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, the skills learned are applied by building a data product using real-world data.
More information about the specialization and the content of each module can be found at Coursera.
Specialization Assignments
| Module | Description | Assignments |
|---|---|---|
|
Getting And Cleaning Data February 2015 |
Prepare a tidy dataset starting from the "Human Activity Recognition Using Smartphone" dataset, with the required supporting materials that can be used for later analysis by a statistician. [R & R ecosystem] | Assignment |
|
Exploratory Analysis March 2015 |
Apply visualization techniques for exploring and summarizing data from two two different datasets. Using visualization to answer questions about possible hypothesis on the data. [R & R ecosystem] |
Assignment 1 Assignment 2 |
|
Reproducible Research October 2015 |
Use the concepts and tools behind reporting modern data analyses in a reproducible manner to perform some analysis on two different real datasets. [R & R ecosystem] |
Assignment 1 Assignment 2 |
|
Statistical Inference November 2015 |
Use the concepts/ tools of Statistical inference in order to draw conclusions about populations or scientific truths from data. [R & R ecosystem] | Assignments |
|
Regression Models December 2015 |
Use regression models to understand if automatic cars are better for fuel consumption using the `mpg` dataset. [R & R ecosystem] | Assignment |
|
Practical Machine Learning February 2016 |
Supervided learning, use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants to predict the manner in which they did the exercise. [R & R ecosystem] | Assignment |
|
Developing Data Products February 2016 |
Create a data product available on the web and a pitch presentation. [R & R ecosystem] |
Product Presentation Code |
|
Capstone Project June 2016 |
Create a text prediction application so that when someone types “I went to the”, the application should presents three options for what the next word might be. For example, the three words might be gym, store, restaurant. Language model should be created using the HC corpora. [R & R ecosystem] |
Artifacts Code |