Data Analyst Nanodegree
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Data Analyst Nanodegree

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udacity公众号

Nanodegree key: nd002
Version: 12.0.0
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.

Module 01: Orientation
- 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.
- Lesson 02: Knowledge, Community, and CareersYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 03: Get Help with Your AccountWhat to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 04: The Life of a Data AnalystIn this lesson, you’ll hear from a few data analysts and data scientists about what it’s like to work in data analytics.
Module 02: Intro Project
- Lesson 01: Explore Weather TrendsIn this project, you will analyze local and global temperature data and compare the temperature trends where you live to overall global temperature trends. Project Description - Explore Weather TrendsProject Rubric - Explore Weather Trends
  - Concept 01: Your First Project
  - Concept 02: Project Instructions
  - Concept 03: Accessing Data With SQL
  - Concept 04: Moving Averages
    Part 02 : Introduction to Data Analysis
    Learn the data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. Learn how to work with data in Python using libraries like NumPy and Pandas.
Module 01: Anaconda
- Lesson 01: AnacondaIn this lesson, you will be getting a quick glimpse at the Anaconda environment - one of the most popular environments for doing data analysis in Python.
- Lesson 02: Jupyter NotebooksJupyter 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!
Module 02: Python for Data Analysis
- Lesson 01: The Data Analysis ProcessLearn about the data analysis process and practice investigating different datasets using Python and its powerful packages for data analysis.
- Lesson 02: Data Analysis Process - Case Study 1Investigate 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.
- Lesson 03: Data Analysis Process - Case Study 2Investigate 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.
- Lesson 04: Programming Workflow for Data AnalysisAdditional 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.
Module 03: Investigate a Dataset
- Lesson 01: Investigate a DatasetChoose one of Udacity’s curated datasets, perform an investigation, and share your findings.Project Description - Investigate a DatasetProject Rubric - Investigate a Dataset
  - Concept 01: Project Overview
  - Concept 02: Project Details
  - Concept 03: Completing and Submitting this Project - Two Options
  - Concept 04: Project Workspace: Complete and Submit Project
  - Concept 05: Project Walkthrough
  - Concept 06: Project Cheet Sheet
    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.
Module 01: Practical Statistics
- 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.
Module 02: Analyze A/B Test Results
- Lesson 01: Analyze A/B Test ResultsYou 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.Project Description - Analyze A/B Test ResultsProject Rubric - Analyze A/B Test Results
Module 03: Career Services: GitHub Profile
- 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 04 : Data Wrangling
    Learn the data wrangling process of gathering, assessing, and cleaning data. Learn how to use Python to wrangle data programmatically and prepare it for deeper analysis.
Module 01: Introduction to Data Wrangling
- Lesson 01: Introduction to Data WranglingIdentify 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.
Module 02: Gathering Data
- Lesson 01: Gathering DataGather 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.
Module 03: Assessing Data
- Lesson 01: Assessing DataAssess 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.
Module 04: Cleaning Data
- Lesson 01: Cleaning DataUsing 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.
Module 05: Project
- Lesson 01: Wrangle and Analyze DataGather 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.Project Description - Wrangle and Analyze DataProject Rubric - Wrangle and Analyze Data
Module 06: Career Services: LinkedIn Profile
- 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 : 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
- 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.
Module 02: Communicate Data Findings
- Lesson 01: Communicate Data FindingsChoose 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.Project Description - Communicate Data FindingsProject Rubric - Communicate Data Findings
  - Concept 01: Project Overview
  - Concept 02: Project Details
  - Concept 03: Project Templates and Example Project
    Part 06 : Congratulations and Next Steps
Module 01: Congratulations
- Lesson 01: Congratulations & Next StepsCongratulations on completing all your projects!
  - Concept 01: Congratulations!
  - Concept 02: Next Steps
  - Concept 03: Projects
  - Concept 04: What’s Next in Your Journey!
    Part 07 (Elective): Intro to Machine Learning
Module 01: Intro to Machine Learning
- Lesson 01: Welcome to Machine LearningMeet with Sebastian and Katie to discuss machine learning.
- Lesson 02: Naive BayesLearn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.
- Lesson 03: SVMBuild an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.
- Lesson 04: Decision TreesLearn about how the decision tree algorithm works, including the concepts of entropy and information gain.
- Lesson 05: Choose Your Own AlgorithmIn 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.
- Lesson 06: Datasets and QuestionsFind out about the Enron data set used in the next lessons and mini-projects.
- Lesson 07: RegressionsSee how we can model continuous data using linear regression.
- Lesson 08: OutliersSebastian discusses outlier detection and removal.
- Lesson 09: ClusteringLearn about what unsupervised learning is and find out how to use scikit-learn’s k-means algorithm.
- Lesson 10: Feature ScalingLearn about feature rescaling and find out which algorithms require feature rescaling before use.
- Lesson 11: Text LearningFind out how to use text data in your machine learning algorithm.
- Lesson 12: Feature SelectionKatie discusses when and why to use feature selection, and provides some methods for doing this.
- Lesson 13: PCALearn about data dimensionality and reducing the number of dimensions with principal component analysis (PCA).
- Lesson 14: ValidationLearn more about testing, training, cross validation, and parameter grid searches in this lesson.
- Lesson 15: Evaluation MetricsHow do we know if our classifier is performing well? Katie discusses different evaluation metrics for classifiers in this lesson.
- Lesson 16: Tying It All TogetherSpend some time reflecting on the course material with Sebastian and Katie!
  - Concept 01: Introduction
  - Concept 02: Summary
  - Concept 03: End of Content
  - Concept 04: Outro
    Part 08 (Elective): Matrix Math and NumPy Refresher
Module 01: Matrix Math and Numpy Refresher
- Lesson 01: Matrix Math and NumPy RefresherIn 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.
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: 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: 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: Python
- Lesson 01: Why Python ProgrammingWelcome to Introduction to Python! Here’s an overview of the course.
  - Concept 01: Welcome to the Course!
  - Concept 02: Programming in Python
- Lesson 02: Data Types and OperatorsFamiliarize yourself with the building blocks of Python! Learn about data types and operators, built-in functions, type conversion, whitespace, and style guidelines.
- Lesson 03: Data StructuresUse 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.
- Lesson 04: 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 05: FunctionsLearn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.
- Lesson 06: 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 07: NumPyLearn the basics of NumPy and how to use it to create and manipulate arrays.
- Lesson 08: PandasLearn the basics of Pandas Series and DataFrames and how to use them to load and process data.
Module 01: Version Control with Git
- 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.

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数据分析系列-2022

数据分析师 12.0

Data Analyst Nanodegree

Content

Part 01 : Welcome to the Nanodegree program!

Part 02 : Introduction to Data Analysis

Part 03 : Practical Statistics

Part 04 : Data Wrangling

Part 05 : Data Visualization

Part 06 : Congratulations and Next Steps

**Part 07 (Elective):** Intro to Machine Learning

**Part 08 (Elective):** Matrix Math and NumPy Refresher

**Part 09 (Elective):** Prerequisite: SQL

**Part 10 (Elective):** Prerequisite: Python

**Part 11 (Elective):** Prerequisite: Git & GitHub

数据分析师 12.0

Data Analyst Nanodegree

Content

Part 01 : Welcome to the Nanodegree program!

Part 02 : Introduction to Data Analysis

Part 03 : Practical Statistics

Part 04 : Data Wrangling

Part 05 : Data Visualization

Part 06 : Congratulations and Next Steps

Part 07 (Elective): Intro to Machine Learning

Part 08 (Elective): Matrix Math and NumPy Refresher

Part 09 (Elective): Prerequisite: SQL

Part 10 (Elective): Prerequisite: Python

Part 11 (Elective): Prerequisite: Git & GitHub

**Part 07 (Elective):** Intro to Machine Learning

**Part 08 (Elective):** Matrix Math and NumPy Refresher

**Part 09 (Elective):** Prerequisite: SQL

**Part 10 (Elective):** Prerequisite: Python

**Part 11 (Elective):** Prerequisite: Git & GitHub