Calculations are a common task for data analysts. In this part of the course, you’ll explore formulas, functions, and pivot tables in spreadsheets and queries in SQL, all of which will help with your calculations. You’ll also learn about the benefits of using SQL to manage temporary tables. Learning Objectives Functions with multiple conditions Conditional functions and formulas perform calculations…
When we clean data, you make changes to the original dataset. It’s important to verify the changes we make are accurate and to let your teammates know about the changes. In this part of the course, we’ll learn to verify that data is clean and report our data cleaning results. With verified clean data, we are ready to begin analysing!…
Knowing a variety of ways to clean data can make a data analyst’s job much easier. Learning Objectives How a junior data analyst uses SQL In this reading, you will learn more about how to decide when to use SQL, or Structured Query Language. As a data analyst, you will be tasked with handling a lot of data, and SQL…
What is dirty data? Earlier, we discussed that dirty data is data that is incomplete, incorrect, or irrelevant to the problem you are trying to solve. This section summarizes: Types of dirty data Duplicate data Description Possible causes Potential harm to businesses Any data record that shows up more than once Manual data entry, batch data imports, or data migration…
Scenario: calendar dates for a global company Calendar dates are represented in a lot of different short forms. Depending on where you live, a different format might be used. Now, think about what would happen if you were working as a data analyst for a global company and didn’t check date formats. Well, your data integrity would probably be questionable.…
This course is the fourth in the Google Data Analytics Certificate program. It will teach you how to clean data using spreadsheets and SQL, as well as how to verify and report your data cleaning results. This is an important skill for data analysts, as it ensures that the data they are working with is accurate and reliable. Here are…
File organisation guidelines Every data analyst’s goal is to conduct efficient data analysis. One way to increase the efficiency of your analyses is to streamline processes that help save time and energy in the long run. Meaningful, logical, and consistent file names help data analysts organise their data and automate their analysis process. When you use consistent guidelines to describe…
Maximise databases in data analytics Databases enable analysts to manipulate, store, and process data. This helps them search through data a lot more efficiently to get the best insights. Relational databases A relational database is a database that contains a series of tables that can be connected to form relationships. Basically, they allow data analysts to organise and link data…
Data Responsibility Rundown Key Learnings: Specific Topics Covered: Data anonymization What is data anonymization? We have been learning about the importance of privacy in data analytics. Now, it is time to talk about data anonymization and what types of data should be anonymized. Personally identifiable information, or PII, is information that can be used by itself or with other data to…
Select the right data Following are some data-collection considerations to keep in mind for your analysis: How the data will be collected Decide if you will collect the data using your own resources or receive (and possibly purchase it) from another party. Data that you collect yourself is called first-party data. Data sources If you don’t collect the data using…