Data Scientist Nanodegree
- Content

Data Scientist Nanodegree

助教微信

udacity公众号

Nanodegree key: nd025
Version: 6.0.0
Locale: en-us
Get hands-on experience running data pipelines, designing experiments, building recommendation systems, and more.

Content

Part 01 : Welcome to the Nanodegree program

Module 01: Welcome to the Nanodegree program
- Lesson 01: Welcome to the Nanodegree Program
- Lesson 02: Get Help with Your Account
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 03: The Skills That Set You Apart
  - Concept 01: What do Data Scientists Do?
  - Concept 02: Interview: Robert Chang [AirBnB]
  - Concept 03: Interview: Caroline [BMG]
  - Concept 04: Interview: Dan [Coinbase]
  - Concept 05: Interview: Richard [Starbucks]
  - Concept 06: Outro
    Part 02 : Introduction to Data Science
Module 01: Introduction to Data Science
- Lesson 01: The Data Science ProcessIn this lesson, you will learn about CRISP-DM and how you can apply it to many data science problems.
- Lesson 02: Communicating to StakeholdersIn this lesson, you will be creating a post to communicate your findings via Medium.
- Lesson 03: Project: Writing a Data Scientist Blog PostProject Description - Write A Data Science Blog PostProject Rubric - Write A Data Science Blog Post
  - Concept 01: Project Overview
  - Concept 02: Project Motivation and Details
Module 02: Career Support
- Lesson 01: Optimize Your GitHub ProfileOther professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.Project Description - Optimize Your GitHub ProfileProject Rubric - Optimize Your GitHub Profile
  - Concept 01: Prove Your Skills With GitHub
  - Concept 02: Introduction
  - Concept 03: GitHub profile important items
  - Concept 04: Good GitHub repository
  - Concept 05: Interview with Art - Part 1
  - Concept 06: Identify fixes for example “bad” profile
  - Concept 07: Quick Fixes #1
  - Concept 08: Quick Fixes #2
  - Concept 09: Writing READMEs with Walter
  - Concept 10: Interview with Art - Part 2
  - Concept 11: Commit messages best practices
  - Concept 12: Reflect on your commit messages
  - Concept 13: Participating in open source projects
  - Concept 14: Interview with Art - Part 3
  - Concept 15: Participating in open source projects 2
  - Concept 16: Starring interesting repositories
  - Concept 17: Next Steps
    Part 03 : Software Engineering
    Software engineering skills are increasingly important for data scientists. In this course, you’ll learn best practices for writing software. Then you’ll work on your software skills by coding a Python package and a web data dashboard.
Module 01: Software Engineering
- Lesson 01: Introduction to Software EngineeringWelcome to Software Engineering for Data Scientists! Learn about the course and meet your instructors.
- Lesson 02: Software Engineering Practices Pt ILearn software engineering practices and how they apply in data science. Part one covers clean and modular code, code efficiency, refactoring, documentation, and version control.
- Lesson 03: Software Engineering Practices Pt IILearn software engineering practices and how they apply in data science. Part two covers testing code, logging, and conducting code reviews.
- Lesson 04: Introduction to Object-Oriented ProgrammingLearn the basics of object-oriented programming so that you can build your own Python package.
- Lesson 05: Portfolio Exercise: Upload a Package to PyPiCreate your own Python package and upload your package to PyPi.
- Lesson 06: Web DevelopmentDevelop a data dashboard using Flask, Bootstrap, Plotly and Pandas.
- Lesson 07: Portfolio Exercise: Deploy a Data DashboardCustomize the data dashboard from the previous lesson to make it your own. Upload the dashboard to the web.
  - Concept 01: Introduction
  - Concept 02: Workspace Portfolio Exercise
  - Concept 03: Troubleshooting Possible Errors
  - Concept 04: Congratulations
  - Concept 05: APIs [advanced version]
  - Concept 06: World Bank API [advanced version]
  - Concept 07: Python and APIs [advanced version]
  - Concept 08: World Bank Data Dashboard [advanced version]
    Part 04 : Data Engineering
    In data engineering for data scientists, you will practice building ETL, NLP, and machine learning pipelines. This will prepare you for the project with our industry partner Figure 8.
Module 01: Data Engineering
- Lesson 01: Introduction to Data EngineeringYou will get an introduction to the data engineering for data scientists course and project. The lessons include ETL pipelines, natural language pipelines, and machine learning pipelines.
- Lesson 02: ETL PipelinesETL stands for extract, transform, and load. This is the most common type of data pipeline, and you will practice each step in this lesson.
- Lesson 03: NLP PipelinesIn order to complete the project at the end of the course, you will need some natural language processing skills. Here you will practice engineering machine learning features from text data.
- Lesson 04: Machine Learning PipelinesYou’ll use the Scikit-Learn package to code a machine learning pipeline. With these skills, you can ingest data, create features, and train a machine learning algorithm in just one step.
Module 02: Project
- Lesson 01: Project: Disaster Response PipelineYou’ll build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.Project Description - Disaster Response PipelinesProject Rubric - Disaster Response Pipelines
Module 03: Career Support
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
  - Concept 01: Get Opportunities with LinkedIn
  - Concept 02: Use Your Story to Stand Out
  - Concept 03: Why Use an Elevator Pitch
  - Concept 04: Create Your Elevator Pitch
  - Concept 05: Use Your Elevator Pitch on LinkedIn
  - Concept 06: Create Your Profile With SEO In Mind
  - Concept 07: Profile Essentials
  - Concept 08: Work Experiences & Accomplishments
  - Concept 09: Build and Strengthen Your Network
  - Concept 10: Reaching Out on LinkedIn
  - Concept 11: Boost Your Visibility
  - Concept 12: Up Next
    Part 05 : Experimental Design & Recommendations
    Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.
Module 01: Intro to Experiment Design and Recommendation Engines
- Lesson 01: Intro to Experiment Design and Recommendation EnginesWhy do we care about experiment design and recommendation engines? In this lesson, you’ll get an overview of the topics you’ll learn in this course.
Module 02: Experiment Design & A/B Testing
- Lesson 01: Concepts in Experiment DesignIn this lesson, you will learn about conceptual topics that must be considered when designing and running an experiment, in order to ensure good, interpretable results.
- Lesson 02: Statistical Considerations in TestingIn this lesson, you will learn how statistics can be used to benefit the design of an experiment, as well as additional statistical tests that can be used to analyze results.
- Lesson 03: A/B Testing Case StudyIn this lesson, you will go through an A/B Testing case study to see how the conceptual and statistical concepts covered in the previous lessons can be applied in experiment designs.
- Lesson 04: Portfolio Exercise: Starbucks In this lesson, you will analyze data that was originally used in screening interviews for data scientists at Starbucks.
Module 03: Recommendation Engines
- Lesson 01: Introduction to Recommendation EnginesIn this lesson, you will learn about the different methods used to create recommendation engines.
- Lesson 02: Matrix Factorization for RecommendationsIn this lesson, you will learn how machine learning is being used to make recommendations.
Module 04: Project: Recommendations with IBM
- Lesson 01: Recommendation EnginesPut your skills to work to make recommendations for IBM Watson Studio’s data platform.Project Description - Recommendations with IBMProject Rubric - Recommendations with IBM
  - Concept 01: Project Introduction
  - Concept 02: Project Details
  - Concept 03: Project Workspace
    Part 06 : Data Scientist Capstone
    Leverage what you’ve learned throughout the program to build your own open-ended Data Science project. This project will serve as a demonstration of your valuable abilities as a Data Scientist.
Module 01: Lessons
- Lesson 01: Communicating to StakeholdersIn this lesson, you will be creating a post to communicate your findings via Medium.
Module 02: Capstone Project
- Lesson 01: Data Scientist CapstoneNow you will put your Data Science skills to the test by solving a real world problem using all that you have learned throughout the program.Project Description - Capstone ProjectProject Rubric - Capstone Project
  - Concept 01: Introduction
  - Concept 02: Project Overview
  - Concept 03: Software & Data Requirements
  - Concept 04: Possible Projects
  - Concept 05: Project Survey
  - Concept 06: Bertelsmann/Arvato Project Overview
  - Concept 07: Arvato: Terms and Conditions
  - Concept 08: Bertelsmann/Arvato Project Workspace
  - Concept 09: Starbucks Project Overview
  - Concept 10: Starbucks Project Workspace
  - Concept 11: Dog Breed Classifier Overview
  - Concept 12: Dog Breed Workspace
  - Concept 13: Spark Project Overview
  - Concept 14: Spark Project Workspace
    Part 07 : Congratulations
    Congratulations on your completion of the Data Scientist Nanodegree!
Module 01: Congrats!
- Lesson 01: Congratulations!Congratulations on your completion of the Data Scientist Nanodegree!
  - Concept 01: Congratulations!
    Part 08 (Elective): [Capstone Content] Convolutional Neural Networks
Module 01: Deep Learning
- Lesson 01: Neural NetworksLuis will give you an overview of logistic regression, gradient descent, and the building blocks of neural networks.
- Lesson 02: Deep Neural NetworksA deeper dive into backpropagation and the training process of neural networks, including techniques to improve the training.
- Lesson 03: Convolutional Neural NetworksAlexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.
Module 01: Spark
- Lesson 01: Introduction to the CourseIn this lesson, you will learn more about this course - what will be covered, and who you will be learning from - let’s get started!
- Lesson 02: The Power of SparkIn this lesson, you will learn about the problems that Apache Spark is designed to solve. You’ll also learn about the greater Big Data ecosystem and how Spark fits into it.
- Lesson 03: Data Wrangling with SparkIn this lesson, we’ll dive into how to use Spark for cleaning and aggregating data.
- Lesson 04: Debugging and OptimizationIn this lesson, we will cover various troubleshooting techniques and potential ways of optimizing the performance of your Spark applications.
- Lesson 05: Machine Learning with SparkIn this lesson, we’ll explore Spark’s ML capabilities and build ML models and pipelines.
- Lesson 06: [DSND Capstone] Cloud Deployment InstructionsProject Description - Capstone ProjectProject Rubric - Capstone Project
  - Concept 01: Overview
  - Concept 02: [AWS] Create an account
  - Concept 03: [AWS] Launch EMR Cluster and Notebook
  - Concept 04: [AWS] Starter Code
  - Concept 05: [AWS] Avoid Paying Unexpected Costs
  - Concept 06: [IBM] Create an IBM Watson account and Watson project
  - Concept 07: [IBM] Launch IBM Cluster and Notebook
  - Concept 08: [IBM] Starter Code
  - Concept 09: [IBM] Avoid Paying Unexpected Costs
    Part 10 (Elective): Prerequisite: Python for Data Analysis
Module 01: Python for Data Analysis
- Lesson 01: Why Python ProgrammingWelcome to Introduction to Python! Here’s an overview of the course.
- Lesson 02: Data Types and OperatorsFamiliarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, built-in functions, and style guidelines.
- Lesson 03: Control FlowBuild logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.
- Lesson 04: FunctionsLearn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.
- Lesson 05: ScriptingSetup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.
- Lesson 06: NumPyLearn the basics of NumPy and how to use it to create and manipulate arrays.
- Lesson 07: PandasLearn the basics of Pandas Series and DataFrames and how to use them to load and process data.
Module 01: SQL for Data Analysis
- Lesson 01: Basic SQLIn 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.
- Lesson 02: SQL JoinsIn this lesson, you will learn how to combine data from multiple tables together.
- Lesson 03: SQL AggregationsIn 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.
- Lesson 04: SQL Subqueries & Temporary TablesIn this lesson, you will be learning to answer much more complex business questions using nested querying methods - also known as subqueries.
- Lesson 05: SQL Data CleaningCleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.
- Lesson 06: [Advanced] SQL Window FunctionsCompare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.
- Lesson 07: [Advanced] SQL Advanced JOINs & Performance TuningLearn 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.
Module 01: Data Visualization in Python
- Lesson 01: Data Visualization in Data AnalysisIn this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.
- Lesson 02: Design of VisualizationsLearn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.
- Lesson 03: Univariate Exploration of DataIn this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.
- Lesson 04: Bivariate Exploration of DataIn 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.
- Lesson 05: Multivariate Exploration of DataIn this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.
- Lesson 06: Explanatory VisualizationsPrevious 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!
- Lesson 07: Visualization Case StudyPut to practice the concepts you’ve learned about exploratory and explanatory data visualization in this case study on factors that impact diamond prices.
  - Concept 01: Introduction
  - Concept 02: Info on the Diamond Dataset
  - Concept 03: Univariate Exploration
  - Concept 04: Bivariate Exploration
  - Concept 05: Multivariate Exploration
  - Concept 06: Explanatory Polishing
  - Concept 07: Conclusion
    Part 13 (Elective): Prerequisite: Command Line Essentials
Module 01: Command Line Essentials
- Lesson 01: Shell WorkshopThe Unix shell is a powerful tool for developers of all sorts. In this lesson, you’ll get a quick introduction to the very basics of using it on your own computer.
Module 01: Git and 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 RepoNow 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 HistoryKnowing 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 RepoA 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 MergingBeing 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 ChangesHelp! Disaster has struck! You don’t have to worry, though, because your project is tracked in version control! You’ll learn how to undo and modify changes that have been saved to the repository.
- Lesson 07: Working With RemotesYou’ll learn how to create remote repositories on GitHub and how to get and send changes to the remote repository.
- Lesson 08: Working On Another Developer’s RepositoryIn this lesson, you’ll learn how to fork another developer’s project. Collaborating with other developers can be a tricky process, so you’ll learn how to contribute to a public project.
- Lesson 09: Staying In Sync With A Remote RepositoryYou’ll learn how to send suggested changes to another developer by using pull requests. You’ll also learn how to use the powerful git rebase command to squash commits together.
  - Concept 01: Intro
  - Concept 02: Create a Pull Request
  - Concept 03: Stay in sync with source project
  - Concept 04: Manage an active PR
  - Concept 05: Squash Commits
  - Concept 06: Course Wrap Up
    Part 15 (Elective): Prerequisite: Linear Algebra
Module 01: Linear Algebra
- Lesson 01: IntroductionTake a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.
- Lesson 02: VectorsLearn about vectors, the basic building block of Linear Algebra.
- Lesson 03: Linear CombinationLearn how to scale and add vectors and how to visualize the process.
- Lesson 04: Linear Transformation and Matrices What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.
Module 01: Practical Stats
- Lesson 01: Descriptive Statistics - Part IIn this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
- Lesson 02: Descriptive Statistics - Part IIIn 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.
- Lesson 03: Admissions Case StudyLearn to ask the right questions, as you learn about Simpson’s Paradox.
- Lesson 04: ProbabilityGain the basics of probability using coins and die.
- Lesson 05: Binomial DistributionLearn about one of the most popular distributions in probability - the Binomial Distribution.
- Lesson 06: Conditional ProbabilityNot all events are independent. Learn the probability rules for dependent events.
- Lesson 07: Bayes RuleLearn one of the most popular rules in all of statistics - Bayes rule.
- Lesson 08: Python Probability PracticeTake what you have learned in the last lessons and put it to practice in Python.
- Lesson 09: Normal Distribution TheoryLearn the mathematics behind moving from a coin flip to a normal distribution.
- Lesson 10: Sampling distributions and the Central Limit TheoremLearn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.
- Lesson 11: Confidence IntervalsLearn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.
- Lesson 12: Hypothesis TestingLearn the necessary skills to create and analyze the results in hypothesis testing.
- Lesson 13: Case Study: A/B testsWork through a case study of how A/B testing works for an online education company called Audacity.
- Lesson 14: RegressionUse python to fit linear regression models, as well as understand how to interpret the results of linear models.
- Lesson 15: Multiple Linear RegressionLearn to apply multiple linear regression models in python. Learn to interpret the results and understand if your model fits well.
- Lesson 16: Logistic RegressionLearn to apply logistic regression models in python. Learn to interpret the results and understand if your model fits well.

【点击购买】

数据分析系列-2022

数据科学家 4.0/Data Scientist

Data Scientist Nanodegree

Content

Part 01 : Welcome to the Nanodegree program

Part 02 : Introduction to Data Science

Part 03 : Software Engineering

Part 04 : Data Engineering

Part 05 : Experimental Design & Recommendations

Part 06 : Data Scientist Capstone

Part 07 : Congratulations

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

**Part 09 (Elective):** [Capstone Content] Spark

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

**Part 11 (Elective):** Prerequisite: SQL

**Part 12 (Elective):** Prerequisite: Data Visualization

**Part 13 (Elective):** Prerequisite: Command Line Essentials

**Part 14 (Elective):** Prerequisite: Git & Github

**Part 15 (Elective):** Prerequisite: Linear Algebra

**Part 16 (Elective):** Prerequisite: Practical Statistics

数据科学家 4.0/Data Scientist

Data Scientist Nanodegree

Content

Part 01 : Welcome to the Nanodegree program

Part 02 : Introduction to Data Science

Part 03 : Software Engineering

Part 04 : Data Engineering

Part 05 : Experimental Design & Recommendations

Part 06 : Data Scientist Capstone

Part 07 : Congratulations

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

Part 09 (Elective): [Capstone Content] Spark

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

Part 11 (Elective): Prerequisite: SQL

Part 12 (Elective): Prerequisite: Data Visualization

Part 13 (Elective): Prerequisite: Command Line Essentials

Part 14 (Elective): Prerequisite: Git & Github

Part 15 (Elective): Prerequisite: Linear Algebra

Part 16 (Elective): Prerequisite: Practical Statistics

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

**Part 09 (Elective):** [Capstone Content] Spark

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

**Part 11 (Elective):** Prerequisite: SQL

**Part 12 (Elective):** Prerequisite: Data Visualization

**Part 13 (Elective):** Prerequisite: Command Line Essentials

**Part 14 (Elective):** Prerequisite: Git & Github

**Part 15 (Elective):** Prerequisite: Linear Algebra

**Part 16 (Elective):** Prerequisite: Practical Statistics