06: Quiz + Notebook: A Look at the Data
 Concept 07: Screencast: A Look at the Data
 Concept 08: What Should You Check?
 Concept 09: Video: Business & Data Understanding
 Concept 10: Video: Gathering & Wrangling
 Concept 11: Screencast: How To Break Into the Field?
 Concept 12: Notebook + Quiz: How To Break Into the Field
 Concept 13: Screencast: How to Break Into the Field Solution
 Concept 14: Screencast: Bootcamps
 Concept 15: Quiz: Bootcamp Takeaways
 Concept 16: Notebook + Quiz: Job Satisfaction
 Concept 17: Screencast: Job Satisfaction
 Concept 18: Video: It Is Not Always About ML
 Concept 19: Video: The Data Science Process  Modeling
 Concept 20: Video: Predicting Salary
 Concept 21: Screencast: Predicting Salary
 Concept 22: Notebook + Quiz: What Happened?
 Concept 23: Screencast: What Happened Solution
 Concept 24: Video: Working With Missing Values
 Concept 25: Video: Removing Data  Why Not?
 Concept 26: Video: Removing Data  When Is It OK?
 Concept 27: Video: Removing Data  Other Considerations
 Concept 28: Quiz: Removing Data
 Concept 29: Notebook + Quiz: Removing Values
 Concept 30: ScreenCast: Removing Data Solution
 Concept 31: Notebook + Quiz: Removing Data Part II
 Concept 32: Screencast: Removing Data Part II Solution
 Concept 33: Video: Imputing Missing Values
 Concept 34: Notebook + Quiz: Imputation Methods & Resources
 Concept 35: Screencast: Imputation Methods & Resources Solution
 Concept 36: Notebook + Quiz: Imputing Values
 Concept 37: Screencast: Imputing Values Solution
 Concept 38: Video: Working With Categorical Variables Refresher
 Concept 39: Notebook + Quiz: Categorical Variables
 Concept 40: Screencast: Categorical Variables Solution
 Concept 41: Video: How to Fix This?
 Concept 42: Notebook + Quiz: Putting It All Together
 Concept 43: Screencast + Notebook: Putting It All Together Solution
 Concept 44: Text + Quiz: Results
 Concept 45: Video: The Data Science Process  Evaluate & Deploy
 Concept 46: Text: Recap

Lesson 02: Communicating to Stakeholders
In this lesson, you will be creating a post to communicate your findings via Medium.
 Concept 01: Video: Introduction
 Concept 02: Video: First Things First
 Concept 03: Text: README Showcase
 Concept 04: Video: Posting to Github
 Concept 05: Quiz: Github Check
 Concept 06: Video: Up And Running On Medium
 Concept 07: Text: Medium Getting Started Post and Links
 Concept 08: Video: Know Your Audience
 Concept 09: Video: Three Steps to Captivate Your Audience
 Concept 10: Video: First Catch Their Eye
 Concept 11: Picture First, Title Second
 Concept 12: Video: More Advice
 Concept 13: More Advice
 Concept 14: Video: End With A Call To Action
 Concept 15: End With A Call To Action
 Concept 16: Video: Other Important Information
 Concept 17: Text: Recap
 Concept 18: Video: Conclusion

Lesson 04: Optimize Your GitHub Profile
Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
 Concept 01: Prove Your Skills With GitHub
 Concept 02: Introduction
 Concept 03: GitHub profile important items
 Concept 04: Good GitHub repository
 Concept 05: Interview with Art  Part 1
 Concept 06: Identify fixes for example “bad” profile
 Concept 07: Quick Fixes #1
 Concept 08: Quick Fixes #2
 Concept 09: Writing READMEs with Walter
 Concept 10: Interview with Art  Part 2
 Concept 11: Commit messages best practices
 Concept 12: Reflect on your commit messages
 Concept 13: Participating in open source projects
 Concept 14: Interview with Art  Part 3
 Concept 15: Participating in open source projects 2
 Concept 16: Starring interesting repositories
 Concept 17: Next Steps

Part 03 : Software Engineering
Software engineering skills are increasingly important for data scientists. In this course, you'll learn best practices for writing software. Then you'll work on your software skills by coding a Python package and a web data dashboard.

Module 01: Software Engineering

Lesson 01: Introduction to Software Engineering
Welcome to Software Engineering for Data Scientists! Learn about the course and meet your instructors.

Lesson 02: Software Engineering Practices Pt I
Learn software engineering practices and how they apply in data science. Part one covers clean and modular code, code efficiency, refactoring, documentation, and version control.
 Concept 01: Introduction
 Concept 02: Clean and Modular Code
 Concept 03: Refactoring Code
 Concept 04: Writing Clean Code
 Concept 05: Quiz: Clean Code
 Concept 06: Writing Modular Code
 Concept 07: Quiz: Refactoring  Wine Quality
 Concept 08: Solution: Refactoring  Wine Quality
 Concept 09: Efficient Code
 Concept 10: Optimizing  Common Books
 Concept 11: Quiz: Optimizing  Common Books
 Concept 12: Solution: Optimizing  Common Books
 Concept 13: Quiz: Optimizing  Holiday Gifts
 Concept 14: Solution: Optimizing  Holiday Gifts
 Concept 15: Documentation
 Concept 16: Inline Comments
 Concept 17: Docstrings
 Concept 18: Project Documentation
 Concept 19: Documentation
 Concept 20: Version Control in Data Science
 Concept 21: Scenario #1
 Concept 22: Scenario #2
 Concept 23: Scenario #3
 Concept 24: Model Versioning
 Concept 25: Conclusion

Lesson 03: Software Engineering Practices Pt II
Learn software engineering practices and how they apply in data science. Part two covers testing code, logging, and conducting code reviews.
 Concept 01: Introduction
 Concept 02: Testing
 Concept 03: Testing and Data Science
 Concept 04: Unit Tests
 Concept 05: Unit Testing Tools
 Concept 06: Quiz: Unit Tests
 Concept 07: Test Driven Development and Data Science
 Concept 08: Logging
 Concept 09: Log Messages
 Concept 10: Logging
 Concept 11: Code Review
 Concept 12: Questions to Ask Yourself When Conducting a Code Review
 Concept 13: Tips for Conducting a Code Review
 Concept 14: Conclusion

Lesson 04: Introduction to ObjectOriented Programming
Learn the basics of objectoriented programming so that you can build your own Python package.
 Concept 01: Introduction
 Concept 02: Procedural vs. ObjectOriented Programming
 Concept 03: Class, Object, Method and Attribute
 Concept 04: OOP Syntax
 Concept 05: Exercise: OOP Syntax Practice  Part 1
 Concept 06: A Couple of Notes about OOP
 Concept 07: Exercise: OOP Syntax Practice  Part 2
 Concept 08: Commenting ObjectOriented Code
 Concept 09: A Gaussian Class
 Concept 10: How the Gaussian Class Works
 Concept 11: Exercise: Code the Gaussian Class
 Concept 12: Magic Methods
 Concept 13: Exercise: Code Magic Methods
 Concept 14: Inheritance
 Concept 15: Exercise: Inheritance with Clothing
 Concept 16: Inheritance: Probability Distribution
 Concept 17: Demo: Inheritance Probability Distributions
 Concept 18: Advanced OOP Topics
 Concept 19: Organizing into Modules
 Concept 20: Demo: Modularized Code
 Concept 21: Making a Package
 Concept 22: Virtual Environments
 Concept 23: Exercise: Making a Package and Pip Installing
 Concept 24: Binomial Class
 Concept 25: Exercise: Binomial Class
 Concept 26: Scikitlearn Source Code
 Concept 27: Putting Code on PyPi
 Concept 28: Exercise: Upload to PyPi
 Concept 29: Lesson Summary

Lesson 05: Portfolio Exercise: Upload a Package to PyPi
Create your own Python package and upload your package to PyPi.

Lesson 06: Web Development
Develop a data dashboard using Flask, Bootstrap, Plotly and Pandas.
 Concept 01: Introduction
 Concept 02: Lesson Overview
 Concept 03: The Web
 Concept 04: Components of a Web App
 Concept 05: The FrontEnd
 Concept 06: HTML
 Concept 07: Exercise: HTML
 Concept 08: Div and Span
 Concept 09: IDs and Classes
 Concept 10: Exercise: HTML Div, Span, IDs, Classes
 Concept 11: CSS
 Concept 12: Exercise: CSS
 Concept 13: Bootstrap Library
 Concept 14: Exercise: Bootstrap
 Concept 15: JavaScript
 Concept 16: Exercise: JavaScript
 Concept 17: Plotly
 Concept 18: Exercise: Plotly
 Concept 19: The Backend
 Concept 20: Flask
 Concept 21: Exercise: Flask
 Concept 22: Flask + Pandas
 Concept 23: Example: Flask + Pandas
 Concept 24: Flask+Plotly+Pandas Part 1
 Concept 25: Flask+Plotly+Pandas Part 2
 Concept 26: Flask+Plotly+Pandas Part 3
 Concept 27: Flask+Plotly+Pandas Part 4
 Concept 28: Example: Flask + Plotly + Pandas
 Concept 29: Exercise: Flask + Plotly + Pandas
 Concept 30: Deployment
 Concept 31: Exercise: Deployment
 Concept 32: Lesson Summary

Lesson 07: Portfolio Exercise: Deploy a Data Dashboard
Customize the data dashboard from the previous lesson to make it your own. Upload the dashboard to the web.
 Concept 01: Introduction
 Concept 02: Workspace Portfolio Exercise
 Concept 03: Troubleshooting Possible Errors
 Concept 04: Congratulations
 Concept 05: APIs [advanced version]
 Concept 06: World Bank API [advanced version]
 Concept 07: Python and APIs [advanced version]
 Concept 08: World Bank Data Dashboard [advanced version]

Part 04 : Data Engineering
In data engineering for data scientists, you will practice building ETL, NLP, and machine learning pipelines. This will prepare you for the project with our industry partner Figure 8.

Module 01: Data Engineering

Lesson 01: Introduction to Data Engineering
You will get an introduction to the data engineering for data scientists course and project. The lessons include ETL pipelines, natural language pipelines, and machine learning pipelines.

Lesson 02: ETL Pipelines
ETL stands for extract, transform, and load. This is the most common type of data pipeline, and you will practice each step in this lesson.
 Concept 01: Introduction
 Concept 02: Lesson Overview
 Concept 03: World Bank Datasets
 Concept 04: How to Tackle the Exercises
 Concept 05: Extract
 Concept 06: Exercise: CSV
 Concept 07: Exercise: JSON and XML
 Concept 08: Exercise: SQL Databases
 Concept 09: Extracting Text Data
 Concept 10: Exercise: APIs
 Concept 11: Transform
 Concept 12: Combining Data
 Concept 13: Exercise: Combining Data
 Concept 14: Cleaning Data
 Concept 15: Exercise: Cleaning Data
 Concept 16: Exercise: Data Types
 Concept 17: Exercise: Parsing Dates
 Concept 18: Matching Encodings
 Concept 19: Exercise: Matching Encodings
 Concept 20: Missing Data  Overview
 Concept 21: Missing Data  Delete
 Concept 22: Missing Data  Impute
 Concept 23: Exercise: Imputation
 Concept 24: SQL, optimization, and ETL  Robert Chang Airbnb
 Concept 25: Duplicate Data
 Concept 26: Exercise: Duplicate Data
 Concept 27: Dummy Variables
 Concept 28: Exercise: Dummy Variables
 Concept 29: Outliers  How to Find Them
 Concept 30: Exercise: Outliers Part 1
 Concept 31: Outliers  What to do
 Concept 32: Exercise: Outliers  Part 2
 Concept 33: AI and Data Engineering  Robert Chang Airbnb
 Concept 34: Scaling Data
 Concept 35: Exercise: Scaling Data
 Concept 36: Feature Engineering
 Concept 37: Exercise: Feature Engineering
 Concept 38: Bloopers
 Concept 39: Load
 Concept 40: Exercise: Load
 Concept 41: Putting It All Together
 Concept 42: Exercise: Putting It All Together
 Concept 43: Lesson Summary

Lesson 03: NLP Pipelines
In order to complete the project at the end of the course, you will need some natural language processing skills. Here you will practice engineering machine learning features from text data.
 Concept 01: NLP and Pipelines
 Concept 02: How NLP Pipelines Work
 Concept 03: Text Processing
 Concept 04: Cleaning
 Concept 05: Notebook: Cleaning
 Concept 06: Normalization
 Concept 07: Notebook: Normalization
 Concept 08: Tokenization
 Concept 09: Notebook: Tokenization
 Concept 10: Stop Word Removal
 Concept 11: Notebook: Stop Words
 Concept 12: PartofSpeech Tagging
 Concept 13: Named Entity Recognition
 Concept 14: Notebook: POS and NER
 Concept 15: Stemming and Lemmatization
 Concept 16: Notebook: Stemming and Lemmatization
 Concept 17: Text Processing Summary
 Concept 18: Feature Extraction
 Concept 19: Bag of Words
 Concept 20: TFIDF
 Concept 21: Notebook: Bag of Words and TFIDF
 Concept 22: OneHot Encoding
 Concept 23: Word Embeddings
 Concept 24: Modeling
 Concept 25: [OPTIONAL] Word2Vec
 Concept 26: [OPTIONAL] GloVe
 Concept 27: [OPTIONAL] Embeddings for Deep Learning
 Concept 28: [OPTIONAL] tSNE

Lesson 04: Machine Learning Pipelines
You'll use the ScikitLearn package to code a machine learning pipeline. With these skills, you can ingest data, create features, and train a machine learning algorithm in just one step.
 Concept 01: Introduction
 Concept 02: Corporate Messaging Case Study
 Concept 03: Case Study: Clean and Tokenize
 Concept 04: Solution: Clean and Tokenize
 Concept 05: Machine Learning Workflow
 Concept 06: Case Study: Machine Learning Workflow
 Concept 07: Solution: Machine Learning Workflow
 Concept 08: Using Pipeline
 Concept 09: Advantages of Using Pipeline
 Concept 10: Case Study: Build Pipeline
 Concept 11: Solution: Build Pipeline
 Concept 12: Pipelines and Feature Unions
 Concept 13: Using Feature Union
 Concept 14: Case Study: Add Feature Union
 Concept 15: Solution: Add Feature Union
 Concept 16: Creating Custom Transformers
 Concept 17: Case Study: Create Custom Transformer
 Concept 18: Solution: Create Custom Transformer
 Concept 19: Pipelines and Grid Search
 Concept 20: Using Grid Search with Pipelines
 Concept 21: Case Study: Grid Search Pipeline
 Concept 22: Solution: Grid Search Pipeline
 Concept 23: Conclusion

Lesson 05: Project: Disaster Response Pipeline
You’ll build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

Lesson 06: Take 30 Min to Improve your LinkedIn
Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
 Concept 01: Get Opportunities with LinkedIn
 Concept 02: Use Your Story to Stand Out
 Concept 03: Why Use an Elevator Pitch
 Concept 04: Create Your Elevator Pitch
 Concept 05: Use Your Elevator Pitch on LinkedIn
 Concept 06: Create Your Profile With SEO In Mind
 Concept 07: Profile Essentials
 Concept 08: Work Experiences & Accomplishments
 Concept 09: Build and Strengthen Your Network
 Concept 10: Reaching Out on LinkedIn
 Concept 11: Boost Your Visibility
 Concept 12: Up Next

Part 05 : Experimental Design & Recommendations
Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.

Module 01: Experimental Design & Recommendations

Lesson 01: Intro to Experiment Design and Recommendation Engines
Why do we care about experiment design and recommendation engines? In this lesson, you'll get an overview of the topics you'll learn in this course.

Lesson 02: Concepts in Experiment Design
In this lesson, you will learn about conceptual topics that must be considered when designing and running an experiment, in order to ensure good, interpretable results.
 Concept 01: Lesson Introduction
 Concept 02: What is an Experiment?
 Concept 03: Types of Experiment
 Concept 04: Types of Sampling
 Concept 05: Measuring Outcomes
 Concept 06: Creating Metrics
 Concept 07: Controlling Variables
 Concept 08: Checking Validity
 Concept 09: Checking Bias
 Concept 10: Ethics in Experimentation
 Concept 11: A SMART Mnemonic for Experiment Design
 Concept 12: Lesson Conclusion

Lesson 03: Statistical Considerations in Testing
In this lesson, you will learn how statistics can be used to benefit the design of an experiment, as well as additional statistical tests that can be used to analyze results.
 Concept 01: Lesson Introduction
 Concept 02: Practice: Statistical Significance
 Concept 03: Statistical Significance  Solution
 Concept 04: Practical Significance
 Concept 05: Experiment Size
 Concept 06: Experiment Size  Solution
 Concept 07: Using Dummy Tests
 Concept 08: NonParametric Tests Part I
 Concept 09: NonParametric Tests Part I  Solution
 Concept 10: NonParametric Tests Part II
 Concept 11: NonParametric Tests Part II  Solution
 Concept 12: Analyzing Multiple Metrics
 Concept 13: Early Stopping
 Concept 14: Early Stopping  Solution
 Concept 15: Lesson Conclusion

Lesson 04: A/B Testing Case Study
In this lesson, you will go through an A/B Testing case study to see how the conceptual and statistical concepts covered in the previous lessons can be applied in experiment designs.
 Concept 01: Lesson Introduction
 Concept 02: Scenario Description
 Concept 03: Building a Funnel
 Concept 04: Building a Funnel  Discussion
 Concept 05: Deciding on Metrics  Part I
 Concept 06: Deciding on Metrics  Part II
 Concept 07: Deciding on Metrics  Discussion
 Concept 08: Experiment Sizing
 Concept 09: Experiment Sizing  Discussion
 Concept 10: Validity, Bias, and Ethics  Discussion
 Concept 11: Analyze Data
 Concept 12: Draw Conclusions
 Concept 13: Draw Conclusions  Discussion
 Concept 14: Lesson Conclusion

Lesson 05: Portfolio Exercise: Starbucks
In this lesson, you will analyze data that was originally used in screening interviews for data scientists at Starbucks.

Lesson 06: Introduction to Recommendation Engines
In this lesson, you will learn about the different methods used to create recommendation engines.
 Concept 01: Video: Intro
 Concept 02: Video + Text: Example Recommendation Engines
 Concept 03: Text: What's Ahead?
 Concept 04: Video: Introduction to MovieTweetings
 Concept 05: Notebook: MovieTweeting Data
 Concept 06: Screencast: Solution MovieTweeting Data
 Concept 07: Video: Ways to Recommend: Knowledge Based
 Concept 08: Notebook: Knowledge Based
 Concept 09: Screencast: Solution Knowledge Based
 Concept 10: Video: More Personalized Recommendations
 Concept 11: Video: Ways to Recommend: Collaborative Filtering
 Concept 12: Video + Quiz: Collaborative Filtering & Content Based Recs
 Concept 13: Video + Text: Measuring Similarity
 Concept 14: Notebook: Measuring Similarity
 Concept 15: Screencast: Solution Measuring Similarity
 Concept 16: Video: Identifying Recommendations
 Concept 17: Notebook: Collaborative Filtering
 Concept 18: Screencast: Solution Collaborative Filtering
 Concept 19: Screencast: Solutions for Collaborative Filtering
 Concept 20: Video: Ways to Recommend: Content Based
 Concept 21: Notebook: Content Based
 Concept 22: Screencast: Solution Content Based
 Concept 23: Video: Three Types of Recommendation Systems
 Concept 24: Text: More Recommendation Technniques
 Concept 25: Quiz: Recommendation Methods
 Concept 26: Video: Types of Ratings
 Concept 27: Video: Goals of Recommendation Systems
 Concept 28: Quiz: Types of Ratings & Goals of Recommendation Systems
 Concept 29: Video: Outro
 Concept 30: Text: Recap

Lesson 07: Matrix Factorization for Recommendations
In this lesson, you will learn how machine learning is being used to make recommendations.
 Concept 01: Video: Intro
 Concept 02: Text: What's Ahead
 Concept 03: Video: How Do We Know Our Recommendations Are Good?
 Concept 04: Text: Validating Your Recommendations
 Concept 05: Quiz: Regression Metrics
 Concept 06: Video: Why SVD?
 Concept 07: Video: Latent Factors
 Concept 08: Quiz: Latent Factors
 Concept 09: Video: Singular Value Decomposition
 Concept 10: Notebook: SVD Practice
 Concept 11: Screencast: SVD Practice Solution
 Concept 12: Video: SVD Practice Takeaways
 Concept 13: Text: SVD Closed Form Solution
 Concept 14: Video: FunkSVD
 Concept 15: Notebook: Implementing FunkSVD
 Concept 16: Screencast: Implementing FunkSVD
 Concept 17: Video: FunkSVD Review
 Concept 18: Notebook: How Are We Doing?
 Concept 19: Screencast: How Are We Doing?
 Concept 20: Video: The Cold Start Problem
 Concept 21: Notebook: The Cold Start Problem
 Concept 22: Screencast: The Cold Start Problem
 Concept 23: Video: Putting It All Together
 Concept 24: Screencast: Code Walkthrough
 Concept 25: Workspace: Recommender Module
 Concept 26: Video: Conclusion
 Concept 27: Text: Review

Lesson 08: Recommendation Engines
Put your skills to work to make recommendations for IBM Watson Studio's data platform.

Part 06 : Data Scientist Capstone
Leverage what you’ve learned throughout the program to build your own openended Data Science project. This project will serve as a demonstration of your valuable abilities as a Data Scientist.

Module 01: Data Scientist Capstone

Lesson 01: Data Scientist Capstone
Now you will put your Data Science skills to the test by solving a real world problem using all that you have learned throughout the program.
 Concept 01: Introduction
 Concept 02: Project Overview
 Concept 03: Software & Data Requirements
 Concept 04: Possible Projects
 Concept 05: Bertelsmann/Arvato Project Overview
 Concept 06: Arvato: Terms and Conditions
 Concept 07: Bertelsmann/Arvato Project Workspace
 Concept 08: Starbucks Project Overview
 Concept 09: Starbucks Project Workspace
 Concept 10: Dog Breed Classifier Overview
 Concept 11: Dog Breed Workspace
 Concept 12: Spark Project Overview
 Concept 13: Spark Project Workspace

Part 07 : Congratulations
Congratulations on your completion of the Data Scientist Nanodegree!

Module 01: Congratulations

Lesson 01: Congratulations!
Congratulations on your completion of the Data Scientist Nanodegree!

Part 08 (Elective): [Capstone Content] Convolutional Neural Networks

Module 01: [Capstone Content] Convolutional Neural Networks

Lesson 01: Neural Networks
Luis will give you an overview of logistic regression, gradient descent, and the building blocks of neural networks.
 Concept 01: Announcement
 Concept 02: Introduction
 Concept 03: Classification Problems 1
 Concept 04: Classification Problems 2
 Concept 05: Linear Boundaries
 Concept 06: Higher Dimensions
 Concept 07: Perceptrons
 Concept 08: Perceptrons as Logical Operators
 Concept 09: Why "Neural Networks"?
 Concept 10: Perceptron Trick
 Concept 11: Perceptron Algorithm
 Concept 12: NonLinear Regions
 Concept 13: Error Functions
 Concept 14: Logloss Error Function
 Concept 15: Discrete vs Continuous
 Concept 16: Softmax
 Concept 17: OneHot Encoding
 Concept 18: Maximum Likelihood
 Concept 19: Maximizing Probabilities
 Concept 20: CrossEntropy 1
 Concept 21: CrossEntropy 2
 Concept 22: MultiClass Cross Entropy
 Concept 23: Logistic Regression
 Concept 24: Gradient Descent
 Concept 25: Logistic Regression Algorithm
 Concept 26: PreLab: Gradient Descent
 Concept 27: Notebook: Gradient Descent
 Concept 28: Perceptron vs Gradient Descent
 Concept 29: Outro

Lesson 02: Deep Neural Networks
A deeper dive into backpropagation and the training process of neural networks, including techniques to improve the training.
 Concept 01: Nonlinear Data
 Concept 02: Continuous Perceptrons
 Concept 03: NonLinear Models
 Concept 04: Neural Network Architecture
 Concept 05: Feedforward
 Concept 06: Backpropagation
 Concept 07: Keras
 Concept 08: PreLab: Student Admissions in Keras
 Concept 09: Lab: Student Admissions in Keras
 Concept 10: Training Optimization
 Concept 11: Early Stopping
 Concept 12: Regularization
 Concept 13: Regularization 2
 Concept 14: Dropout
 Concept 15: Local Minima
 Concept 16: Vanishing Gradient
 Concept 17: Other Activation Functions
 Concept 18: Batch vs Stochastic Gradient Descent
 Concept 19: Learning Rate Decay
 Concept 20: Random Restart
 Concept 21: Momentum
 Concept 22: Optimizers in Keras
 Concept 23: Error Functions Around the World
 Concept 24: Neural Network Regression
 Concept 25: Neural Networks Playground
 Concept 26: Mini Project Intro
 Concept 27: PreLab: IMDB Data in Keras
 Concept 28: Lab: IMDB Data in Keras
 Concept 29: Outro

Lesson 03: Convolutional Neural Networks
Alexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.
 Concept 01: Introducing Alexis
 Concept 02: Applications of CNNs
 Concept 03: How Computers Interpret Images
 Concept 04: MLPs for Image Classification
 Concept 05: Categorical CrossEntropy
 Concept 06: Model Validation in Keras
 Concept 07: When do MLPs (not) work well?
 Concept 08: Mini project: Training an MLP on MNIST
 Concept 09: Local Connectivity
 Concept 10: Convolutional Layers (Part 1)
 Concept 11: Convolutional Layers (Part 2)
 Concept 12: Stride and Padding
 Concept 13: Convolutional Layers in Keras
 Concept 14: Quiz: Dimensionality
 Concept 15: Pooling Layers
 Concept 16: Max Pooling Layers in Keras
 Concept 17: CNNs for Image Classification
 Concept 18: CNNs in Keras: Practical Example
 Concept 19: Mini project: CNNs in Keras
 Concept 20: Image Augmentation in Keras
 Concept 21: Mini project: Image Augmentation in Keras
 Concept 22: Groundbreaking CNN Architectures
 Concept 23: Visualizing CNNs (Part 1)
 Concept 24: Visualizing CNNs (Part 2)
 Concept 25: Transfer Learning
 Concept 26: Transfer Learning in Keras

Part 09 (Elective): [Capstone Content] Spark

Module 01: [Capstone Content] Spark

Lesson 01: Introduction to the Course
In this lesson, you will learn more about this course  what will be covered, and who you will be learning from  let's get started!

Lesson 02: The Power of Spark
In this lesson, you will learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it.
 Concept 01: Introduction
 Concept 02: What is Big Data?
 Concept 03: Numbers Everyone Should Know
 Concept 04: Hardware: CPU
 Concept 05: Hardware: Memory
 Concept 06: Hardware: Storage
 Concept 07: Hardware: Network
 Concept 08: Hardware: Key Ratios
 Concept 09: Small Data Numbers
 Concept 10: Big Data Numbers
 Concept 11: Medium Data Numbers
 Concept 12: History of Distributed Computing
 Concept 13: The Hadoop Ecosystem
 Concept 14: MapReduce
 Concept 15: Hadoop MapReduce [Demo]
 Concept 16: The Spark Cluster
 Concept 17: Spark Use Cases
 Concept 18: Summary

Lesson 03: Data Wrangling with Spark
In this lesson, we'll dive into how to use Spark for cleaning and aggregating data.
 Concept 01: Introduction
 Concept 02: Functional Programming
 Concept 03: Why Use Functional Programming
 Concept 04: Procedural Example
 Concept 05: Procedural [Example Code]
 Concept 06: Pure Functions in the Bread Factory
 Concept 07: The Spark DAGs: Recipe for Data
 Concept 08: Maps and Lambda Functions
 Concept 09: Maps and Lambda Functions [Example Code]
 Concept 10: Data Formats
 Concept 11: Distributed Data Stores
 Concept 12: SparkSession
 Concept 13: Reading and Writing Data into Spark Data Frames
 Concept 14: Read and Write Data into Spark Data Frames [example code]
 Concept 15: Imperative vs Declarative programming
 Concept 16: Data Wrangling with DataFrames
 Concept 17: Data Wrangling with DataFrames Extra Tips
 Concept 18: Data Wrangling with Spark [Example Code]
 Concept 19: Quiz  Data Wrangling with DataFrames
 Concept 20: Quiz  Data Wrangling with DataFrames Jupyter Notebook
 Concept 21: Quiz [Solution Code]
 Concept 22: Spark SQL
 Concept 23: Example Spark SQL
 Concept 24: Example Spark SQL [Example Code]
 Concept 25: Quiz  Data Wrangling with SparkSQL
 Concept 26: Quiz [Spark SQL Solution Code]
 Concept 27: RDDs
 Concept 28: Summary

Lesson 04: Debugging and Optimization
In this lesson, we will cover various troubleshooting techniques and potential ways of optimizing the performance of your Spark applications.
 Concept 01: Introduction
 Concept 02: Setup Instructions AWS
 Concept 03: From Local to Standalone Mode
 Concept 04: Spark Scripts
 Concept 05: Submitting Spark Scripts
 Concept 06: Storing and Retrieving Data on the Cloud
 Concept 07: Reading and Writing to Amazon S3
 Concept 08: Introduction to HDFS
 Concept 09: Reading and Writing Data to HDFS
 Concept 10: Recap Local Mode to Cluster Mode
 Concept 11: Debugging is Hard
 Concept 12: Syntax Errors
 Concept 13: Code Errors
 Concept 14: Data Errors
 Concept 15: Demo: Data Errors
 Concept 16: Debugging your Code
 Concept 17: How to Use Accumulators
 Concept 18: Spark WebUI
 Concept 19: Connecting to the Spark Web UI
 Concept 20: Getting Familiar with the Spark UI
 Concept 21: Review of the Log Data
 Concept 22: Diagnosing Errors Part I
 Concept 23: Diagnosing Errors Part 2
 Concept 24: Diagnosing Errors Part 3
 Concept 25: Optimization Introduction
 Concept 26: Understanding Data Skew
 Concept 27: Understanding Big O Complexity
 Concept 28: Other Issues and How to Address Them
 Concept 29: Lesson Summary

Lesson 05: Machine Learning with Spark
In this lesson, we'll explore Spark's ML capabilities and build ML models and pipelines.
 Concept 01: Introduction
 Concept 02: Machine Learning in Spark
 Concept 03: Feature Extraction
 Concept 04: Numeric Features
 Concept 05: Numeric Features [Example Code]
 Concept 06: Text Processing
 Concept 07: Text Processing [Example Code]
 Concept 08: Quiz  Creating Features
 Concept 09: Quiz  Creating Features Jupyter Notebook
 Concept 10: Quiz [Solution Code]
 Concept 11: Dimensionality Reduction
 Concept 12: Supervised ML Algorithms
 Concept 13: Linear Regression
 Concept 14: Linear Regression
 Concept 15: Quiz  Linear Regression Jupyter Notebook
 Concept 16: Quiz [Solution Code]
 Concept 17: Logistic Regression
 Concept 18: Unsupervised ML Algorithms
 Concept 19: Quiz  Kmeans
 Concept 20: Quiz  Kmeans Jupyter Notebook
 Concept 21: Quiz [Solution Code]
 Concept 22: ML Pipelines
 Concept 23: ML Pipeline Example
 Concept 24: Model Selection and Tuning
 Concept 25: Model Selection and Tuning Example
 Concept 26: Quiz  Model Tuning
 Concept 27: Quiz  Model Tuning Jupyter Notebook
 Concept 28: Quiz [Solution Code]
 Concept 29: Summary

Lesson 06: [DSND Capstone] Cloud Deployment Instructions
 Concept 01: Overview
 Concept 02: [AWS] Launch EMR Cluster and Notebook
 Concept 03: [AWS] Starter Code
 Concept 04: Monitor your AWS Costs and Credits
 Concept 05: [IBM] Create an IBM Watson account and Watson project
 Concept 06: [IBM] Launch IBM Cluster and Notebook
 Concept 07: [IBM] Starter Code
 Concept 08: [IBM] Avoid Paying Unexpected Costs

Part 10 (Elective): Prerequisite: Python for Data Analysis

Module 01: Prerequisite: Python for Data Analysis

Lesson 01: Why Python Programming
Welcome to Introduction to Python! Here's an overview of the course.

Lesson 02: Data Types and Operators
Familiarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, builtin functions, and style guidelines.
 Concept 01: Introduction
 Concept 02: Arithmetic Operators
 Concept 03: Quiz: Arithmetic Operators
 Concept 04: Solution: Arithmetic Operators
 Concept 05: Variables and Assignment Operators
 Concept 06: Quiz: Variables and Assignment Operators
 Concept 07: Solution: Variables and Assignment Operators
 Concept 08: Integers and Floats
 Concept 09: Quiz: Integers and Floats
 Concept 10: Booleans, Comparison Operators, and Logical Operators
 Concept 11: Quiz: Booleans, Comparison Operators, and Logical Operators
 Concept 12: Solution: Booleans, Comparison and Logical Operators
 Concept 13: Strings
 Concept 14: Quiz: Strings
 Concept 15: Solution: Strings
 Concept 16: Type and Type Conversion
 Concept 17: Quiz: Type and Type Conversion
 Concept 18: Solution: Type and Type Conversion
 Concept 19: String Methods
 Concept 20: String Methods
 Concept 21: Another String Method  Split
 Concept 22: Lists and Membership Operators
 Concept 23: Quiz: Lists and Membership Operators
 Concept 24: Solution: List and Membership Operators
 Concept 25: List Methods
 Concept 26: Quiz: List Methods
 Concept 27: Tuples
 Concept 28: Quiz: Tuples
 Concept 29: Sets
 Concept 30: Quiz: Sets
 Concept 31: Dictionaries and Identity Operators
 Concept 32: Quiz: Dictionaries and Identity Operators
 Concept 33: Solution: Dictionaries and Identity Operators
 Concept 34: Quiz: More With Dictionaries
 Concept 35: Compound Data Structures
 Concept 36: Quiz: Compound Data Structures
 Concept 37: Solution: Compound Data Structions
 Concept 38: Conclusion
 Concept 39: Summary

Lesson 03: Control Flow
Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful builtin functions, and list comprehensions.
 Concept 01: Introduction
 Concept 02: Conditional Statements
 Concept 03: Practice: Conditional Statements
 Concept 04: Solution: Conditional Statements
 Concept 05: Quiz: Conditional Statements
 Concept 06: Solution: Conditional Statements
 Concept 07: Boolean Expressions for Conditions
 Concept 08: Quiz: Boolean Expressions for Conditions
 Concept 09: Solution: Boolean Expressions for Conditions
 Concept 10: For Loops
 Concept 11: Practice: For Loops
 Concept 12: Solution: For Loops Practice
 Concept 13: Quiz: For Loops
 Concept 14: Solution: For Loops Quiz
 Concept 15: Quiz: Match Inputs To Outputs
 Concept 16: Building Dictionaries
 Concept 17: Iterating Through Dictionaries with For Loops
 Concept 18: Quiz: Iterating Through Dictionaries
 Concept 19: Solution: Iterating Through Dictionaries
 Concept 20: While Loops
 Concept 21: Practice: While Loops
 Concept 22: Solution: While Loops Practice
 Concept 23: Quiz: While Loops
 Concept 24: Solution: While Loops Quiz
 Concept 25: Break, Continue
 Concept 26: Quiz: Break, Continue
 Concept 27: Solution: Break, Continue
 Concept 28: Zip and Enumerate
 Concept 29: Quiz: Zip and Enumerate
 Concept 30: Solution: Zip and Enumerate
 Concept 31: List Comprehensions
 Concept 32: Quiz: List Comprehensions
 Concept 33: Solution: List Comprehensions
 Concept 34: Conclusion

Lesson 04: Functions
Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.
 Concept 01: Introduction
 Concept 02: Defining Functions
 Concept 03: Quiz: Defining Functions
 Concept 04: Solution: Defining Functions
 Concept 05: Variable Scope
 Concept 06: Variable Scope
 Concept 07: Solution: Variable Scope
 Concept 08: Documentation
 Concept 09: Quiz: Documentation
 Concept 10: Solution: Documentation
 Concept 11: Lambda Expressions
 Concept 12: Quiz: Lambda Expressions
 Concept 13: Solution: Lambda Expressions
 Concept 14: [Optional] Iterators and Generators
 Concept 15: [Optional] Quiz: Iterators and Generators
 Concept 16: [Optional] Solution: Iterators and Generators
 Concept 17: [Optional] Generator Expressions
 Concept 18: Conclusion
 Concept 19: Further Learning

Lesson 05: Scripting
Setup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.
 Concept 01: Introduction
 Concept 02: Python Installation
 Concept 03: Install Python Using Anaconda
 Concept 04: [For Windows] Configuring Git Bash to Run Python
 Concept 05: Running a Python Script
 Concept 06: Programming Environment Setup
 Concept 07: Editing a Python Script
 Concept 08: Scripting with Raw Input
 Concept 09: Quiz: Scripting with Raw Input
 Concept 10: Solution: Scripting with Raw Input
 Concept 11: Errors and Exceptions
 Concept 12: Errors and Exceptions
 Concept 13: Handling Errors
 Concept 14: Practice: Handling Input Errors
 Concept 15: Solution: Handling Input Errors
 Concept 16: Accessing Error Messages
 Concept 17: Reading and Writing Files
 Concept 18: Quiz: Reading and Writing Files
 Concept 19: Solution: Reading and Writing Files
 Concept 20: Importing Local Scripts
 Concept 21: The Standard Library
 Concept 22: Quiz: The Standard Library
 Concept 23: Solution: The Standard Library
 Concept 24: Techniques for Importing Modules
 Concept 25: Quiz: Techniques for Importing Modules
 Concept 26: ThirdParty Libraries
 Concept 27: Experimenting with an Interpreter
 Concept 28: Online Resources
 Concept 29: Conclusion

Lesson 06: NumPy
Learn the basics of NumPy and how to use it to create and manipulate arrays.
 Concept 01: Instructors
 Concept 02: Introduction to NumPy
 Concept 03: Why Use NumPy?
 Concept 04: Creating and Saving NumPy ndarrays
 Concept 05: Using Builtin Functions to Create ndarrays
 Concept 06: Create an ndarray
 Concept 07: Accessing, Deleting, and Inserting Elements Into ndarrays
 Concept 08: Slicing ndarrays
 Concept 09: Boolean Indexing, Set Operations, and Sorting
 Concept 10: Manipulating ndarrays
 Concept 11: Arithmetic operations and Broadcasting
 Concept 12: Creating ndarrays with Broadcasting
 Concept 13: Glossary
 Concept 14: Getting Set Up for the MiniProject
 Concept 15: MiniProject: Mean Normalization and Data Separation

Lesson 07: Pandas
Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.
 Concept 01: Instructors
 Concept 02: Introduction to pandas
 Concept 03: Why Use pandas?
 Concept 04: Creating pandas Series
 Concept 05: Accessing and Deleting Elements in pandas Series
 Concept 06: Arithmetic Operations on pandas Series
 Concept 07: Manipulate a Series
 Concept 08: Creating pandas DataFrames
 Concept 09: Accessing Elements in pandas DataFrames
 Concept 10: Dealing with NaN
 Concept 11: Manipulate a DataFrame
 Concept 12: Loading Data into a pandas DataFrame
 Concept 13: Glossary
 Concept 14: Getting Set Up for the MiniProject
 Concept 15: MiniProject: Statistics From Stock Data

Part 11 (Elective): Prerequisite: SQL

Module 01: Prerequisite: SQL

Lesson 01: Basic SQL
In this section, you will gain knowledge about SQL basics for working with a single table. You will learn the key commands to filter a table in many different ways.
 Concept 01: Video: SQL Introduction
 Concept 02: Video: The Parch & Posey Database
 Concept 03: Video + Text: The Parch & Posey Database
 Concept 04: Quiz: ERD Fundamentals
 Concept 05: Text: Map of SQL Content
 Concept 06: Video: Why SQL
 Concept 07: Video: How Databases Store Data
 Concept 08: Text + Quiz: Types of Databases
 Concept 09: Video: Types of Statements
 Concept 10: Statements
 Concept 11: Video: SELECT & FROM
 Concept 12: Your First Queries in SQL Workspace
 Concept 13: Solution: Your First Queries
 Concept 14: Formatting Best Practices
 Concept 15: Video: LIMIT
 Concept 16: Quiz: LIMIT
 Concept 17: Solution: LIMIT
 Concept 18: Video: ORDER BY
 Concept 19: Quiz: ORDER BY
 Concept 20: Solutions: ORDER BY
 Concept 21: Video: ORDER BY Part II
 Concept 22: Quiz: ORDER BY Part II
 Concept 23: Solutions: ORDER BY Part II
 Concept 24: Video: WHERE
 Concept 25: Quiz: WHERE
 Concept 26: Solutions: WHERE
 Concept 27: Video: WHERE with NonNumeric Data
 Concept 28: Quiz: WHERE with NonNumeric
 Concept 29: Solutions: WHERE with NonNumeric
 Concept 30: Video: Arithmetic Operators
 Concept 31: Quiz: Arithmetic Operators
 Concept 32: Solutions: Arithmetic Operators
 Concept 33: Text: Introduction to Logical Operators
 Concept 34: Video: LIKE
 Concept 35: Quiz: LIKE
 Concept 36: Solutions: LIKE
 Concept 37: Video: IN
 Concept 38: Quiz: IN
 Concept 39: Solutions: IN
 Concept 40: Video: NOT
 Concept 41: Quiz: NOT
 Concept 42: Solutions: NOT
 Concept 43: Video: AND and BETWEEN
 Concept 44: Quiz: AND and BETWEEN
 Concept 45: Solutions: AND and BETWEEN
 Concept 46: Video: OR
 Concept 47: Quiz: OR
 Concept 48: Solutions: OR
 Concept 49: Text: Recap & Looking Ahead

Lesson 02: SQL Joins
In this lesson, you will learn how to combine data from multiple tables together.
 Concept 01: Video: Motivation
 Concept 02: Video: Why Would We Want to Split Data Into Separate Tables?
 Concept 03: Video: Introduction to JOINs
 Concept 04: Text + Quiz: Your First JOIN
 Concept 05: Solution: Your First JOIN
 Concept 06: Text: ERD Reminder
 Concept 07: Text: Primary and Foreign Keys
 Concept 08: Quiz: Primary  Foreign Key Relationship
 Concept 09: Text + Quiz: JOIN Revisited
 Concept 10: Video: Alias
 Concept 11: Quiz: JOIN Questions Part I
 Concept 12: Solutions: JOIN Questions Part I
 Concept 13: Video: Motivation for Other JOINs
 Concept 14: Video: LEFT and RIGHT JOINs
 Concept 15: Text: Other JOIN Notes
 Concept 16: LEFT and RIGHT JOIN
 Concept 17: Solutions: LEFT and RIGHT JOIN
 Concept 18: Video: JOINs and Filtering
 Concept 19: Quiz: Last Check
 Concept 20: Solutions: Last Check
 Concept 21: Text: Recap & Looking Ahead

Lesson 03: SQL Aggregations
In this lesson, you will learn how to aggregate data using SQL functions like SUM, AVG, and COUNT. Additionally, CASE, HAVING, and DATE functions provide you an incredible problem solving toolkit.
 Concept 01: Video: Introduction to Aggregation
 Concept 02: Video: Introduction to NULLs
 Concept 03: Video: NULLs and Aggregation
 Concept 04: Video + Text: First Aggregation  COUNT
 Concept 05: Video: COUNT & NULLs
 Concept 06: Video: SUM
 Concept 07: Quiz: SUM
 Concept 08: Solution: SUM
 Concept 09: Video: MIN & MAX
 Concept 10: Video: AVG
 Concept 11: Quiz: MIN, MAX, & AVG
 Concept 12: Solutions: MIN, MAX, & AVG
 Concept 13: Video: GROUP BY
 Concept 14: Quiz: GROUP BY
 Concept 15: Solutions: GROUP BY
 Concept 16: Video: GROUP BY Part II
 Concept 17: Quiz: GROUP BY Part II
 Concept 18: Solutions: GROUP BY Part II
 Concept 19: Video: DISTINCT
 Concept 20: Quiz: DISTINCT
 Concept 21: Solutions: DISTINCT
 Concept 22: Video: HAVING
 Concept 23: HAVING
 Concept 24: Solutions: HAVING
 Concept 25: Video: DATE Functions
 Concept 26: Video: DATE Functions II
 Concept 27: Quiz: DATE Functions
 Concept 28: Solutions: DATE Functions
 Concept 29: Video: CASE Statements
 Concept 30: Video: CASE & Aggregations
 Concept 31: Quiz: CASE
 Concept 32: Solutions: CASE
 Concept 33: Text: Recap

Lesson 04: SQL Subqueries & Temporary Tables
In this lesson, you will be learning to answer much more complex business questions using nested querying methods  also known as subqueries.
 Concept 01: Video: Introduction
 Concept 02: Video: Introduction to Subqueries
 Concept 03: Video + Quiz: Write Your First Subquery
 Concept 04: Solutions: Write Your First Subquery
 Concept 05: Text: Subquery Formatting
 Concept 06: Video: More On Subqueries
 Concept 07: Quiz: More On Subqueries
 Concept 08: Solutions: More On Subqueries
 Concept 09: Quiz: Subquery Mania
 Concept 10: Solution: Subquery Mania
 Concept 11: Video: WITH
 Concept 12: Text + Quiz: WITH vs. Subquery
 Concept 13: Quiz: WITH
 Concept 14: Solutions: WITH
 Concept 15: Video: Subquery Conclusion

Lesson 05: SQL Data Cleaning
Cleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.
 Concept 01: Video: Introduction to SQL Data Cleaning
 Concept 02: Video: LEFT & RIGHT
 Concept 03: Quiz: LEFT & RIGHT
 Concept 04: Solutions: LEFT & RIGHT
 Concept 05: Video: POSITION, STRPOS, & SUBSTR
 Concept 06: Quiz: POSITION, STRPOS, & SUBSTR  AME DATA AS QUIZ 1
 Concept 07: Solutions: POSITION, STRPOS, & SUBSTR
 Concept 08: Video: CONCAT
 Concept 09: Quiz: CONCAT
 Concept 10: Solutions: CONCAT
 Concept 11: Video: CAST
 Concept 12: Quiz: CAST
 Concept 13: Solutions: CAST
 Concept 14: Video: COALESCE
 Concept 15: Quiz: COALESCE
 Concept 16: Solutions: COALESCE
 Concept 17: Video + Text: Recap

Lesson 06: [Advanced] SQL Window Functions
Compare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.
 Concept 01: Video: Introduction to Window Functions
 Concept 02: Video: Window Functions 1
 Concept 03: Quiz: Window Functions 1
 Concept 04: Solutions: Window Functions 1
 Concept 05: Quiz: Window Functions 2
 Concept 06: Solutions: Window Functions 2
 Concept 07: Video: ROW_NUMBER & RANK
 Concept 08: Quiz: ROW_NUMBER & RANK
 Concept 09: Solutions: ROW_NUMBER & RANK
 Concept 10: Video: Aggregates in Window Functions
 Concept 11: Quiz: Aggregates in Window Functions
 Concept 12: Solutions: Aggregates in Window Functions
 Concept 13: Video: Aliases for Multiple Window Functions
 Concept 14: Quiz: Aliases for Multiple Window Functions
 Concept 15: Solutions: Aliases for Multiple Window Functions
 Concept 16: Video: Comparing a Row to Previous Row
 Concept 17: Quiz: Comparing a Row to Previous Row
 Concept 18: Solutions: Comparing a Row to Previous Row
 Concept 19: Video: Introduction to Percentiles
 Concept 20: Video: Percentiles
 Concept 21: Quiz: Percentiles
 Concept 22: Solutions: Percentiles
 Concept 23: Video: Recap

Lesson 07: [Advanced] SQL Advanced JOINs & Performance Tuning
Learn advanced joins and how to make queries that run quickly across giant datasets. Most of the examples in the lesson involve edge cases, some of which come up in interviews.
 Concept 01: Video: Introduction to Advanced SQL
 Concept 02: Text + Images: FULL OUTER JOIN
 Concept 03: Quiz: FULL OUTER JOIN
 Concept 04: Solutions: FULL OUTER JOIN
 Concept 05: Video: JOINs with Comparison Operators
 Concept 06: Quiz: JOINs with Comparison Operators
 Concept 07: Solutions: JOINs with Comparison Operators
 Concept 08: Video: Self JOINs
 Concept 09: Quiz: Self JOINs
 Concept 10: Solutions: Self JOINs
 Concept 11: Video: UNION
 Concept 12: Quiz: UNION
 Concept 13: Solutions: UNION
 Concept 14: Video: Performance Tuning Motivation
 Concept 15: Video + Quiz: Performance Tuning 1
 Concept 16: Video: Performance Tuning 2
 Concept 17: Video: Performance Tuning 3
 Concept 18: Video: JOINing Subqueries
 Concept 19: Video: SQL Completion Congratulations

Part 12 (Elective): Prerequisite: Data Visualization

Module 01: Prerequisite: Data Visualization

Lesson 01: Data Visualization in Data Analysis
In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.
 Concept 01: Introduction to Data Visualization
 Concept 02: Motivation for Data Visualization
 Concept 03: Further Motivation
 Concept 04: Exploratory vs. Explanatory Analyses
 Concept 05: Quiz: Exploratory vs. Explanatory
 Concept 06: Visualization in Python
 Concept 07: Course Structure
 Concept 08: Lesson Summary

Lesson 02: Design of Visualizations
Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.
 Concept 01: Introduction
 Concept 02: What Makes a Bad Visual?
 Concept 03: Levels of Measurement & Types of Data
 Concept 04: Quiz: Data Types (Quantitative vs. Categorical)
 Concept 05: Text + Quiz: Data Types (Ordinal vs. Nominal)
 Concept 06: Data Types (Continuous vs. Discrete)
 Concept 07: Identifying Data Types
 Concept 08: What Experts Say About Visual Encodings
 Concept 09: Chart Junk
 Concept 10: Data Ink Ratio
 Concept 11: Design Integrity
 Concept 12: Bad Visual Quizzes (Part I)
 Concept 13: Bad Visual Quizzes (Part II)
 Concept 14: Using Color
 Concept 15: Designing for Color Blindness
 Concept 16: Shape, Size, & Other Tools
 Concept 17: Good Visual
 Concept 18: Lesson Summary

Lesson 03: Univariate Exploration of Data
In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.
 Concept 01: Introduction
 Concept 02: Tidy Data
 Concept 03: Bar Charts
 Concept 04: Absolute vs. Relative Frequency
 Concept 05: Counting Missing Data
 Concept 06: Bar Chart Practice
 Concept 07: Pie Charts
 Concept 08: Histograms
 Concept 09: Histogram Practice
 Concept 10: Figures, Axes, and Subplots
 Concept 11: Choosing a Plot for Discrete Data
 Concept 12: Descriptive Statistics, Outliers and Axis Limits
 Concept 13: Scales and Transformations
 Concept 14: Scales and Transformations Practice
 Concept 15: Lesson Summary
 Concept 16: Extra: Kernel Density Estimation
 Concept 17: Extra: Waffle Plots

Lesson 04: Bivariate Exploration of Data
In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.
 Concept 01: Introduction
 Concept 02: Scatterplots and Correlation
 Concept 03: Overplotting, Transparency, and Jitter
 Concept 04: Heat Maps
 Concept 05: Scatterplot Practice
 Concept 06: Violin Plots
 Concept 07: Box Plots
 Concept 08: Violin and Box Plot Practice
 Concept 09: Clustered Bar Charts
 Concept 10: Categorical Plot Practice
 Concept 11: Faceting
 Concept 12: Adaptation of Univariate Plots
 Concept 13: Line Plots
 Concept 14: Additional Plot Practice
 Concept 15: Lesson Summary
 Concept 16: Extra: QQ Plots
 Concept 17: Extra: Swarm Plots
 Concept 18: Extra: Rug and Strip Plots
 Concept 19: Extra: Stacked Plots
 Concept 20: Extra: Ridgeline Plots

Lesson 05: Multivariate Exploration of Data
In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.
 Concept 01: Introduction
 Concept 02: NonPositional Encodings for Third Variables
 Concept 03: Color Palettes
 Concept 04: Encodings Practice
 Concept 05: Faceting in Two Directions
 Concept 06: Other Adaptations of Bivariate Plots
 Concept 07: Adapted Plot Practice
 Concept 08: Plot Matrices
 Concept 09: Feature Engineering
 Concept 10: How Much is Too Much?
 Concept 11: Additional Plot Practice
 Concept 12: Lesson Summary

Lesson 06: Explanatory Visualizations
Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!
 Concept 01: Introduction
 Concept 02: Revisiting the Data Analysis Process
 Concept 03: Tell A Story
 Concept 04: Same Data, Different Stories
 Concept 05: Quizzes on Data Story Telling
 Concept 06: Polishing Plots
 Concept 07: Polishing Plots Practice
 Concept 08: Creating a Slide Deck with Jupyter
 Concept 09: Getting and Using Feedback
 Concept 10: Lesson Summary

Lesson 07: Visualization Case Study
Put to practice the concepts you've learned about exploratory and explanatory data visualization in this case study on factors that impact diamond prices.

Part 13 (Elective): Prerequisite: Command Line Essentials

Module 01: Prerequisite: Command Line Essentials

Lesson 01: Shell Workshop
The Unix shell is a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer.
 Concept 01: The Command Line
 Concept 02: Intro to the Shell
 Concept 03: Windows: Installing Git Bash
 Concept 04: Opening a terminal
 Concept 05: Your first command (echo)
 Concept 06: Navigating directories (ls, cd, ..)
 Concept 07: Current working directory (pwd)
 Concept 08: Parameters and options (ls l)
 Concept 09: Organizing your files (mkdir, mv)
 Concept 10: Downloading (curl)
 Concept 11: Viewing files (cat, less)
 Concept 12: Removing things (rm, rmdir)
 Concept 13: Searching and pipes (grep, wc)
 Concept 14: Shell and environment variables
 Concept 15: Startup files (.bash_profile)
 Concept 16: Controlling the shell prompt ($PS1)
 Concept 17: Aliases
 Concept 18: Keep learning!

Part 14 (Elective): Prerequisite: Git & Github

Module 01: Prerequisite: Git & Github

Lesson 01: What is Version Control?
Version control is an incredibly important part of a professional programmer's life. In this lesson, you'll learn about the benefits of version control and install the version control tool Git!

Lesson 02: Create A Git Repo
Now that you've learned the benefits of Version Control and gotten Git installed, it's time you learn how to create a repository.

Lesson 03: Review a Repo's History
Knowing how to review an existing Git repository's history of commits is extremely important. You'll learn how to do just that in this lesson.

Lesson 04: Add Commits To A Repo
A repository is nothing without commits. In this lesson, you'll learn how to make commits, write descriptive commit messages, and verify the changes you're about to save to the repository.

Lesson 05: Tagging, Branching, and Merging
Being able to work on your project in isolation from other changes will multiply your productivity. You'll learn how to do this isolated development with Git's branches.

Lesson 06: Undoing Changes
Help! Disaster has struck! You don't have to worry, though, because your project is tracked in version control! You'll learn how to undo and modify changes that have been saved to the repository.

Lesson 07: Working With Remotes
You'll learn how to create remote repositories on GitHub and how to get and send changes to the remote repository.

Lesson 08: Working On Another Developer's Repository
In this lesson, you'll learn how to fork another developer's project. Collaborating with other developers can be a tricky process, so you'll learn how to contribute to a public project.

Lesson 09: Staying In Sync With A Remote Repository
You'll learn how to send suggested changes to another developer by using pull requests. You'll also learn how to use the powerful
git rebase
command to squash commits together.

Part 15 (Elective): Prerequisite: Linear Algebra

Module 01: Prerequisite: Linear Algebra

Lesson 01: Introduction
Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Lesson 02: Vectors
Learn about vectors, the basic building block of Linear Algebra.
 Concept 01: What's a Vector?
 Concept 02: Vectors, what even are they? Part 2
 Concept 03: Vectors, what even are they? Part 3
 Concept 04: Vectors Mathematical definition
 Concept 05: Transpose
 Concept 06: Magnitude and Direction
 Concept 07: Vectors Quiz 1
 Concept 08: Operations in the Field
 Concept 09: Vector Addition
 Concept 10: Vectors Quiz 2
 Concept 11: Scalar by Vector Multiplication
 Concept 12: Vectors Quiz 3
 Concept 13: Vectors Quiz Answers

Lesson 03: Linear Combination
Learn how to scale and add vectors and how to visualize the process.
 Concept 01: Linear Combination. Part 1
 Concept 02: Linear Combination. Part 2
 Concept 03: Linear Combination and Span
 Concept 04: Linear Combination Quiz 1
 Concept 05: Linear Dependency
 Concept 06: Solving a Simplified Set of Equations
 Concept 07: Linear Combination  Quiz 2
 Concept 08: Linear Combination  Quiz 3

Lesson 04: Linear Transformation and Matrices
What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.
 Concept 01: What is a Matrix?
 Concept 02: Matrix Addition
 Concept 03: Matrix Addition Quiz
 Concept 04: Scalar Multiplication of Matrix and Quiz
 Concept 05: Multiplication of Square Matrices
 Concept 06: Square Matrix Multiplication Quiz
 Concept 07: Matrix Multiplication  General
 Concept 08: Matrix Multiplication Quiz
 Concept 09: Linear Transformation and Matrices . Part 1
 Concept 10: Linear Transformation and Matrices. Part 2
 Concept 11: Linear Transformation and Matrices. Part 3
 Concept 12: Linear Transformation Quiz Answers

Part 16 (Elective): Prerequisite: Practical Statistics

Module 01: Prerequisite: Practical Statistics

Lesson 01: Descriptive Statistics  Part I
In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
 Concept 01: Introduce Instructors
 Concept 02: Text: Optional Lessons Note
 Concept 03: Video: Welcome!
 Concept 04: Video: What is Data? Why is it important?
 Concept 05: Video: Data Types (Quantitative vs. Categorical)
 Concept 06: Quiz: Data Types (Quantitative vs. Categorical)
 Concept 07: Video: Data Types (Ordinal vs. Nominal)
 Concept 08: Video: Data Types (Continuous vs. Discrete)
 Concept 09: Video: Data Types Summary
 Concept 10: Text + Quiz: Data Types (Ordinal vs. Nominal)
 Concept 11: Data Types (Continuous vs. Discrete)
 Concept 12: Video: Introduction to Summary Statistics
 Concept 13: Video: Measures of Center (Mean)
 Concept 14: Measures of Center (Mean)
 Concept 15: Video: Measures of Center (Median)
 Concept 16: Measures of Center (Median)
 Concept 17: Video: Measures of Center (Mode)
 Concept 18: Measures of Center (Mode)
 Concept 19: Video: What is Notation?
 Concept 20: Video: Random Variables
 Concept 21: Quiz: Variable Types
 Concept 22: Video: Capital vs. Lower
 Concept 23: Quiz: Introduction to Notation
 Concept 24: Video: Better Way?
 Concept 25: Video: Summation
 Concept 26: Video: Notation for the Mean
 Concept 27: Quiz: Summation
 Concept 28: Quiz: Notation for the Mean
 Concept 29: Text: Summary on Notation

Lesson 02: Descriptive Statistics  Part II
In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
 Concept 01: Video: What are Measures of Spread?
 Concept 02: Video: Histograms
 Concept 03: Video: Weekdays vs. Weekends: What is the Difference
 Concept 04: Video: Introduction to Five Number Summary
 Concept 05: Quiz: 5 Number Summary Practice
 Concept 06: Video: What if We Only Want One Number?
 Concept 07: Video: Introduction to Standard Deviation and Variance
 Concept 08: Video: Standard Deviation Calculation
 Concept 09: Measures of Spread (Calculation and Units)
 Concept 10: Text: Introduction to the Standard Deviation and Variance
 Concept 11: Video: Why the Standard Deviation?
 Concept 12: Video: Important Final Points
 Concept 13: Advanced: Standard Deviation and Variance
 Concept 14: Quiz: Applied Standard Deviation and Variance
 Concept 15: Homework 1: Final Quiz on Measures Spread
 Concept 16: Text: Measures of Center and Spread Summary
 Concept 17: Video: Shape
 Concept 18: Video: The Shape For Data In The World
 Concept 19: Quiz: Shape and Outliers (What's the Impact?)
 Concept 20: Video: Shape and Outliers
 Concept 21: Video: Working With Outliers
 Concept 22: Video: Working With Outliers My Advice
 Concept 23: Quiz: Shape and Outliers (Comparing Distributions)
 Concept 24: Quiz: Shape and Outliers (Visuals)
 Concept 25: Quiz: Shape and Outliers (Final Quiz)
 Concept 26: Text: Descriptive Statistics Summary
 Concept 27: Video: Descriptive vs. Inferential Statistics
 Concept 28: Quiz: Descriptive vs. Inferential (Udacity Students)
 Concept 29: Quiz: Descriptive vs. Inferential (Bagels)
 Concept 30: Text: Descriptive vs. Inferential Summary
 Concept 31: Video: Summary

Lesson 03: Admissions Case Study
Learn to ask the right questions, as you learn about Simpson's Paradox.
 Concept 01: Admissions Case Study Introduction
 Concept 02: Admissions 1
 Concept 03: Admissions 2
 Concept 04: Admissions 3
 Concept 05: Admissions 4
 Concept 06: Gender Bias
 Concept 07: Aggregation
 Concept 08: Aggregation 2
 Concept 09: Aggregation 3
 Concept 10: Gender Bias Revisited
 Concept 11: Dangers of Statistics
 Concept 12: Text: Recap + Next Steps
 Concept 13: Case Study in Python
 Concept 14: Conclusion

Lesson 04: Probability
Gain the basics of probability using coins and die.
 Concept 01: Introduction to Probability
 Concept 02: Flipping Coins
 Concept 03: Fair Coin
 Concept 04: Loaded Coin 1
 Concept 05: Loaded Coin 2
 Concept 06: Loaded Coin 3
 Concept 07: Complementary Outcomes
 Concept 08: Two Flips 1
 Concept 09: Two Flips 2
 Concept 10: Two Flips 3
 Concept 11: Two Flips 4
 Concept 12: Two Flips 5
 Concept 13: One Head 1
 Concept 14: One Head 2
 Concept 15: One Of Three 1
 Concept 16: One Of Three 2
 Concept 17: Even Roll
 Concept 18: Doubles
 Concept 19: Probability Conclusion
 Concept 20: Text: Recap + Next Steps

Lesson 05: Binomial Distribution
Learn about one of the most popular distributions in probability  the Binomial Distribution.
 Concept 01: Binomial
 Concept 02: Heads Tails
 Concept 03: Heads Tails 2
 Concept 04: 5 Flips 1 Head
 Concept 05: 5 Flips 2 Heads
 Concept 06: 5 Flips 3 Heads
 Concept 07: 10 Flips 5 Heads
 Concept 08: Formula
 Concept 09: Arrangements
 Concept 10: Binomial 1
 Concept 11: Binomial 2
 Concept 12: Binomial 3
 Concept 13: Binomial 4
 Concept 14: Binomial 5
 Concept 15: Binomial 6
 Concept 16: Binomial Conclusion
 Concept 17: Text: Recap + Next Steps

Lesson 06: Conditional Probability
Not all events are independent. Learn the probability rules for dependent events.
 Concept 01: Introduction to Conditional Probability
 Concept 02: Medical Example 1
 Concept 03: Medical Example 2
 Concept 04: Medical Example 3
 Concept 05: Medical Example 4
 Concept 06: Medical Example 5
 Concept 07: Medical Example 6
 Concept 08: Medical Example 7
 Concept 09: Medical Example 8
 Concept 10: Total Probability
 Concept 11: Two Coins 1
 Concept 12: Two Coins 2
 Concept 13: Two Coins 3
 Concept 14: Two Coins 4
 Concept 15: Summary
 Concept 16: Text: Summary

Lesson 07: Bayes Rule
Learn one of the most popular rules in all of statistics  Bayes rule.
 Concept 01: Bayes Rule
 Concept 02: Cancer Test
 Concept 03: Prior And Posterior
 Concept 04: Normalizing 1
 Concept 05: Normalizing 2
 Concept 06: Normalizing 3
 Concept 07: Total Probability
 Concept 08: Bayes Rule Diagram
 Concept 09: Equivalent Diagram
 Concept 10: Cancer Probabilities
 Concept 11: Probability Given Test
 Concept 12: Normalizer
 Concept 13: Normalizing Probability
 Concept 14: Disease Test 1
 Concept 15: Disease Test 2
 Concept 16: Disease Test 3
 Concept 17: Disease Test 4
 Concept 18: Disease Test 5
 Concept 19: Disease Test 6
 Concept 20: Bayes Rule Summary
 Concept 21: Robot Sensing 1
 Concept 22: Robot Sensing 2
 Concept 23: Robot Sensing 3
 Concept 24: Robot Sensing 4
 Concept 25: Robot Sensing 5
 Concept 26: Robot Sensing 6
 Concept 27: Robot Sensing 7
 Concept 28: Robot Sensing 8
 Concept 29: Generalizing
 Concept 30: Sebastian At Home
 Concept 31: Learning Objectives  Conditional Probability
 Concept 32: Reducing Uncertainty
 Concept 33: Bayes' Rule and Robotics
 Concept 34: Learning from Sensor Data
 Concept 35: Using Sensor Data
 Concept 36: Learning Objectives  Bayes' Rule
 Concept 37: Bayes Rule Conclusion

Lesson 08: Python Probability Practice
Take what you have learned in the last lessons and put it to practice in Python.

Lesson 09: Normal Distribution Theory
Learn the mathematics behind moving from a coin flip to a normal distribution.
 Concept 01: Maximum Probability
 Concept 02: Shape
 Concept 03: Better Formula
 Concept 04: Quadratics
 Concept 05: Quadratics 2
 Concept 06: Quadratics 3
 Concept 07: Quadratics 4
 Concept 08: Maximum
 Concept 09: Maximum Value
 Concept 10: Minimum
 Concept 11: Minimum Value
 Concept 12: Normalizer
 Concept 13: Formula Summary
 Concept 14: Central Limit Theorem
 Concept 15: Summary

Lesson 10: Sampling distributions and the Central Limit Theorem
Learn all about the underpinning of confidence intervals and hypothesis testing  sampling distributions.
 Concept 01: Introduction
 Concept 02: Video: Descriptive vs. Inferential Statistics
 Concept 03: Quiz: Descriptive vs. Inferential (Udacity Students)
 Concept 04: Quiz: Descriptive vs. Inferential (Bagels)
 Concept 05: Text: Descriptive vs. Inferential Statistics
 Concept 06: Video + Quiz: Introduction to Sampling Distributions Part I
 Concept 07: Video + Quiz: Introduction to Sampling Distributions Part II
 Concept 08: Video: Introduction to Sampling Distributions Part III
 Concept 09: Notebook + Quiz: Sampling Distributions & Python
 Concept 10: Text: Sampling Distribution Notes
 Concept 11: Video: Introduction to Notation
 Concept 12: Video: Notation for Parameters vs. Statistics
 Concept 13: Quiz: Notation
 Concept 14: Video: Other Sampling Distributions
 Concept 15: Video: Two Useful Theorems  Law of Large Numbers
 Concept 16: Notebook + Quiz: Law of Large Numbers
 Concept 17: Video: Two Useful Theorems  Central Limit Theorem
 Concept 18: Notebook + Quiz: Central Limit Theorem
 Concept 19: Notebook + Quiz: Central Limit Theorem  Part II
 Concept 20: Video: When Does the Central Limit Theorem Not Work?
 Concept 21: Notebook + Quiz: Central Limit Theorem  Part III
 Concept 22: Video: Bootstrapping
 Concept 23: Video: Bootstrapping & The Central Limit Theorem
 Concept 24: Notebook + Quiz: Bootstrapping
 Concept 25: Video: The Background of Bootstrapping
 Concept 26: Video: Why are Sampling Distributions Important
 Concept 27: Quiz + Text: Recap & Next Steps

Lesson 11: Confidence Intervals
Learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.
 Concept 01: Video: Introduction
 Concept 02: Video: From Sampling Distributions to Confidence Intervals
 Concept 03: ScreenCast: Sampling Distributions and Confidence Intervals
 Concept 04: Notebook + Quiz: Building Confidence Intervals
 Concept 05: ScreenCast: Difference In Means
 Concept 06: Notebook + Quiz: Difference in Means
 Concept 07: Video: Confidence Interval Applications
 Concept 08: Video: Statistical vs. Practical Significance
 Concept 09: Statistical vs. Practical Significance
 Concept 10: Video: Traditional Confidence Intervals
 Concept 11: ScreenCast: Traditional Confidence Interval Methods
 Concept 12: Video: Other Language Associated with Confidence Intervals
 Concept 13: Other Language Associated with Confidence Intervals
 Concept 14: Video: Correct Interpretations of Confidence Intervals
 Concept 15: Correct Interpretations of Confidence Intervals
 Concept 16: Video: Confidence Intervals & Hypothesis Tests
 Concept 17: Text: Recap + Next Steps

Lesson 12: Hypothesis Testing
Learn the necessary skills to create and analyze the results in hypothesis testing.
 Concept 01: Introduction
 Concept 02: Hypothesis Testing
 Concept 03: Setting Up Hypothesis Tests  Part I
 Concept 04: Setting Up Hypotheses
 Concept 05: Setting Up Hypothesis Tests  Part II
 Concept 06: Quiz: Setting Up Hypothesis Tests
 Concept 07: Types of Errors  Part I
 Concept 08: Quiz: Types of Errors  Part I
 Concept 09: Types of Errors  Part II
 Concept 10: Quiz: Types of Errors  Part II(a)
 Concept 11: Quiz: Types of Errors  Part II(b)
 Concept 12: Types of Errors  Part III
 Concept 13: Quiz: Types of Errors  Part III
 Concept 14: Common Types of Hypothesis Tests
 Concept 15: Quiz: More Hypothesis Testing Practice
 Concept 16: How Do We Choose Between Hypotheses?
 Concept 17: Video: Simulating from the Null
 Concept 18: Notebook + Quiz: Simulating from the Null
 Concept 19: Solution Notebook: Simulating from the Null
 Concept 20: What is a pvalue Anyway?
 Concept 21: Video: Calculating the pvalue
 Concept 22: Quiz: What is a pvalue Anyway?
 Concept 23: Quiz: Calculating a pvalue
 Concept 24: Quiz: Calculating another pvalue
 Concept 25: Connecting Errors and PValues
 Concept 26: Conclusions in Hypothesis Testing
 Concept 27: Quiz: Connecting Errors and PValues
 Concept 28: Notebook + Quiz: Drawing Conclusions
 Concept 29: Solution Notebook: Drawing Conclusions
 Concept 30: Other Things to Consider  Impact of Large Sample Size
 Concept 31: Other Things to Consider  What If We Test More Than Once?
 Concept 32: Other Things to Consider  How Do CIs and HTs Compare?
 Concept 33: Notebook + Quiz: Impact of Sample Size
 Concept 34: Solution Notebook: Impact of Sample Size
 Concept 35: Notebook + Quiz: Multiple Tests
 Concept 36: Solution Notebook: Multiple tests
 Concept 37: Hypothesis Testing Conclusion
 Concept 38: Quiz + Text: Recap

Lesson 13: Case Study: A/B tests
Work through a case study of how A/B testing works for an online education company called Audacity.
 Concept 01: Introduction
 Concept 02: A/B Testing
 Concept 03: A/B Testing
 Concept 04: Business Example
 Concept 05: Experiment I
 Concept 06: Quiz: Experiment I
 Concept 07: Metric  Click Through Rate
 Concept 08: Click Through Rate
 Concept 09: Experiment II
 Concept 10: Metric  Enrollment Rate
 Concept 11: Metric  Average Reading Duration
 Concept 12: Metric  Average Classroom Time
 Concept 13: Metric  Completion Rate
 Concept 14: Analyzing Multiple Metrics
 Concept 15: Quiz: Analyzing Multiple Metrics
 Concept 16: Drawing Conclusions
 Concept 17: Quiz: Difficulties in A/B Testing
 Concept 18: Conclusion

Lesson 14: Regression
Use python to fit linear regression models, as well as understand how to interpret the results of linear models.
 Concept 01: Video: Introduction
 Concept 02: Video: Introduction to Machine Learning
 Concept 03: Quiz: Machine Learning Big Picture
 Concept 04: Video: Introduction to Linear Regression
 Concept 05: Quiz: Linear Regression Language
 Concept 06: Scatter Plots
 Concept 07: Quizzes On Scatter Plots
 Concept 08: Correlation Coefficients
 Concept 09: Correlation Coefficient Quizzes
 Concept 10: Video: What Defines A Line?
 Concept 11: Quiz: What Defines A Line?  Notation Quiz
 Concept 12: Quiz: What Defines A Line?  Line Basics Quiz
 Concept 13: Video: Fitting A Regression Line
 Concept 14: Text: The Regression Closed Form Solution
 Concept 15: Screencast: Fitting A Regression Line in Python
 Concept 16: Video: How to Interpret the Results?
 Concept 17: Video: Does the Line Fit the Data Well?
 Concept 18: Notebook + Quiz: How to Interpret the Results
 Concept 19: Notebook + Quiz: Regression  Your Turn  Part I
 Concept 20: Notebook + Quiz: Your Turn  Part II
 Concept 21: Video: Recap
 Concept 22: Text: Recap + Next Steps

Lesson 15: Multiple Linear Regression
Learn to apply multiple linear regression models in python. Learn to interpret the results and understand if your model fits well.
 Concept 01: Video: Introduction
 Concept 02: Video: Multiple Linear Regression
 Concept 03: Screencast: Fitting A Multiple Linear Regression Model
 Concept 04: Notebook + Quiz: Fitting A MLR Model
 Concept 05: Screencast + Text: How Does MLR Work?
 Concept 06: Video: Multiple Linear Regression Model Results
 Concept 07: Quiz: Interpreting Coefficients in MLR
 Concept 08: Video: Dummy Variables
 Concept 09: Text: Dummy Variables
 Concept 10: Dummy Variables
 Concept 11: Screencast: Dummy Variables
 Concept 12: Notebook + Quiz: Dummy Variables
 Concept 13: Video: Dummy Variables Recap
 Concept 14: [Optional] Notebook + Quiz: Other Encodings
 Concept 15: Video: Potential Problems
 Concept 16: [Optional] Text: Linear Model Assumptions
 Concept 17: Screencast: Multicollinearity & VIFs
 Concept 18: Video: Multicollinearity & VIFs
 Concept 19: Notebook + Quiz: Multicollinearity & VIFs
 Concept 20: Video: Higher Order Terms
 Concept 21: Text: Higher Order Terms
 Concept 22: Screencast: How to Add Higher Order Terms
 Concept 23: Video: Interpreting Interactions
 Concept 24: Text: Interpreting Interactions
 Concept 25: Notebook + Quiz: Interpreting Model Coefficients
 Concept 26: Video: Recap
 Concept 27: Text: Recap

Lesson 16: Logistic Regression
Learn to apply logistic regression models in python. Learn to interpret the results and understand if your model fits well.
 Concept 01: Video: Introduction
 Concept 02: Video: Fitting Logistic Regression
 Concept 03: Quiz: Logistic Regression Quick Check
 Concept 04: Video: Fitting Logistic Regression in Python
 Concept 05: Notebook + Quiz: Fitting Logistic Regression in Python
 Concept 06: Video: Interpreting Results  Part I
 Concept 07: Video (ScreenCast): Interpret Results  Part II
 Concept 08: Notebook + Quiz: Interpret Results
 Concept 09: Video: Model Diagnostics + Performance Metrics
 Concept 10: Confusion Matrices
 Concept 11: Confusion Matrix Practice 1
 Concept 12: Confusion Matrix Practice 2
 Concept 13: Filling in a Confusion Matrix
 Concept 14: Confusion Matrix: False Alarms
 Concept 15: Confusion Matrix for Eigenfaces
 Concept 16: How Many Schroeders
 Concept 17: How Many Schroeder Predictions
 Concept 18: Classifying Chavez Correctly 1
 Concept 19: Classifying Chavez Correctly 2
 Concept 20: Precision and Recall
 Concept 21: Powell Precision and Recall
 Concept 22: Bush Precision and Recall
 Concept 23: True Positives in Eigenfaces
 Concept 24: False Positives in Eigenfaces
 Concept 25: False Negatives in Eigenfaces
 Concept 26: Practicing TP, FP, FN with Rumsfeld
 Concept 27: Equation for Precision
 Concept 28: Equation for Recall
 Concept 29: Screencast: Model Diagnostics in Python  Part I
 Concept 30: Notebook + Quiz: Model Diagnostics
 Concept 31: Video: Final Thoughts On Shifting to Machine Learning
 Concept 32: Text: Recap
 Concept 33: Video: Congratulations
