About This Specialization Ask the right questions, … · · 2017-12-15Video · Statistical Inference Overview ... Video · Tips from Coursera Users - Optional Video ... Peer Review

About This SpecializationAsk the right questions, manipulate data sets, and create visualizations to communicate results.

This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

10 coursesFollow the suggested order or choose yourown

ProjectsFollow the suggested order or choose yourown

Certi�catesFollow the suggested order or choose yourown

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1-4 hours/week

English, French, Chinese (Simpli�ed), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

About the CourseIn this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Week 1Week 1During Week 1, you'll learn about the goals and objectives of the Data Science Specialization and each of its components. You'll also get an overview of the field as well as instructions on how to install R.

Reading · Welcome to the Data Scientist's Toolbox

Reading · Pre-Course Survey

Reading · Syllabus

Reading · Specialization Textbooks

Video · Specialization Motivation

Reading · The Elements of Data Analytic Style

Video · The Data Scientist's Toolbox

Video · Getting Help

Video · Finding Answers

Video · R Programming Overview

Video · Getting Data Overview

Video · Exploratory Data Analysis Overview

Video · Reproducible Research Overview

Video · Statistical Inference Overview

Video · Regression Models Overview

Video · Practical Machine Learning Overview

Video · Building Data Products Overview

Video · Installing R on Windows {Roger Peng}

Video · Install R on a Mac {Roger Peng}

Video · Installing Rstudio {Roger Peng}

Video · Installing Outside Software on Mac (OS X Mavericks)

Quiz · Week 1 Quizs

Week 2Week 2: Installing the ToolboxThis is the most lecture-intensive week of the course. The primary goal is to get you set up with R, Rstudio, Github, and the other tools we will use throughout the Data Science Specialization and your ongoing work as a data scientist.

Video · Tips from Coursera Users - Optional Video

Video · Command Line Interface

Video · Introduction to Git

Video · Introduction to Github

Video · Creating a Github Repository

Video · Basic Git Commands

Video · Basic Markdown

Video · Installing R Packages

Video · Installing Rtools

Quiz · Week 2 Quiz

Week 3Week 3: Conceptual IssuesThe Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists.

Video · Types of Questions

Video · What is Data?

Video · What About Big Data?

Video · Experimental Design

Quiz · Week 3 Quiz

Week 4Week 4: Course Project Submission & EvaluationIn Week 4, we'll focus on the Course Project. This is your opportunity to install the tools and set up the accounts that you'll need for the rest of the specialization and for work in data science.

Peer Review · Course Project

Reading · Post-Course Survey

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About the CourseIn this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Week 1Week 1: Background, Getting Started, and Nuts & BoltsThis week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.

Reading · Welcome to R Programming

Reading · About the Instructor


Reading · Syllabus

Reading · Course Textbook

Reading · Course Supplement: The Art of Data Science

Reading · Data Science Podcast: Not So Standard Deviations

Video · Installing R on a Mac

Video · Installing R on Windows

Video · Installing R Studio (Mac)

Video · Writing Code / Setting Your Working Directory (Windows)

Video · Writing Code / Setting Your Working Directory (Mac)

Reading · Getting Started and R Nuts and Bolts

Video · Introduction

Video · Overview and History of R

Video · Getting Help

Video · R Console Input and Evaluation

Video · Data Types - R Objects and Attributes

Video · Data Types - Vectors and Lists

Video · Data Types - Matrices

Video · Data Types - Factors

Video · Data Types - Missing Values

Video · Data Types - Data Frames

Video · Data Types - Names Attribute

Video · Data Types - Summary

Video · Reading Tabular Data

Video · Reading Large Tables

Video · Textual Data Formats

Video · Connections: Interfaces to the Outside World

Video · Subsetting - Basics

Video · Subsetting - Lists

Video · Subsetting - Matrices

Video · Subsetting - Partial Matching

Video · Subsetting - Removing Missing Values

Video · Vectorized Operations

Quiz · Week 1 Quiz

Video · Introduction to swirl

Reading · Practical R Exercises in swirl Part 1

Practice Programming Assignment · swirl Lesson 1: Basic Building Blocks

Practice Programming Assignment · swirl Lesson 2: Workspace and Files

Practice Programming Assignment · swirl Lesson 3: Sequences of Numbers

Practice Programming Assignment · swirl Lesson 4: Vectors

Practice Programming Assignment · swirl Lesson 5: Missing Values

Practice Programming Assignment · swirl Lesson 6: Subsetting Vectors

Practice Programming Assignment · swirl Lesson 7: Matrices and Data Frames

Week 2Week 2: Programming with RWelcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.

Reading · Week 2: Programming with R

Video · Control Structures - Introduction

Video · Control Structures - If-else

Video · Control Structures - For loops

Video · Control Structures - While loops

Video · Control Structures - Repeat, Next, Break

Video · Your First R Function

Video · Functions (part 1)

Video · Functions (part 2)

Video · Scoping Rules - Symbol Binding

Video · Scoping Rules - R Scoping Rules

Video · Scoping Rules - Optimization Example (OPTIONAL)

Video · Coding Standards

Video · Dates and Times


Practice Programming Assignment · swirl Lesson 1: Logic

Practice Programming Assignment · swirl Lesson 2: Functions

Practice Programming Assignment · swirl Lesson 3: Dates and Times

Quiz · Week 2 Quiz

Reading · Programming Assignment 1 INSTRUCTIONS: Air Pollution

Quiz · Programming Assignment 1: Quiz

Week 3Week 3: Loop Functions and DebuggingWe have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.

Reading · Week 3: Loop Functions and Debugging

Video · Loop Functions - lapply

Video · Loop Functions - apply

Video · Loop Functions - mapply

Video · Loop Functions - tapply

Video · Loop Functions - split

Video · Debugging Tools - Diagnosing the Problem

Video · Debugging Tools - Basic Tools

Video · Debugging Tools - Using the Tools


Practice Programming Assignment · swirl Lesson 1: lapply and sapply

Practice Programming Assignment · swirl Lesson 2: vapply and tapply

Quiz · Week 3 Quiz

Peer Review · Programming Assignment 2: Lexical Scoping

Week 4Week 4: Simulation & ProfilingThis week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.

Reading · Week 4: Simulation & Profiling

Video · The str Function

Video · Simulation - Generating Random Numbers

Video · Simulation - Simulating a Linear Model

Video · Simulation - Random Sampling

Video · R Profiler (part 1)

Video · R Profiler (part 2)

Quiz · Week 4 Quiz


Practice Programming Assignment · swirl Lesson 1: Looking at Data

Practice Programming Assignment · swrl Lesson 2: Simulation

Practice Programming Assignment · swirl Lesson 3: Base Graphics

Reading · Programming Assignment 3 INSTRUCTIONS: Hospital Quality

Quiz · Programming Assignment 3: Quiz


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About the CourseBefore you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Week 1Week 1In this first week of the course, we look at finding data and reading different file types.

Reading · Welcome to Week 1

Reading · Syllabus


Video · Obtaining Data Motivation

Video · Raw and Processed Data

Video · Components of Tidy Data

Video · Downloading Files

Video · Reading Local Files

Video · Reading Excel Files

Video · Reading XML

Video · Reading JSON

Video · The data.table Package


Quiz · Week 1 Quiz

Week 2Week 2Welcome to Week 2 of Getting and Cleaning Data! The primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.

.Video · Reading from MySQL

Video · Reading from HDF5

Video · Reading from The Web

Video · Reading From APIs

Video · Reading From Other Sources

Quiz · Week 2 Quiz

Week 3Week 3Welcome to Week 3 of Getting and Cleaning Data! This week the lectures will focus on organizing, merging and managing the data you have collected using the lectures from Weeks 1 and 2.

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Video · Subsetting and Sorting

Video · Summarizing Data

Video · Creating New Variables

Video · Reshaping Data

Video · Managing Data Frames with dplyr - Introduction

Video · Managing Data Frames with dplyr - Basic Tools

Video · Merging Data


Practice Programming Assignment · swirl Lesson 1: Manipulating Data with dplyr

Practice Programming Assignment · swirl Lesson 2: Grouping and Chaining with dplyr

Practice Programming Assignment · swirl Lesson 3: Tidying Data with tidyr

Quiz · Week 3 Quiz

Week 4Week 4Welcome to Week 4 of Getting and Cleaning Data! This week we finish up with lectures on text and date manipulation in R. In this final week we will also focus on peer grading of Course Projects.

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Video · Editing Text Variables

Video · Regular Expressions I

Video · Regular Expressions II

Video · Working with Dates

Video · Data Resources


Practice Programming Assignment · swirl Lesson 1: Dates and Times with lubridate

Quiz · Week 4 Quiz

Peer Review · Getting and Cleaning Data Course Project


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About the CourseThis course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Week 1Week 1This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already.

.Reading · Welcome to Exploratory Data Analysis

Reading · Syllabus



Reading · Exploratory Data Analysis with R Book

Reading · The Art of Data Science

Video · Installing R on Windows (3.2.1)

Video · Installing R on a Mac (3.2.1)

Video · Installing R Studio (Mac)

Video · Setting Your Working Directory (Windows)

Video · Setting Your Working Directory (Mac)

Video · Principles of Analytic Graphics

Video · Exploratory Graphs (part 1)

Video · Exploratory Graphs (part 2)

Video · Plotting Systems in R

Video · Base Plotting System (part 1)

Video · Base Plotting System (part 2)

Video · Base Plotting Demonstration

Video · Graphics Devices in R (part 1)

Video · Graphics Devices in R (part 2)


Practice Programming Assignment · swirl Lesson 1: Principles of Analytic Graphs

Practice Programming Assignment · swirl Lesson 2: Exploratory Graphs

Practice Programming Assignment · swirl Lesson 3: Graphics Devices in R

Practice Programming Assignment · swirl Lesson 4: Plotting Systems

Practice Programming Assignment · swirl Lesson 5: Base Plotting System

Quiz · Week 1 Quiz

Peer Review · Course Project 1

Week 2Week 2Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualiz-ing data, it was part of the original R system and lacks many features that may be desirable in a plotting system, particular-ly when visualizing high dimensional data. The Lattice and ggplot2 systems also simplify the laying out of plots making it a much less tedious process..

Video · Lattice Plotting System (part 1)

Video · Lattice Plotting System (part 2)

Video · ggplot2 (part 1)






Practice Programming Assignment · swirl Lesson 1: Lattice Plotting System

Practice Programming Assignment · swirl Lesson 2: Working with Colors

Practice Programming Assignment · swirl Lesson 3: GGPlot2 Part1

Practice Programming Assignment · swirl Lesson 4: GGPlot2 Part2

Practice Programming Assignment · swirl Lesson 5: GGPlot2 Extras

Quiz · Week 2 Quiz)

Week 3Week 3Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for explor-atory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dimensional data (many many variables). We also cover novel ways to specify colors in R so that you can use color as an important and useful dimension when making data graphics. All of this material is covered in chapters 9-12 of my book Exploratory Data Analysis with R..

Video · Hierarchical Clustering (part 1)



Video · K-Means Clustering (part 1)

Video · K-Means Clustering (part 2)

Video · Dimension Reduction (part 1)



Video · Working with Color in R Plots (part 1)





Practice Programming Assignment · swirl Lesson 1: Hierarchical Clustering

Practice Programming Assignment · swirl Lesson 2: K Means Clustering

Practice Programming Assignment · swirl Lesson 3: Dimension Reduction

Practice Programming Assignment · swirl Lesson 4: Clustering Example

Week Week 4This week, we'll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis tech-niques, and the second is a more involved analysis of some air pollution data. How one goes about doing EDA is often personal, but I'm providing these videos to give you a sense of how you might proceed with a specific type of dataset.

Video · Clustering Case Study

Video · Air Pollution Case Study


Practice Programming Assignment · swirl Lesson 1: CaseStudy



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About the CourseThis course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Repro-ducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details report-ed in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

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6 weeks of study, 3-8 hours/week, the week will vary.

Week 1Week 1: Concepts, Ideas, & StructureThis week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.


Reading · Syllabus

Reading · Pre-course survey

Reading · Course Book: Report Writing for Data Science in R

Video · What is Reproducible Research About?

Video · Reproducible Research: Concepts and Ideas (part 1)



Video · Scripting Your Analysis

Video · Structure of a Data Analysis (part 1)

Video · Structure of a Data Analysis (part 2)

Video · Organizing Your Analysis

Quiz · Week 1 Quiz

Week 2Week 2: Markdown & knitrThis week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.

Video · Coding Standards in R

Video · Markdown

Video · R Markdown

Video · R Markdown Demonstration

Video · knitr (part 1)




Quiz · Week 2 Quiz

Video · Introduction to Course Project 1


Week 3Week 3: Reproducible Research Checklist & Evidence-based Data AnalysisThis week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.

Video · Communicating Results

Video · RPubs

Video · Reproducible Research Checklist (part 1)



Video · Evidence-based Data Analysis (part 1)





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Statistical Inference

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1-4 hours/week

English

About the CourseStatistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practi-tioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamen-tals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Week 1Week 1: Probability & Expected ValuesThis week, we'll focus on the fundamentals including probability, random variables, expectations and more.

Video · 04 03 Expected values for PDFs

Reading · Practical R Exercises in swirl 1

Practice Programming Assignment · swirl Lesson 1: Introduction

Practice Programming Assignment · swirl Lesson 2: Probability1

Practice Programming Assignment · swirl Lesson 3: Probability2

Practice Programming Assignment · swirl Lesson 4: ConditionalProbability

Practice Programming Assignment · swirl Lesson 5: Expectations

Quiz · Quiz 1

Video · Introductory video

Reading · Welcome to Statistical Inference

Reading · Some introductory comments


Reading · Syllabus

Reading · Course Book: Statistical Inference for Data Science

Reading · Data Science Specialization Community Site

Reading · Homework Problems

Reading · Probability

Video · 02 01 Introduction to probability

Video · 02 02 Probability mass functions

Video · 02 03 Probability density functions

Reading · Conditional probability

Video · 03 01 Conditional Probability

Video · 03 02 Bayes' rule

Video · 03 03 Independence

Reading · Expected values

Video · 04 01 Expected values

Video · 04 02 Expected values, simple examples

Week 2Week 2: Variability, Distribution, & AsymptoticsWe're going to tackle variability, distributions, limits, and confidence intervals.

Reading · Variability

Video · 05 01 Introduction to variability

Video · 05 02 Variance simulation examples

Video · 05 03 Standard error of the mean

Video · 05 04 Variance data example

Reading · Distributions

Video · 06 01 Binomial distrubtion

Video · 06 02 Normal distribution

Video · 06 03 Poisson

Reading · Asymptotics

Video · 07 01 Asymptotics and LLN

Video · 07 02 Asymptotics and the CLT

Video · 07 03 Asymptotics and confidence intervals


Practice Programming Assignment · swirl Lesson 1: Variance

Practice Programming Assignment · swirl Lesson 2: CommonDistros

Practice Programming Assignment · swirl Lesson 3: Asymptotics

Quiz · Quiz 2

Week 3Week: Intervals, Testing, & PvaluesWe will be taking a look at intervals, testing, and values in this lesson.

Week: Intervals, Testing, & Pvalues

We will be taking a look at intervals, testing, and pvalues in this lesson.

Reading · Confidence intervals

Video · 08 01 T confidence intervals

Video · 08 02 T confidence intervals example

Video · 08 03 Independent group T intervals

Video · 08 04 A note on unequal variance

Reading · Hypothesis testing

Video · 09 01 Hypothesis testing

Video · 09 02 Example of choosing a rejection region

Video · 09 03 T tests

Video · 09 04 Two group testing

Reading · P-values

Video · 10 01 Pvalues

Video · 10 02 Pvalue further examples

Reading · Knitr

Video · Just enough knitr to do the project


Practice Programming Assignment · swirl Lesson 1: T Confidence Intervals

Practice Programming Assignment · swirl Lesson 2: Hypothesis Testing

Practice Programming Assignment · swirl Lesson 3: P Values

Quiz · Quiz 3

Week 4Week 4: Power, Bootstrapping, & Permutation TestsWe will begin looking into power, bootstrapping, and permutation tests.

Reading · Power

Video · 11 01 Power

Video · 11 02 Calculating Power

Video · 11 03 Notes on power

Video · 11 04 T test power

Video · 12 01 Multiple Comparisons

Reading · Resampling

Video · 13 01 Bootstrapping

Video · 13 02 Bootstrapping example

Video · 13 03 Notes on the bootstrap

Video · 13 04 Permutation tests

Quiz · Quiz 4

Peer Review · Statistical Inference Course Project


Practice Programming Assignment · swirl Lesson 1: Power

Practice Programming Assignment · swirl Lesson 2: Multiple Testing

Practice Programming Assignment · swirl Lesson 3: Resampling


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About the CourseLinear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regres-sion model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Week 1Week 1: Least Squares and Linear RegressionThis week, we focus on least squares and linear regression.

Reading · Welcome to Regression Models

Reading · Book: Regression Models for Data Science in R

Reading · Syllabus


Reading · Data Science Specialization Community Site

Reading · Where to get more advanced material

Reading · Regression

Video · Introduction to Regression

Video · Introduction: Basic Least Squares

Reading · Technical details

Video · Technical Details (Skip if you'd like)

Video · Introductory Data Example

Reading · Least squares

Video · Notation and Background

Video · Linear Least Squares

Video · Linear Least Squares Coding Example

Video · Technical Details (Skip if you'd like)

Reading · Regression to the mean

Video · Regression to the Mean


Practice Programming Assignment · swirl Lesson 1: Introduction

Practice Programming Assignment · swirl Lesson 2: Residuals

Practice Programming Assignment · swirl Lesson 3: Least Squares Estimation

Quiz · Quiz 1

Week 2Week 2: Linear Regression & Multivariable RegressionThis week, we will work through the remainder of linear regression and then turn to the first part of multivariable regres-sion.

Reading · *Statistical* linear regression models

Video · Statistical Linear Regression Models

Video · Interpreting Coefficients

Video · Linear Regression for Prediction

Reading · Residuals

Video · Residuals

Video · Residuals, Coding Example

Video · Residual Variance

Reading · Inference in regression

Video · Inference in Regression

Video · Coding Example

Video · Prediction

Reading · Looking ahead to the project

Video · Really, really quick intro to knitr


Practice Programming Assignment · swirl Lesson 1: Residual Variation

Practice Programming Assignment · swirl Lesson 2: Introduction to Multivariable Regression

Practice Programming Assignment · swirl Lesson 3: MultiVar Examples

Quiz · Quiz 2

Week 3Week 3: Multivariable Regression, Residuals, & DiagnosticsThis week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.

Reading · Multivariable regression

Video · Multivariable Regression part I

Video · Multivariable Regression part II

Video · Multivariable Regression Continued

Video · Multivariable Regression Examples part I

Video · Multivariable Regression Examples part II

Video · Multivariable Regression Examples part III

Video · Multivariable Regression Examples part IV

Reading · Adjustment

Video · Adjustment Examples

Reading · Residuals

Video · Residuals and Diagnostics part I

Video · Residuals and Diagnostics part II

Video · Residuals and Diagnostics part III

Reading · Model selection

Video · Model Selection part I

Video · Model Selection part II

Video · Model Selection part III


Practice Programming Assignment · swirl Lesson 1: MultiVar Examples2


Practice Programming Assignment · swirl Lesson 3: Residuals Diagnostics and Variation

Quiz · Quiz 3

Practice Quiz · (OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017

Week 4Week 4: Logistic Regression and Poisson RegressionThis week, we will work on generalized linear models, including binary outcomes and Poisson regression.



Practice Programming Assignment · swirl Lesson 3: Residuals Diagnostics and Variation

Quiz · Quiz 3

Practice Quiz · (OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017

Reading · GLMs

Video · GLMs

Reading · Logistic regression

Video · Logistic Regression part I

Video · Logistic Regression part II

Video · Logistic Regression part III

Reading · Count Data

Video · Poisson Regression part I

Video · Poisson Regression part II

Reading · Mishmash

Video · Hodgepodge


Practice Programming Assignment · swirl Lesson 1: Variance Inflation Factors

Practice Programming Assignment · swirl Lesson 2: Overfitting and Underfitting

Practice Programming Assignment · swirl Lesson 3: Binary Outcomes

Practice Programming Assignment · swirl Lesson 4: Count Outcomes

Quiz · Quiz 4

Peer Review · Regression Models Course Project


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Practical Machine Learning

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About the CourseOne of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical appli-cations. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classifi-cation trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Week 1Week 1: Prediction, Errors, and Cross ValidationThis week will cover prediction, relative importance of steps, errors, and cross validation

Reading · Welcome to Practical Machine Learning

Reading · Syllabus


Video · Prediction motivation

Video · What is prediction?

Video · Relative importance of steps

Video · In and out of sample errors

Video · Prediction study design

Video · Types of errors

Video · Receiver Operating Characteristic

Video · Cross validation

Video · What data should you use?

Quiz · Quiz 1

Week 2 Week 2: The Caret PackageThis week will introduce the caret package, tools for creating features and preprocessing

Video · Caret package

Video · Data slicing

Video · Training options

Video · Plotting predictors

Video · Basic preprocessing

Video · Covariate creation

Video · Preprocessing with principal components analysis

Video · Predicting with Regression

Video · Predicting with Regression Multiple Covariates

Quiz · Quiz 2

Week 3Week 3: Predicting with trees, Random Forests, & Model Based PredictionsThis week we introduce a number of machine learning algorithms you can use to complete your course project.

Video · Predicting with trees

Video · Bagging

Video · Random Forests

Video · Boosting

Video · Model Based Prediction

Quiz · Quiz 3

Week 4Week 4: Regularized Regression and Combining PredictorsThis week, we will cover regularized regression and combining predictors.

Video · Regularized regression

Video · Combining predictors

Video · Forecasting

Video · Unsupervised Prediction

Quiz · Quiz 4

Reading · Course Project Instructions (READ FIRST)

Peer Review · Prediction Assignment Writeup

Quiz · Course Project Prediction Quiz


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Developing Data Products

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About the CourseA data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

Week 1Course OverviewIn this overview module, we'll go over some information and resources to help you get started and succeed in the course.

Shiny, GoogleVis, and PlotlyNow we can turn to the first substantive lessons. In this module, you'll learn how to develop basic applications and interac-tive graphics in shiny, compose interactive HTML graphics with GoogleVis, and prepare data visualizations with Plotly.

Video · Welcome to Developing Data ProductsT

Reading · Syllabus

Reading · Welcome

Reading · Book: Developing Data Products in R

Reading · Community Site

Reading · R and RStudio Links & Tutorials

Reading · Shiny

Reading · Shinyapps.io Project

Video · Shiny 1.1

Video · Shiny 1.2

Video · Shiny 1.3

Video · Shiny 1.4

Video · Shiny 1.5

Video · Shiny 2.1

Video · Shiny 2.2

Video · Shiny 2.3

Video · Shiny 2.4

Video · Plotly 1.4

Video · Plotly 1.5

Video · Plotly 1.6

Video · Plotly 1.7

Video · Plotly 1.8

Quiz · Quiz 1

Video · Shiny 2.5

Video · Shiny 2.6

Video · Shiny Gadgets 1.1



Video · GoogleVis 1.1

Video · GoogleVis 1.2

Video · Plotly 1.1

Video · Plotly 1.2

Video · Plotly 1.3

Week 2Course OverviewR Markdown and LeafletDuring this module, we'll learn how to create R Markdown files and embed R code in an Rmd. We'll also explore Leaflet and use it to create interactive annotated maps.

Video · R Markdown 1.1






Reading · Three Ways to Share R Markdown Products

Video · Leaflet 1.1






Quiz · Quiz 2

Peer Review · R Markdown and Leaflet

Week 3R PackagesIn this module, we'll dive into the world of creating R packages and practice developing an R Markdown presentation that includes a data visualization built using Plotly.

Reading · R Packages

Video · R Packages (Part 1)

Video · R Packages (Part 2)

Video · Building R Packages Demo

Video · R Classes and Methods (Part 1)

Video · R Classes and Methods (Part 2)

Quiz · Quiz 3

Peer Review · R Markdown Presentation & Plotly

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Data Science Capstone

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4-9 hours/week

About the Capstone ProjectThe capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.

Week 1Overview, Understanding the Problem, and Getting the DataThis week, we introduce the project so you can get a clear grip on the problem at hand and begin working with the dataset.

Video · Welcome to the Capstone Project

Reading · Project Overview

Video · Welcome from SwiftKey

Video · You Are a Data Scientist Now

Reading · Syllabus

Video · Introduction to Task 0: Understanding the Problem

Reading · Task 0 - Understanding the problem

Reading · About the Copora

Video · Introduction to Task 1: Getting and Cleaning the Data

Reading · Task 1 - Getting and cleaning the data

Video · Regular Expressions: Part 1 (Optional)

Video · Regular Expressions: Part 2 (Optional)

Quiz · Quiz 1: Getting Started

Week 2Exploratory Data Analysis and ModelingThis week, we move on to the next tasks, exploratory data analysis and modeling. You'll also submit your milestone report and review submissions from your classmates.

Video · Introduction to Task 2: Exploratory Data Analysis

Reading · Task 2 - Exploratory Data Analysis

Video · Introduction to Task 3: Modeling

Reading · Task 3 - Modeling

Peer Review · Milestone Report

Week 3Prediction ModelThis week, you'll build and evaluate your prediction model. The goal is to make your model efficient and accurate.

Video · Introduction to Task 4: Prediction Model

Reading · Task 4 - Prediction Model

Quiz · Quiz 2: Natural language processing I

Week 4Creative ExplorationThis week's goal is to improve the predictive accuracy while reducing computational runtime and model complexity.

Video · Introduction to Task 5: Creative Exploration

Reading · Task 5 - Creative Exploration

Quiz · Quiz 3: Natural language processing II

Week 5Creative ExplorationThis week's goal is to improve the predictive accuracy while reducing computational runtime and model complexity.

Video · Introduction to Task 6: Data Product

Reading · Task 6 - Data Product

Week 6Slide DeckThis week, you'll work on developing the second component of your final project, a slide deck to accompany your data product.

Video · Introduction to Task 7: Slide Deck

Reading · Task 7 - Slide Deck

Week 7Final Project Submission and EvaluationThis week, you'll submit your final project and review the work of your classmates.

Peer Review · Final Project Submission

Video · Congratulations!

Documents

About This Specialization Ask the right questions, … · · 2017-12-15Video · Statistical Inference Overview ... Video · Tips from Coursera Users - Optional Video ... Peer Review