Introduction
Data is everywhere, but it’s not always ready for analysis. Data preparation is the first step in any analytics project, and it can be challenging if you don’t know what you’re doing. This guide will show you how to prepare your data so that it can be analyzed easily.
Understand the data
- Get to know the data
- Understand the data’s structure and history
- Understand its quality, limitations, and biases
Normalize the data
Normalize the data.
First, normalize the data by removing or adjusting values that are not useful. For example, if you have a column of integers representing the number of hours worked by each employee in one week, then it might make sense to convert these values into percentages so that they can be easily compared with one another. In this case you would divide each integer by 7 (the number of days in a week) and then multiply this result by 100 for an easy-to-read percentage value. You could also use decimals instead if you wanted more precision but less readability; however this may result in some rounding errors when calculating averages later on if there are many decimal places involved in your calculations!
Next comes recoding your variables into standard scales such as integers from 0-10 or 1-5 where appropriate depending on what kind of analysis you want performed later on – this step is especially important when dealing with ordinal categories like gender which could take any value between 0 (male) through 4 (female). It might seem counterintuitive at first but doing this allows us make comparisons among our groups more easily because there will always be only two options available: “0” represents one group while “1+2+3+4 = 5″ represents another group.”
Merge and join your datasets
Merge and join your datasets!
Merging data from multiple sources can be useful for a variety of reasons. For example, if you want to combine the customers and orders tables in the AdventureWorks database into one table that includes all of their information, you could use a merge join. To do this, first create the new table that will contain all customer information:
CREATE TABLE [dbo].[CustomerInfo]([CustomerID] [int] NOT NULL PRIMARY KEY CLUSTERED ,[FirstName] [varchar](50) NULL ,[LastName] [varchar](50) NULL ) ON [PRIMARY]; GO INSERT INTO CustomerInfo ([CustomerID],[FirstName],[LastName]) SELECT LTRIM([LastName]),LTRIM([FirstName]) FROM Person.[Person].Contact WHERE [Gender]= ‘M’ OR Gender = ‘F’; GO Then run a query against both tables: MERGE INTO AdventureWorksDW2008R2..AdventureWorks2008R2 .dbo .SalesOrderHeader AS soh WITH AdventureWorksDW2008R2..AdventureWorks2008R2 .dbo .CustTable AS cust ON soh.[CustomerID] = cust.[CustomerID] WHEN MATCHED THEN UPDATE SET soh.[AddressLine1],soh.[City],soh.[StateProvinceID],soh.[PostalCode],[ExtendedAmount],soh.[OrderDate],[DueDate],[ShipDate],[StatusCode],soh.[TotalDueForAllOrders],soh.[UnitPrice],soh.[DiscountAmount],[FreightAmt][FreightTaxAmt]; GO
Calculate aggregations and transformations
Aggregations and transformations are mathematical operations that can be performed on data. The most common aggregations include average, sum, count and others. Transformations include normalization (e.g., z-scores) or filtering out rows based on certain conditions.
When calculating aggregations for different types of data, you need to use different functions depending on whether your column is numeric or categorical (string). For example if we have a table named “players” with columns “name”, “position”, “age”, etc., we could calculate an average age by using this SQL statement:
SELECT AVG(player_age) FROM players;
Add indexes to your datasets
Add indexes to your datasets
Indexes are useful for filtering and sorting data. They can be added to any field, but they’re especially helpful if you have a lot of columns or if you frequently query your data by one or more fields.
For example, say you have a table containing employee information: name, salary, number of years employed with company, etc. Adding an index on the “years employed with company” field would allow queries like this one: SELECT * FROM employee WHERE years_employed > 5 ORDER BY salary DESC LIMIT 0 , 10
Data preparation is an important first step in any analytics project.
Data preparation is an important first step in any analytics project. It can be time consuming and tedious, but it’s necessary to make sure that your data is ready to use. In this article, we’ll explore some of the most common problems people encounter when preparing their data for analysis, and how you can address them!
What is Data Preparation?
Data preparation refers to all of the steps required before you can start analyzing your data using tools like Tableau or RStudio (or whatever tool works best for your team). This includes: cleaning up dirty records; transforming columns into something more useful; masking sensitive information; aggregating large tables so they’re easier on memory resources when loading them into a visualization tool like Tableau or Power BI Pro (more on this later).
Why Should You Care About Preparing Your Data?
Conclusion
Data preparation is an important first step in any analytics project. By taking the time to properly prepare your data, you’ll make sure that you have a solid foundation for your analysis and can avoid common pitfalls when working with it.
