You will need to join or merge two or more data sets at different points in your work as a data enthusiast. The dplyr package offers very sophisticated functions to help you achieve the join operation you desire. This project-based course, "Joining Data in R using dplyr" is for R users willing to advance their knowledge and skills. In this course, you will learn practical ways for data manipulation in R. We will talk about different join operations and spend a great deal of our time here joining the sales and customers data sets using the dplyr package. By the end of this 2-hour-long project, you will perform inner join, full (outer) join, right join, left join, cross join, semi join, and anti join using the merge() and dplyr functions. This project-based course is an intermediate-level course in R. Therefore, to get the most of this project, it is essential to have prior experience using R for basic analysis. I recommend that you complete the project titled: "Data Manipulation with dplyr in R" before you take this current project.

This is a self-paced lab that takes place in the Google Cloud console. The Cloud Speech API lets you do speech to text transcription from audio files in over 80 languages. In this hands-on lab you’ll record your own audio file and send it to the Speech API for transcription.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will build upon a previous flow and learn some advanced tactics for preparing data.

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

This is a Google Cloud Self-Paced Lab. Learn basic Google Workspace administration tasks - personalize your Admin Console, update the company profile, load users in single or batch mode.

By the end of this project, you will be able to create a simple Gantt Chart using Google Sheets. You will have a better understanding of how to use this tool to help track and present project schedules. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

By the end of this project you will have used LibreOffice to create a variety of reports for displaying data that originates in relational tables. A report provides a way for users to view only the data they need, and in the format that makes data analysis most effective. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this project you will use MySQL Workbench to write SQL queries that retrieve, sort, and filter data from tables in a relational database. Adding filtering to a query ensures that only the data needed is displayed in the query result. Sorting is applied to arrange the rows in that query result into an order that is meaningful to the data’s user. Adding code to an SQL query to filter and sort data generates a query result that makes data analysis easier for users, enabling more effective decision-making. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. CFT Scorecard is a utility you can combine with Forseti policies to check for violations in your Google Cloud environment.

In this lab you will schedule a Cloud Function to identify and clean up unused IP addresses.