Nanodegree key: nd002
Version: 11.0.0
Locale: enus
Learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate your findings.
Content
Part 01 : Welcome to the Nanodegree program!
Welcome to the program! In this part, you’ll get an orientation into using our classroom and services. You’ll also get advice for making the best use of your time while enrolled in this program.

Module 01: Welcome to the Nanodegree program!

Lesson 01: Welcome to the Nanodegree!
Welcome to the Data Analyst Nanodegree program! In this lesson, you will learn more about the structure of the program and meet the team.
 Concept 01: Looking Ahead
 Concept 02: Projects
 Concept 03: Meet the Team
 Concept 04: Orientation Introduction
 Concept 05: Projects and Progress
 Concept 06: Integrity and Mindset
 Concept 07: How Does Project Submission Work?
 Concept 08: How Do I Find Time for My Nanodegree?
 Concept 09: Learning Strategies
 Concept 10: Career Services

Lesson 02: Mentor Help, Peer Chat, and Careers
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Lesson 03: Get Help with Your Account
What to do if you have questions about your account or general questions about the program.

Lesson 04: The Life of a Data Analyst
In this lesson, you'll hear from a few data analysts and data scientists about what it's like to work in data analytics.

Lesson 05: Explore Weather Trends
In this project, you will analyze local and global temperature data and compare the temperature trends where you live to overall global temperature trends.

Part 02 : Introduction to Data Analysis
Learn the data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. Learn how to work with data in Python using libraries like NumPy and Pandas.

Module 01: Introduction to Data Analysis

Lesson 01: Anaconda
In this lesson, you will be getting a quick glimpse at the Anaconda environment  one of the most popular environments for doing data analysis in Python.

Lesson 02: Jupyter Notebooks
Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data viz!
 Concept 01: What are Jupyter notebooks?
 Concept 02: Installing Jupyter Notebook
 Concept 03: Launching the Notebook Server
 Concept 04: Notebook Interface
 Concept 05: Code Cells
 Concept 06: Markdown Cells
 Concept 07: Keyboard Shortcuts
 Concept 08: Magic Keywords
 Concept 09: Converting Notebooks
 Concept 10: Creating a Slideshow
 Concept 11: Finishing up

Lesson 03: The Data Analysis Process
Learn about the data analysis process and practice investigating different datasets using Python and its powerful packages for data analysis.
 Concept 01: Handoff to Juno Lee
 Concept 02: Lesson Overview
 Concept 03: Problems Solved by Data Analysts
 Concept 04: Setting Up Your Programming Environment
 Concept 05: Data Analysis Process Overview
 Concept 06: Data Analysis Process Quiz
 Concept 07: Packages Overview
 Concept 08: Packages Overview Quiz
 Concept 09: Asking Questions
 Concept 10: Questions for a Dataset
 Concept 11: Data Wrangling and EDA
 Concept 12: Gathering Data
 Concept 13: Reading CSV Files
 Concept 14: Assessing and Building Intuition
 Concept 15: Assessing and Building Intuition Quiz
 Concept 16: Cleaning Data
 Concept 17: Cleaning Example
 Concept 18: Cleaning Practice
 Concept 19: Exploring Data with Visuals
 Concept 20: Plotting with Pandas
 Concept 21: Exploring Data with Visuals Quiz
 Concept 22: Drawing Conclusions
 Concept 23: Drawing Conclusions Example
 Concept 24: Drawing Conclusions Quiz
 Concept 25: Communicating Results
 Concept 26: Communicating Results Example
 Concept 27: Communicating Results Practice
 Concept 28: Conclusion

Lesson 04: Data Analysis Process  Case Study 1
Investigate a dataset on chemical properties and quality ratings of wine samples by going through the entire data analysis process and building more skill with Python for data analysis.
 Concept 01: Lesson Overview
 Concept 02: Data Overview
 Concept 03: Asking Questions
 Concept 04: Gathering Data
 Concept 05: Assessing Data
 Concept 06: Appending and NumPy
 Concept 07: Appending Data
 Concept 08: Troubleshooting with Appending
 Concept 09: Renaming Columns
 Concept 10: Appending Data (cont.)
 Concept 11: Exploring with Visuals
 Concept 12: Pandas Groupby
 Concept 13: Conclusions Using Groupby
 Concept 14: Pandas Query
 Concept 15: Conclusions Using Query
 Concept 16: Type & Quality Plot  Part 1
 Concept 17: Type & Quality Plot  Part 2
 Concept 18: Matplotlib Example
 Concept 19: Plotting with Matplotlib
 Concept 20: Type & Quality Plot with Matplotlib
 Concept 21: Conclusion

Lesson 05: Data Analysis Process  Case Study 2
Investigate a more challenging dataset on fuel economy and learn more about problems and strategies in data analysis. Continue to build on your Python for data analysis skills.
 Concept 01: Lesson Overview
 Concept 02: Data Overview
 Concept 03: Data Attributes
 Concept 04: Asking Questions
 Concept 05: Assessing Data
 Concept 06: Cleaning Column Labels
 Concept 07: Filter, Drop Nulls, Dedupe
 Concept 08: Inspecting Data Types
 Concept 09: Fixing Data Types Pt 1
 Concept 10: Fixing Data Types Pt 2
 Concept 11: Fixing Data Types Pt 3
 Concept 12: Exploring with Visuals
 Concept 13: Conclusions & Visuals
 Concept 14: Types of Merges
 Concept 15: Merging Datasets
 Concept 16: Results with Merged Dataset
 Concept 17: Conclusion

Lesson 06: Programming Workflow for Data Analysis
Additional content to expose you to a different workflow for your analysis in Python: IPython's command line interface, writing scripts in text editors, running scripts in the terminal.

Lesson 07: Investigate a Dataset
Choose one of Udacity's curated datasets, perform an investigation, and share your findings.

Part 03 : Practical Statistics
Learn how to apply inferential statistics and probability to important, realworld scenarios, such as analyzing A/B tests and building supervised learning models.

Module 01: 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

Lesson 17: Analyze A/B Test Results
You will be working to understand the results of an A/B test run by an ecommerce website. Your goal is to work through to help the company understand if they should implement the new page design.
 Concept 01: Project Details
 Concept 02: Quiz 1: Understanding the Dataset
 Concept 03: Quiz 2: Messy Data
 Concept 04: Quiz 3: Updated DataFrame
 Concept 05: Quiz 4: Probability
 Concept 06: Quiz 5: Hypothesis Testing
 Concept 07: Completing and Submitting this Project in the Classroom
 Concept 08: BONUS: Project FAQs
 Concept 09: Project Workspace: Complete and Submit Project
 Concept 10: Check Rubric

Lesson 18: 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 04 : Data Wrangling
Learn the data wrangling process of gathering, assessing, and cleaning data. Learn how to use Python to wrangle data programmatically and prepare it for deeper analysis.

Module 01: Data Wrangling

Lesson 01: Introduction to Data Wrangling
Identify each step of the data wrangling process (gathering, assessing, cleaning) through a brief walkthrough of the process. The dataset for this lesson is an online job postings dataset from Kaggle.
 Concept 01: Introduction
 Concept 02: Course Outline
 Concept 03: A Definition and An Analogy
 Concept 04: Examples
 Concept 05: Walkthrough and Dataset
 Concept 06: Gather (Intro)
 Concept 07: Template and Software
 Concept 08: Quiz: Gather (Download)
 Concept 09: Quiz: Gather (Open Jupyter Notebook)
 Concept 10: Quiz: Gather (Unzip File)
 Concept 11: Gather (CSV Files)
 Concept 12: Quiz: Gather (Import)
 Concept 13: Gather (Summary)
 Concept 14: Assess (Intro)
 Concept 15: Quiz: Assess (Visual)
 Concept 16: Quiz: Assess (Programmatic)
 Concept 17: Quiz: Assess (Tidiness)
 Concept 18: Assess (Summary)
 Concept 19: Clean (Intro)
 Concept 20: Clean (Define)
 Concept 21: Quiz: Clean (Code 1)
 Concept 22: Quiz: Clean (Code 2)
 Concept 23: Quiz: Clean (Test)
 Concept 24: Clean (Summary)
 Concept 25: Reassess and Iterate
 Concept 26: Wrangling vs. EDA vs. ETL
 Concept 27: Analysis and Visualization
 Concept 28: Data Wrangling Summary
 Concept 29: Conclusion

Lesson 02: Gathering Data
Gather data from various sources and a variety of file formats using Python. Rotten Tomatoes ratings, Roger Ebert reviews, and Wikipedia movie poster images make up the dataset for this lesson.
 Concept 01: Introduction
 Concept 02: Lesson Outline
 Concept 03: Dataset: Finding the Best Movies
 Concept 04: Navigating Your Working Directory and File I/O
 Concept 05: Source: Files on Hand
 Concept 06: Flat File Structure
 Concept 07: Flat Files in Python
 Concept 08: Source: Web Scraping
 Concept 09: HTML File Structure
 Concept 10: HTML Files in Python
 Concept 11: Flashforward 1
 Concept 12: Source: Downloading Files from the Internet
 Concept 13: Text File Structure
 Concept 14: Text Files in Python
 Concept 15: Source: APIs (Application Programming Interfaces)
 Concept 16: JSON File Structure
 Concept 17: JSON Files in Python
 Concept 18: Mashup: APIs, Downloading Files Programmatically, JSON
 Concept 19: Mashup Solution
 Concept 20: Flashforward 2
 Concept 21: Storing Data
 Concept 22: Relational Database Structure
 Concept 23: Relational Databases in Python
 Concept 24: Other File Formats
 Concept 25: You Can Iterate
 Concept 26: Gathering Summary
 Concept 27: Conclusion

Lesson 03: Assessing Data
Assess data visually and programmatically for quality and tidiness issues using pandas. The dataset for this lesson is mock Phase II clinical trial data for a new oral insulin called Auralin.
 Concept 01: Introduction
 Concept 02: Lesson Outline
 Concept 03: Dataset: Oral Insulin Phase II Clinical Trial Data
 Concept 04: Unclean Data: Dirty vs. Messy 1
 Concept 05: Unclean Data: Dirty vs. Messy 2
 Concept 06: Assessment: Types vs. Steps
 Concept 07: Visual Assessment
 Concept 08: Visual Assessment: Acquaint Yourself
 Concept 09: Quality: Visual Assessment 1
 Concept 10: Assessing vs. Exploring
 Concept 11: Quality: Visual Assessment 2
 Concept 12: Data Quality Dimensions 1
 Concept 13: Data Quality Dimensions 2
 Concept 14: Programmatic Assessment
 Concept 15: Quality: Programmatic Assessment 1
 Concept 16: Quality: Programmatic Assessment 2
 Concept 17: Tidiness: Visual Assessment
 Concept 18: Tidiness: Programmatic Assessment
 Concept 19: How Data Gets Dirty and Messy
 Concept 20: You Can Iterate!
 Concept 21: Assessing Summary
 Concept 22: Conclusion

Lesson 04: Cleaning Data
Using pandas, clean the quality and tidiness issues you identified in the "Assessing Data" lesson. The dataset is the same: mock Phase II clinical trial data for a new oral insulin called Auralin.
 Concept 01: Introduction
 Concept 02: Lesson Outline
 Concept 03: Dataset: Oral Insulin Phase II Clinical Trial Data
 Concept 04: Manual vs. Programmatic Cleaning
 Concept 05: Data Cleaning Process
 Concept 06: Cleaning Sequences
 Concept 07: Quiz and Solution Notebooks
 Concept 08: Address Missing Data First
 Concept 09: Quiz: Missing Data
 Concept 10: Solution: Missing Data
 Concept 11: Cleaning for Tidiness
 Concept 12: Quiz: Tidiness
 Concept 13: Solution: Tidiness
 Concept 14: Cleaning for Quality
 Concept 15: Quiz: Quality
 Concept 16: Solution: Quality
 Concept 17: Flashforward
 Concept 18: You Can Iterate
 Concept 19: Cleaning Summary
 Concept 20: Conclusion

Lesson 05: Wrangle and Analyze Data
Gather data from a variety of sources and in a variety of formats, assess its quality and tidiness, then clean it. Showcase your wrangling efforts through analyses and visualizations.

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 : Data Visualization
Learn to apply sound design and data visualization principles to the data analysis process. Learn how to use analysis and visualizations to tell a story with data.

Module 01: 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.

Lesson 08: Communicate Data Findings
Choose a dataset, either your own or a Udacitycurated dataset, and perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.

Part 06 : Congratulations and Next Steps

Module 01: Congratulations and Next Steps

Lesson 01: Congratulations & Next Steps
Congratulations on completing all your projects!

Part 07 (Elective): Intro to Machine Learning

Module 01: Intro to Machine Learning

Lesson 01: Welcome to Machine Learning
Meet with Sebastian and Katie to discuss machine learning.

Lesson 02: Naive Bayes
Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.
 Concept 01: ML in The Google SelfDriving Car
 Concept 02: Acerous Vs. NonAcerous
 Concept 03: Supervised Classification Example
 Concept 04: Features and Labels Musical Example
 Concept 05: Features Visualization Quiz
 Concept 06: Classification By Eye
 Concept 07: Intro To Stanley Terrain Classification
 Concept 08: Speed Scatterplot: Grade and Bumpiness
 Concept 09: Speed Scatterplot 2
 Concept 10: Speed Scatterplot 3
 Concept 11: From Scatterplots to Predictions
 Concept 12: From Scatterplots to Predictions 2
 Concept 13: From Scatterplots to Decision Surfaces
 Concept 14: A Good Linear Decision Surface
 Concept 15: Transition to Using Naive Bayes
 Concept 16: NB Decision Boundary in Python
 Concept 17: Getting Started With sklearn
 Concept 18: Gaussian NB Example
 Concept 19: GaussianNB Deployment on Terrain Data
 Concept 20: Calculating NB Accuracy
 Concept 21: Training and Testing Data
 Concept 22: Unpacking NB Using Bayes Rule
 Concept 23: Bayes Rule
 Concept 24: Cancer Test
 Concept 25: Prior and Posterior
 Concept 26: Normalizing 1
 Concept 27: Normalizing 2
 Concept 28: Normalizing 3
 Concept 29: Total Probability
 Concept 30: Bayes Rule Diagram
 Concept 31: Bayes Rule for Classification
 Concept 32: Chris or Sara
 Concept 33: Posterior Probabilities
 Concept 34: Bayesian Probabilities On Your Own
 Concept 35: Why Is Naive Bayes Naive
 Concept 36: Naive Bayes Strengths and Weaknesses
 Concept 37: Congrats on Learning Naive Bayes
 Concept 38: Naive Bayes MiniProject Video
 Concept 39: Getting Started with MiniProjects
 Concept 40: Machine Learning for Author ID
 Concept 41: Getting Your Code Set Up
 Concept 42: Author ID Accuracy
 Concept 43: Timing Your NB Classifier

Lesson 03: SVM
Build an intuition about how support vector machines (SVMs) work and implement one using scikitlearn.
 Concept 01: Welcome to SVM
 Concept 02: Separating Line
 Concept 03: Choosing Between Separating Lines
 Concept 04: What Makes A Good Separating Line
 Concept 05: Practice with Margins
 Concept 06: SVMs and Tricky Data Distributions
 Concept 07: SVM Response to Outliers
 Concept 08: SVM Outlier Practice
 Concept 09: Handoff to Katie
 Concept 10: SVM in SKlearn
 Concept 11: SVM Decision Boundary
 Concept 12: Coding Up the SVM
 Concept 13: Nonlinear SVMs
 Concept 14: Nonlinear Data
 Concept 15: A New Feature
 Concept 16: Visualizing the New Feature
 Concept 17: Separating with the New Feature
 Concept 18: Practice Making a New Feature
 Concept 19: Kernel Trick
 Concept 20: Playing Around with Kernel Choices
 Concept 21: Kernel and Gamma
 Concept 22: SVM C Parameter
 Concept 23: SVM Gamma Parameter
 Concept 24: Overfitting
 Concept 25: SVM Strengths and Weaknesses
 Concept 26: SVM MiniProject Video
 Concept 27: SVM MiniProject
 Concept 28: SVM Author ID Accuracy
 Concept 29: SVM Author ID Timing
 Concept 30: A Smaller Training Set
 Concept 31: SpeedAccuracy Tradeoff
 Concept 32: Deploy an RBF Kernel
 Concept 33: Optimize C Parameter
 Concept 34: Accuracy after Optimizing C
 Concept 35: Optimized RBF vs. Linear SVM: Accuracy
 Concept 36: Extracting Predictions from an SVM
 Concept 37: How Many Chris Emails Predicted?
 Concept 38: Final Thoughts on Deploying SVMs

Lesson 04: Decision Trees
Learn about how the decision tree algorithm works, including the concepts of entropy and information gain.
 Concept 01: Welcome To Decision Trees
 Concept 02: Linearly Separable Data
 Concept 03: Multiple Linear Questions
 Concept 04: Constructing a Decision Tree First Split
 Concept 05: Constructing a Decision Tree 2nd Split
 Concept 06: Class Labels After Second Split
 Concept 07: Constructing A Decision Tree/Third Split
 Concept 08: Coding A Decision Tree
 Concept 09: Decision Tree Accuracy
 Concept 10: Decision Tree Parameters
 Concept 11: Min Samples Split
 Concept 12: Decision Tree Accuracy
 Concept 13: Data Impurity and Entropy
 Concept 14: Minimizing Impurity in Splitting
 Concept 15: Formula of Entropy
 Concept 16: Entropy Calculation Part 1
 Concept 17: Entropy Calculation Part 2
 Concept 18: Entropy Calculation Part 3
 Concept 19: Entropy Calculation Part 4
 Concept 20: Entropy Calculation Part 5
 Concept 21: Information Gain
 Concept 22: Information Gain Calculation Part 1
 Concept 23: Information Gain Calculation Part 2
 Concept 24: Information Gain Calculation Part 3
 Concept 25: Information Gain Calculation Part 4
 Concept 26: Information Gain Calculation Part 5
 Concept 27: Information Gain Calculation Part 6
 Concept 28: Information Gain Calculation Part 7
 Concept 29: Information Gain Calculation Part 8
 Concept 30: Information Gain Calculation Part 9
 Concept 31: Information Gain Calculation Part 10
 Concept 32: Tuning Criterion Parameter
 Concept 33: BiasVariance Dilemma
 Concept 34: DT Strengths and Weaknesses
 Concept 35: Decision Tree MiniProject Video
 Concept 36: Decision Tree MiniProject
 Concept 37: Your First Email DT: Accuracy
 Concept 38: Speeding Up Via Feature Selection 1
 Concept 39: Changing the Number of Features
 Concept 40: SelectPercentile and Complexity
 Concept 41: Accuracy Using 1% of Features

Lesson 05: Choose Your Own Algorithm
In this mini project, you will extend your toolbox of algorithms by choosing your own algorithm to classify terrain data, including knearest neighbors, AdaBoost, and random forests.
 Concept 01: Choose Your own Algorithm
 Concept 02: Why Study a New Algorithm Solo?
 Concept 03: Choose Your Own Adventure
 Concept 04: Algorithm Options
 Concept 05: Investigation Process
 Concept 06: ChooseYourOwn Algorithm Checklist
 Concept 07: How Does Your Algorithm Compare
 Concept 08: Can You Beat Our High Score?
 Concept 09: L4_Mini Project
 Concept 10: Welcome to the end of the lesson

Lesson 06: Datasets and Questions
Find out about the Enron data set used in the next lessons and miniprojects.
 Concept 01: Introduction
 Concept 02: What Is A POI
 Concept 03: Accuracy vs. Training Set Size
 Concept 04: Downloading Enron Data
 Concept 05: Types of Data Quiz 1
 Concept 06: Types of Data Quiz 2
 Concept 07: Types of Data Quiz 3
 Concept 08: Types of Data Quiz 4
 Concept 09: Types of Data Quiz 5
 Concept 10: Types of Data Quiz 6
 Concept 11: Enron Dataset MiniProject Video
 Concept 12: Datasets and Questions MiniProject
 Concept 13: Size of the Enron Dataset
 Concept 14: Features in the Enron Dataset
 Concept 15: Finding POIs in the Enron Data
 Concept 16: How Many POIs Exist?
 Concept 17: Problems with Incomplete Data
 Concept 18: Query the Dataset 1
 Concept 19: Query the Dataset 2
 Concept 20: Query the Dataset 3
 Concept 21: Research the Enron Fraud
 Concept 22: Enron CEO
 Concept 23: Enron Chairman
 Concept 24: Enron CFO
 Concept 25: Follow the Money
 Concept 26: Unfilled Features
 Concept 27: Dealing with Unfilled Features
 Concept 28: Dicttoarray conversion
 Concept 29: Missing POIs 1 (optional)
 Concept 30: Missing POIs 2 (optional)
 Concept 31: Missing POIs 3 (optional)
 Concept 32: Missing POIs 4 (optional)
 Concept 33: Missing POIs 5 (optional)
 Concept 34: Missing POIs 6 (optional)
 Concept 35: Mixing Data Sources (optional)

Lesson 07: Regressions
See how we can model continuous data using linear regression.
 Concept 01: Continuous Output Quiz
 Concept 02: Continuous Quiz
 Concept 03: Age: Continuous or Discrete?
 Concept 04: Weather: Continuous or Discrete
 Concept 05: Email Author: Continuous or Discrete
 Concept 06: Phone Number: Continuous or Discrete?
 Concept 07: Income: Continuous or Discrete?
 Concept 08: Continuous Feature Quiz
 Concept 09: Supervised Learning w/ Continuous Output
 Concept 10: Equation of the Regression Line
 Concept 11: Slope and Intercept
 Concept 12: Slope Quiz
 Concept 13: Intercept Quiz
 Concept 14: Predictions Using Regression
 Concept 15: Adding An Intercept
 Concept 16: Handoff to Katie
 Concept 17: Coding It Up
 Concept 18: Age/Net Worth Regression in sklearn
 Concept 19: Extracting Information from sklearn
 Concept 20: Extracting Score Data from sklearn
 Concept 21: Linear Regression Errors
 Concept 22: Error Quiz
 Concept 23: Errors and Fit Quality
 Concept 24: Minimizing Sum of Squared Errors
 Concept 25: Algorithms for Minimizing Squared Errors
 Concept 26: Why Minimize SSE
 Concept 27: Problem with Minimizing Absolute Errors
 Concept 28: Evaluating Regression by Eye
 Concept 29: Problem with SSE
 Concept 30: R Squared Metric for Regression
 Concept 31: R Squared in SKlearn
 Concept 32: Visualizing Regression
 Concept 33: What Data Is Good For Linear Regression
 Concept 34: Comparing Classification and Regression
 Concept 35: Multivariate Regression Quiz
 Concept 36: MultiVariate Regression Quiz 2
 Concept 37: Regression MiniProject Video
 Concept 38: Regression MiniProject
 Concept 39: Bonus Target and Features
 Concept 40: Visualizing Regression Data
 Concept 41: Extracting Slope and Intercept
 Concept 42: Regression Score: Training Data
 Concept 43: Regression Score: Test Data
 Concept 44: Regressing Bonus Against LTI
 Concept 45: Salary vs. LTI for Predicting Bonus
 Concept 46: Sneak Peek: Outliers Break Regressions

Lesson 08: Outliers
Sebastian discusses outlier detection and removal.
 Concept 01: Outliers in Regression
 Concept 02: What Causes Outliers
 Concept 03: Outlier Selection
 Concept 04: Outlier Detection/Removal Algorithm
 Concept 05: Outlier Detection Using Residual Errors
 Concept 06: Effect of Outlier Removal on Regression
 Concept 07: Summary of Outlier Removal Strategy
 Concept 08: Outliers MiniProject Video
 Concept 09: Outliers MiniProject
 Concept 10: Slope of Regression with Outliers
 Concept 11: Score of Regression with Outliers
 Concept 12: Slope After Cleaning
 Concept 13: Score After Cleaning
 Concept 14: Enron Outliers
 Concept 15: Identify the Biggest Enron Outlier
 Concept 16: Remove Enron Outlier?
 Concept 17: Any More Outliers?
 Concept 18: Identifying Two More Outliers
 Concept 19: Remove These Outliers?

Lesson 09: Clustering
Learn about what unsupervised learning is and find out how to use scikitlearn's kmeans algorithm.
 Concept 01: Unsupervised Learning
 Concept 02: Clustering Movies
 Concept 03: How Many Clusters?
 Concept 04: Match Points with Clusters
 Concept 05: Optimizing Centers (Rubber Bands)
 Concept 06: Moving Centers 2
 Concept 07: Match Points (again)
 Concept 08: Handoff to Katie
 Concept 09: KMeans Cluster Visualization
 Concept 10: KMeans Clustering Visualization 2
 Concept 11: KMeans Clustering Visualization 3
 Concept 12: Sklearn
 Concept 13: Some challenges of kmeans
 Concept 14: Limitations of KMeans
 Concept 15: Counterintuitive Clusters
 Concept 16: Counterintuitive Clusters 2
 Concept 17: Clustering MiniProject Video
 Concept 18: KMeans Clustering MiniProject
 Concept 19: Clustering Features
 Concept 20: Deploying Clustering
 Concept 21: Clustering with 3 Features
 Concept 22: Stock Option Range
 Concept 23: Salary Range
 Concept 24: Clustering Changes

Lesson 10: Feature Scaling
Learn about feature rescaling and find out which algorithms require feature rescaling before use.
 Concept 01: Chris's TShirt Size (Intuition)
 Concept 02: A Metric for Chris
 Concept 03: Height + Weight for Cameron
 Concept 04: Sarah's Height + Weight
 Concept 05: Chris's Shirt Size by Our Metric
 Concept 06: Comparing Features with Different Scales
 Concept 07: Feature Scaling Formula Quiz 1
 Concept 08: Feature Scaling Formula Quiz 2
 Concept 09: Feature Scaling Formula Quiz 3
 Concept 10: Min/Max Rescaler Coding Quiz
 Concept 11: Min/Max Scaler in sklearn
 Concept 12: Quiz on Algorithms Requiring Rescaling
 Concept 13: Feature Scaling MiniProject Video
 Concept 14: Feature Scaling MiniProject
 Concept 15: What Kind of Scaling
 Concept 16: Computing Rescaled Features
 Concept 17: When to Deploy Feature Scaling

Lesson 11: Text Learning
Find out how to use text data in your machine learning algorithm.
 Concept 01: Dimensions when Learning From Text
 Concept 02: Bag Of Words
 Concept 03: A Very Nice Day
 Concept 04: Mr. Day Loves a Nice Day
 Concept 05: Properties of Bag of Words
 Concept 06: Bag of Words in Sklearn
 Concept 07: LowInformation Words
 Concept 08: Stopwords
 Concept 09: Getting Stopwords from NLTK
 Concept 10: Stemming to Consolidate Vocabulary
 Concept 11: Stemming with NLTK
 Concept 12: Order of Operations in Text Processing
 Concept 13: Weighting by Term Frequency
 Concept 14: Why Upweight Rare Words
 Concept 15: Text Learning MiniProject Video
 Concept 16: Text Learning MiniProject
 Concept 17: Warming Up with parseOutText()
 Concept 18: Deploying Stemming
 Concept 19: Clean Away "Signature Words"
 Concept 20: TfIdf It
 Concept 21: Accessing TfIdf Features

Lesson 12: Feature Selection
Katie discusses when and why to use feature selection, and provides some methods for doing this.
 Concept 01: Why Feature Selection?
 Concept 02: A New Enron Feature
 Concept 03: A New Enron Feature Quiz
 Concept 04: Visualizing Your New Feature
 Concept 05: Beware of Feature Bugs!
 Concept 06: Example: Buggy Feature
 Concept 07: Getting Rid of Features
 Concept 08: Features != Information
 Concept 09: Univariate Feature Selection
 Concept 10: Feature Selection in TfIdf Vectorizer
 Concept 11: Bias, Variance, and Number of Features
 Concept 12: Bias, Variance & Number of Features Pt 2
 Concept 13: Overfitting by Eye
 Concept 14: Balancing Error with Number of Features
 Concept 15: Regularization
 Concept 16: Lasso Regression
 Concept 17: Lasso Code Quiz
 Concept 18: Lasso Prediction with sklearn Quiz
 Concept 19: Lasso Coefficients with sklearn Quiz
 Concept 20: Using Lasso in sklearn Quiz
 Concept 21: Feature Selection MiniProject Video
 Concept 22: Feature Selection MiniProject
 Concept 23: Overfitting a Decision Tree 1
 Concept 24: Overfitting a Decision Tree 2
 Concept 25: Number of Features and Overfitting
 Concept 26: Accuracy of Your Overfit Decision Tree
 Concept 27: Identify the Most Powerful Features
 Concept 28: Use TfIdf to Get the Most Important Word
 Concept 29: Remove, Repeat
 Concept 30: Checking Important Features Again
 Concept 31: Accuracy of the Overfit Tree

Lesson 13: PCA
Learn about data dimensionality and reducing the number of dimensions with principal component analysis (PCA).
 Concept 01: Data Dimensionality
 Concept 02: Trickier Data Dimensionality
 Concept 03: OneDimensional, or Two?
 Concept 04: Slightly Less Perfect Data
 Concept 05: Trickiest Data Dimensionality
 Concept 06: PCA for Data Transformation
 Concept 07: Center of a New Coordinate System
 Concept 08: Principal Axis of New Coordinate System
 Concept 09: Second Principal Component of New System
 Concept 10: Practice Finding Centers
 Concept 11: Practice Finding New Axes
 Concept 12: Which Data is Ready for PCA
 Concept 13: When Does an Axis Dominate
 Concept 14: Measurable vs. Latent Features Quiz
 Concept 15: From Four Features to Two
 Concept 16: Compression While Preserving Information
 Concept 17: Composite Features
 Concept 18: Maximal Variance
 Concept 19: Advantages of Maximal Variance
 Concept 20: Maximal Variance and Information Loss
 Concept 21: Info Loss and Principal Components
 Concept 22: Neighborhood Composite Feature
 Concept 23: PCA for Feature Transformation
 Concept 24: Maximum Number of PCs Quiz
 Concept 25: Review/Definition of PCA
 Concept 26: Applying PCA to Real Data
 Concept 27: PCA on the Enron Finance Data
 Concept 28: PCA in sklearn
 Concept 29: When to Use PCA
 Concept 30: PCA for Facial Recognition
 Concept 31: Eigenfaces Code
 Concept 32: PCA MiniProject Intro
 Concept 33: PCA MiniProject
 Concept 34: Explained Variance of Each PC
 Concept 35: How Many PCs to Use?
 Concept 36: F1 Score vs. No. of PCs Used
 Concept 37: Dimensionality Reduction and Overfitting
 Concept 38: Selecting Principal Components

Lesson 14: Validation
Learn more about testing, training, cross validation, and parameter grid searches in this lesson.
 Concept 01: Cross Validation for Fun and Profit
 Concept 02: Benefits of Testing
 Concept 03: Train/Test Split in sklearn
 Concept 04: Where to use training vs. testing data 1
 Concept 05: Where to use training vs. testing data 2
 Concept 06: Where to use training vs. testing data 3
 Concept 07: Where to use training vs. testing data 4
 Concept 08: KFold Cross Validation
 Concept 09: KFold CV in sklearn
 Concept 10: Practical Advice for KFold in sklearn
 Concept 11: Cross Validation for Parameter Tuning
 Concept 12: GridSearchCV in sklearn
 Concept 13: GridSearchCV in sklearn
 Concept 14: On to the Validation MiniProject
 Concept 15: Validation MiniProject Video
 Concept 16: Validation MiniProject
 Concept 17: Your First (Overfit) POI Identifier
 Concept 18: Deploying a Training/Testing Regime

Lesson 15: Evaluation Metrics
How do we know if our classifier is performing well? Katie discusses different evaluation metrics for classifiers in this lesson.
 Concept 01: Welcome to Evaluation Metrics Lesson
 Concept 02: Accuracy Review
 Concept 03: Shortcomings of Accuracy
 Concept 04: Picking the Most Suitable Metric
 Concept 05: Confusion Matrices
 Concept 06: Confusion Matrix Practice 1
 Concept 07: Confusion Matrix Practice 2
 Concept 08: Filling in a Confusion Matrix
 Concept 09: Confusion Matrix: False Alarms
 Concept 10: Decision Tree Confusion Matrix
 Concept 11: Confusion Matrix for Eigenfaces
 Concept 12: How Many Schroeders
 Concept 13: How Many Schroeder Predictions
 Concept 14: Classifying Chavez Correctly 1
 Concept 15: Classifying Chavez Correctly 2
 Concept 16: Precision and Recall
 Concept 17: Powell Precision and Recall
 Concept 18: Bush Precision and Recall
 Concept 19: True Positives in Eigenfaces
 Concept 20: False Positives in Eigenfaces
 Concept 21: False Negatives in Eigenfaces
 Concept 22: Practicing TP, FP, FN with Rumsfeld
 Concept 23: Equation for Precision
 Concept 24: Equation for Recall
 Concept 25: Welcome to the End of Evaluation Lesson
 Concept 26: Evaluation MiniProject Video
 Concept 27: Applying Metrics to Your POI Identifier
 Concept 28: Number of POIs in Test Set
 Concept 29: Number of People in Test Set
 Concept 30: Accuracy of a Biased Identifier
 Concept 31: Number of True Positives
 Concept 32: Unpacking Into Precision and Recall
 Concept 33: Recall of Your POI Identifier
 Concept 34: How Many True Positives?
 Concept 35: How Many True Negatives?
 Concept 36: False Positives?
 Concept 37: False Negatives?
 Concept 38: Precision
 Concept 39: Recall
 Concept 40: Making Sense of Metrics 1
 Concept 41: Making Sense of Metrics 2
 Concept 42: Making Sense of Metrics 3
 Concept 43: Making Sense of Metrics 4
 Concept 44: Metrics for Your POI Identifier

Lesson 16: Tying It All Together
Spend some time reflecting on the course material with Sebastian and Katie!

Part 08 (Elective): Matrix Math and NumPy Refresher

Module 01: Matrix Math and NumPy Refresher

Lesson 01: Matrix Math and NumPy Refresher
In this lesson, you'll review the matrix math you'll need to understand to build your neural networks. You'll also explore NumPy, the library you'll use to efficiently deal with matrices in Python.
 Concept 01: Introduction
 Concept 02: Data Dimensions
 Concept 03: Data in NumPy
 Concept 04: Elementwise Matrix Operations
 Concept 05: Elementwise Operations in NumPy
 Concept 06: Matrix Multiplication: Part 1
 Concept 07: Matrix Multiplication: Part 2
 Concept 08: NumPy Matrix Multiplication
 Concept 09: Matrix Transposes
 Concept 10: Transposes in NumPy
 Concept 11: NumPy Quiz

Part 09 (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: 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: 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: More Practice!
 Concept 20: Video: SQL Completion Congratulations

Part 10 (Elective): Prerequisite: Python

Module 01: Prerequisite: Python

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, builtin functions, type conversion, whitespace, 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: Quiz: String Methods Practice
 Concept 23: Solution: String Methods Practice
 Concept 24: "There's a Bug in my Code"
 Concept 25: Conclusion
 Concept 26: Summary

Lesson 03: Data Structures
Use data structures to order and group different data types together! Learn about the types of data structures in Python, along with more useful builtin functions and operators.
 Concept 01: Introduction
 Concept 02: Lists and Membership Operators
 Concept 03: Quiz: Lists and Membership Operators
 Concept 04: Solution: List and Membership Operators
 Concept 05: Why Do We Need Lists?
 Concept 06: List Methods
 Concept 07: Quiz: List Methods
 Concept 08: Check for Understanding: Lists
 Concept 09: Tuples
 Concept 10: Quiz: Tuples
 Concept 11: Sets
 Concept 12: Quiz: Sets
 Concept 13: Dictionaries and Identity Operators
 Concept 14: Quiz: Dictionaries and Identity Operators
 Concept 15: Solution: Dictionaries and Identity Operators
 Concept 16: Quiz: More With Dictionaries
 Concept 17: When to Use Dictionaries?
 Concept 18: Check for Understanding: Data Structures
 Concept 19: Compound Data Structures
 Concept 20: Quiz: Compound Data Structures
 Concept 21: Solution: Compound Data Structions
 Concept 22: Practice Questions
 Concept 23: Solution: Practice Questions
 Concept 24: Conclusion

Lesson 04: 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: For Loops vs. While Loops
 Concept 26: Check for Understanding: For and While Loops
 Concept 27: Solution: Check for Understanding: For and While Loops
 Concept 28: Break, Continue
 Concept 29: Quiz: Break, Continue
 Concept 30: Solution: Break, Continue
 Concept 31: Practice: Loops
 Concept 32: Solution: Loops
 Concept 33: Zip and Enumerate
 Concept 34: Quiz: Zip and Enumerate
 Concept 35: Solution: Zip and Enumerate
 Concept 36: List Comprehensions
 Concept 37: Quiz: List Comprehensions
 Concept 38: Solution: List Comprehensions
 Concept 39: Practice Questions
 Concept 40: Solutions to Practice Questions
 Concept 41: Conclusion

Lesson 05: 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: Check For Understanding: Functions
 Concept 06: Variable Scope
 Concept 07: Variable Scope
 Concept 08: Solution: Variable Scope
 Concept 09: Check For Understanding: Variable Scope
 Concept 10: Documentation
 Concept 11: Quiz: Documentation
 Concept 12: Solution: Documentation
 Concept 13: Lambda Expressions
 Concept 14: Quiz: Lambda Expressions
 Concept 15: Solution: Lambda Expressions
 Concept 16: Conclusion

Lesson 06: 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: Quiz: Practice Debugging
 Concept 21: Solutions for Quiz: Practice Debugging
 Concept 22: Importing Local Scripts
 Concept 23: The Standard Library
 Concept 24: Quiz: The Standard Library
 Concept 25: Solution: The Standard Library
 Concept 26: Techniques for Importing Modules
 Concept 27: Quiz: Techniques for Importing Modules
 Concept 28: ThirdParty Libraries
 Concept 29: Experimenting with an Interpreter
 Concept 30: Online Resources
 Concept 31: Practice Question
 Concept 32: Solution for Practice Question
 Concept 33: Conclusion

Lesson 07: 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: Quiz: Creating and Saving NumPy ndarrays
 Concept 06: Solution: Creating and Saving NumPy ndarrays
 Concept 07: Using Builtin Functions to Create ndarrays
 Concept 08: Create an ndarray
 Concept 09: Accessing, Deleting, and Inserting Elements Into ndarrays
 Concept 10: Slicing ndarrays
 Concept 11: Boolean Indexing, Set Operations, and Sorting
 Concept 12: Manipulating ndarrays
 Concept 13: Arithmetic operations and Broadcasting
 Concept 14: Creating ndarrays with Broadcasting

Lesson 08: 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

Part 11 (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.
