Nanodegree key: nd002
Version: 11.0.0
Locale: en-us
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.
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Module 01: Orientation
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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: Access the Career Portal
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Lesson 02: Knowledge, Community, and Careers
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
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Lesson 03: Get Help with Your Account
What to do if you have questions about your account or general questions about the program.
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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.
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Module 02: Intro Project
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Lesson 01: 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.
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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.
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Module 01: Anaconda
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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.
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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
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Module 02: Python for Data Analysis
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Lesson 01: 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
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Lesson 02: 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
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Lesson 03: 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
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Lesson 04: 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.
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Module 03: Investigate a Dataset
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Lesson 01: Investigate a Dataset
Choose one of Udacity's curated datasets, perform an investigation, and share your findings.
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Part 03 : Practical Statistics
Learn how to apply inferential statistics and probability to important, real-world scenarios, such as analyzing A/B tests and building supervised learning models.
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Module 01: Practical Statistics
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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
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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
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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
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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
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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
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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
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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
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Lesson 08: Python Probability Practice
Take what you have learned in the last lessons and put it to practice in Python.
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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
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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
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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
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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 p-value Anyway?
- Concept 21: Video: Calculating the p-value
- Concept 22: Quiz: What is a p-value Anyway?
- Concept 23: Quiz: Calculating a p-value
- Concept 24: Quiz: Calculating another p-value
- Concept 25: Connecting Errors and P-Values
- Concept 26: Conclusions in Hypothesis Testing
- Concept 27: Quiz: Connecting Errors and P-Values
- 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
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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
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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
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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
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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
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Module 02: Analyze A/B Test Results
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Lesson 01: Analyze A/B Test Results
You will be working to understand the results of an A/B test run by an e-commerce 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: Project Workspace: Complete and Submit Project
- Concept 09: Check Rubric
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Module 03: Career Services: GitHub Profile
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Lesson 01: 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
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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.
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Module 01: Introduction to Data Wrangling
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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
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Module 02: Gathering Data
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Lesson 01: 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
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Module 03: Assessing Data
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Lesson 01: 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
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-
Module 04: Cleaning Data
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Lesson 01: 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
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Module 05: Project
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Lesson 01: 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.
-
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Module 06: Career Services: LinkedIn Profile
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Lesson 01: 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
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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 with Python
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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
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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
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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
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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: Q-Q Plots
- Concept 17: Extra: Swarm Plots
- Concept 18: Extra: Rug and Strip Plots
- Concept 19: Extra: Stacked Plots
- Concept 20: Extra: Ridgeline Plots
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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: Non-Positional 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
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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
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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.
-
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Module 02: Communicate Data Findings
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Lesson 01: Communicate Data Findings
Choose a dataset, either your own or a Udacity-curated dataset, and perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.
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Part 06 : Congratulations and Next Steps
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Module 01: Congratulations
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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 Self-Driving Car
- Concept 02: Acerous Vs. Non-Acerous
- 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 Mini-Project Video
- Concept 39: Getting Started with Mini-Projects
- 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
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Lesson 03: SVM
Build an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.
- 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 Mini-Project Video
- Concept 27: SVM Mini-Project
- Concept 28: SVM Author ID Accuracy
- Concept 29: SVM Author ID Timing
- Concept 30: A Smaller Training Set
- Concept 31: Speed-Accuracy 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
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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: Bias-Variance Dilemma
- Concept 34: DT Strengths and Weaknesses
- Concept 35: Decision Tree Mini-Project Video
- Concept 36: Decision Tree Mini-Project
- 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
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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 k-nearest 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: Choose-Your-Own 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
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Lesson 06: Datasets and Questions
Find out about the Enron data set used in the next lessons and mini-projects.
- 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 Mini-Project Video
- Concept 12: Datasets and Questions Mini-Project
- 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: Dict-to-array 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)
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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: Multi-Variate Regression Quiz 2
- Concept 37: Regression Mini-Project Video
- Concept 38: Regression Mini-Project
- 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
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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 Mini-Project Video
- Concept 09: Outliers Mini-Project
- 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?
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Lesson 09: Clustering
Learn about what unsupervised learning is and find out how to use scikit-learn's k-means 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: K-Means Cluster Visualization
- Concept 10: K-Means Clustering Visualization 2
- Concept 11: K-Means Clustering Visualization 3
- Concept 12: Sklearn
- Concept 13: Some challenges of k-means
- Concept 14: Limitations of K-Means
- Concept 15: Counterintuitive Clusters
- Concept 16: Counterintuitive Clusters 2
- Concept 17: Clustering Mini-Project Video
- Concept 18: K-Means Clustering Mini-Project
- 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
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Lesson 10: Feature Scaling
Learn about feature rescaling and find out which algorithms require feature rescaling before use.
- Concept 01: Chris's T-Shirt 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 Mini-Project Video
- Concept 14: Feature Scaling Mini-Project
- Concept 15: What Kind of Scaling
- Concept 16: Computing Rescaled Features
- Concept 17: When to Deploy Feature Scaling
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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: Low-Information 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 Mini-Project Video
- Concept 16: Text Learning Mini-Project
- 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
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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 Mini-Project Video
- Concept 22: Feature Selection Mini-Project
- 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
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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: One-Dimensional, 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 Mini-Project Intro
- Concept 33: PCA Mini-Project
- 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
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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: K-Fold Cross Validation
- Concept 09: K-Fold CV in sklearn
- Concept 10: Practical Advice for K-Fold 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 Mini-Project
- Concept 15: Validation Mini-Project Video
- Concept 16: Validation Mini-Project
- Concept 17: Your First (Overfit) POI Identifier
- Concept 18: Deploying a Training/Testing Regime
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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 Mini-Project 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
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Lesson 16: Tying It All Together
Spend some time reflecting on the course material with Sebastian and Katie!
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Part 08 (Elective): Matrix Math and NumPy Refresher
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Module 01: Matrix Math and Numpy Refresher
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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: Element-wise Matrix Operations
- Concept 05: Element-wise 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
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Part 09 (Elective): Prerequisite: SQL
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Module 01: SQL for Data Analysis
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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 Non-Numeric Data
- Concept 28: Quiz: WHERE with Non-Numeric
- Concept 29: Solutions: WHERE with Non-Numeric
- 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
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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
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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
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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
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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
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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
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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
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Part 10 (Elective): Prerequisite: Python
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Module 01: Python
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Lesson 01: Why Python Programming
Welcome to Introduction to Python! Here's an overview of the course.
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Lesson 02: Data Types and Operators
Familiarize yourself with the building blocks of Python! Learn about data types and operators, built-in 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
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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 built-in 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
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Lesson 04: Control Flow
Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in 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
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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
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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: Third-Party Libraries
- Concept 29: Experimenting with an Interpreter
- Concept 30: Online Resources
- Concept 31: Practice Question
- Concept 32: Solution for Practice Question
- Concept 33: Conclusion
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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 Built-in 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
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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
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Part 11 (Elective): Prerequisite: Git & GitHub
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Module 01: Version Control with Git
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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!
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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.
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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.
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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.
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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.
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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.
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