Data Analyst Nanodegree v13.0.0

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Learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate your findings.

Content

Part 01 : Introduction to Python

Learn Python programming fundamentals such as data types and structures, variables, loops, and functions.

Module 01: Old Lessons
- Lesson 01: Numbers and Strings
  In this lesson you will learn how to use Python's numeric and string data types. You will use built-in functions and methods to process this data, and store the results in variables
- Lesson 02: Functions, Installation and Conditionals
  In this lesson you will install Python on your own computer. You will learn how to define functions, and how to use conditional statements to write more elaborate programs. We will also practice our software engineering skills by learning how to break programs down into manageable pieces.
- Lesson 03: Data Structures and Loops
  In this lesson you will learn how to use Python's collections: lists, sets and dictionaries. You will learn how to iterate over these collections with for loops and while loops. You will also learn how to build compound data structures that combine these collections. We will practice the software engineering skills of refactoring and self-reliant problem solving.
- Lesson 04: Files and Modules
  In this lesson you will extend your knowledge of functions by learning how to specify default arguments and how to return multiple value from a function. You will learn how to read from files. You will also learn how to import modules from Python's standard library, and how to install third-party libraries. We will also learn more about reliant problem solving.
- Lesson 05: Wikipedia Web Crawl Case Study
  In this lesson we will apply the skills learned previously by writing a web crawler that explores Wikipedia.
Module 02: Project
- Lesson 01: Python Project
  Project Description - Explore US Bikeshare Data
  
  Project Rubric - Explore US Bikeshare Data

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: Anaconda
  
  In this lesson, you will be getting a quick glimpse at the Anaconda environment - one of the most popular environments for doing data analysis in Python.
- Lesson 02: Jupyter Notebooks
  
  Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data viz!
Module 02: Python for Data Analysis
- 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.
- 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.
- 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.
- 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.
Module 03: Investigate a Dataset
- Lesson 01: Investigate a Dataset
  
  Choose one of Udacity's curated datasets, perform an investigation, and share your findings.
  
  Project Description - Investigate a Dataset
  
  Project Rubric - Investigate a Dataset

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.

Part 04 : Exploratory Data Analysis

Learn to explore data at multiple levels using appropriate visualizations, acquire statistical knowledge for summarizing data, and develop intuition around a data set.

Module 01: Data Analysis with R
- Lesson 01: What is EDA?
  Learn about what exploratory data analysis (EDA) is and why it is important.
- Lesson 02: R Basics
  Install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.
- Lesson 03: Explore One Variable
  Learn how to quantify and visualize individual variables within a data set using histograms, boxplots, and transforms.
- Lesson 04: Problem Set: Explore One Variable
  Practice using R functions and univariate visualizations to explore and understand individual variables.
- Lesson 05: Explore Two Variables
  Learn techniques for exploring the relationship between any two variables in a data set, including scatter plots, line plots, and correlations.
- Lesson 06: Problem Set: Explore Two Variables
  Practice using bivariate exploration techniques to explore relationships between variables.
- Lesson 07: Explore Many Variables
  Learn powerful methods for examining relationships among multiple variables, and find out how to reshape your data.
- Lesson 08: Problem Set: Explore Many Variables
  Practice using multivariate exploration techniques to look at more complicated relationships between multiple variables.
- Lesson 09: Diamonds & Price Predictions
  Put it all together in this case study where we investigate the diamonds data set alongside Facebook Data Scientist, Solomon Messing.
Module 02: Project
- Lesson 01: Explore and Summarize Data
  Choose one of Udacity's curated datasets or find one of your own and perform a complete exploratory data analysis on the data using R.
  
  Project Description - Explore and Summarize Data
  
  Project Rubric - Explore and Summarize Data

Part 05 : Data Story Telling

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 Viz Lessons 2
- Lesson 01: Introduction to Data Visualization
  In this lesson, you will learn the main data visualizations used for univariate and bivariate analyses. As well as the visualizations that are used for when you you want to compare more variables.
- Lesson 02: Design
  In this lesson, you will learn about visual encodings, and best practices for building data visualizations.
- Lesson 03: Data Visualizations in Tableau
  In this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!
- Lesson 04: Making Dashboards & Stories in Tableau
  In this lesson, you will learn from a Tableau expert, and start putting together your own dashboards and stories.
Module 02: Project
- Lesson 01: Create a Tableau Story
  Create a Tableau data visualization from a data set that tells a story or highlights trends or patterns in the data. Your work should be a reflection of the theory and practice of data visualization.
  
  Project Description - Create a Tableau Story
  
  Project Rubric - Create a Tableau Story

Part 06 : 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 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.
Module 02: Gathering Data
- 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.
Module 03: Assessing Data
- 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.
Module 04: Cleaning Data
- 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.
Module 06: Project
- 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.
  
  Project Description - Wrangle and Analyze Data
  
  Project Rubric - Wrangle and Analyze Data
Module 06: Career Services: LinkedIn Profile
- 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.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile

Part 07 : 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 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.
- Lesson 02: Design of Visualizations
  
  Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.
- 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.
- 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.
- 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.
- 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!
- 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.
Module 02: Communicate Data Findings
- 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.
  
  Project Description - Communicate Data Findings
  
  Project Rubric - Communicate Data Findings

Content

Part 01 : Introduction to Python

Part 02 : Introduction to Data Analysis

Part 03 : Practical Statistics

Part 04 : Exploratory Data Analysis

Part 05 : Data Story Telling

Part 06 : Data Wrangling

Part 07 : Data Visualization

Part 08 : Congratulations and Next Steps

Part 09 (Elective) : Intro to Machine Learning

Part 10 (Elective) : Matrix Math and NumPy Refresher

Part 11 (Elective) : Prerequisite: SQL

更多新课+数据代码+wx：udacity6

Part 12 (Elective) : Prerequisite: Python

Part 13 (Elective) : Prerequisite: Git & GitHub

Part 14 (Elective): Matrix Math and Numpy Refresher

**Part 09 (Elective) :** Intro to Machine Learning

**Part 10 (Elective) :** Matrix Math and NumPy Refresher

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

**Part 12 (Elective) :** Prerequisite: Python

**Part 13 (Elective) :** Prerequisite: Git & GitHub

**Part 14 (Elective):** Matrix Math and Numpy Refresher