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Course

Cleaning Data in SQL Server Databases

IntermediateSkill Level

4.8+

Updated 04/2022

Develop the skills you need to clean raw data and transform it into accurate insights.

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SQLData Preparation

4 hr

13 videos

48 Exercises

3,750 XP

11,622

Statement of Accomplishment

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Course Description

Did you know that data scientists and data analysts spend a large amount of time cleaning data before they can analyze it? This is because real-world data is messy. To help you navigate messy data this course teaches you how to clean data stored in an SQL Server database. You’ll learn how to solve common problems such as how to clean messy strings, deal with empty values, compare the similarity between strings, and much more. You’ll get hands-on with all these tasks using a wide range of interesting and messy datasets, including monthly airline flights by airport, TV series and paper shop sales. Are you ready to get your hands messy?

Prerequisites

Intermediate SQL Server

1

Starting with Cleaning Data

To begin the course, you will learn why cleaning data is important. You will solve simple problems such as leading and trailing spaces in strings, unifying formats for flight registrations, combining strings and more.

Introduction to Cleaning Data

Unifying flight formats I

Unifying flight formats II

Cleaning messy strings

Trimming strings I

Trimming strings II

Unifying strings

Comparing the similarity between strings

SOUNDEX() and DIFFERENCE()

Comparing names with SOUNDEX()

Comparing names with DIFFERENCE()

2

Dealing with missing data, duplicate data, and different date formats

In this chapter, you will deepen your data cleaning knowledge. You will learn how to deal with missing data, avoid duplicate data in your datasets, and work with different formats of dates.

Dealing with missing data

Removing missing values

Removing blank spaces

Filling missing values using ISNULL()

Filling missing values using COALESCE()

Avoiding duplicate data

Diagnosing duplicates

Treating duplicates

Dealing with different date formats

Using CONVERT()

Using FORMAT()

CONVERT() vs FORMAT()

3

Dealing with out of range values, different data types, and pattern matching

In this chapter, you will deal with out of range values and inaccurate data. You will also practice converting data with different types. Finally, you will work on matching patterns to your data to find outliers.

Out of range values and inaccurate data

Out of range values or inaccurate data?

Detecting out of range values

Excluding out of range values

Detecting and excluding inaccurate data

Converting data with different types

Using CAST() and CONVERT()

The series with most episodes

Pattern matching

Characters to specify a patterns

Matching urls

Checking phone numbers

4

Combining, splitting, and transforming data

In this final chapter, you will learn how to combine and aggregate data of some columns into one, split data of one column into more columns, and transform rows into columns and vice versa.

Combining data of some columns into one column

Combining cities and states using +

Concatenating cities and states

Working with DATEFROMPARTS()

Splitting data of one column into more columns

Using SUBSTRING() and CHARINDEX()

Using RIGHT() , LEFT() and REVERSE()

SUBSTRING() or CHARINDEX()?

Transforming rows into columns and vice versa

PIVOT or UNPIVOT?

Turning rows into columns

Turning columns into rows

Congratulations!

Cleaning Data in SQL Server Databases

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FAQs

What types of messy data problems does this course address?

You learn to fix leading and trailing spaces, unify string formats, handle missing values, remove duplicates, correct out-of-range values, convert data types, and apply pattern matching.

What datasets are used for practice?

You work with several interesting datasets including monthly airline flights by airport, TV series data, and paper shop sales records.

Is this course specific to SQL Server or does it apply to other databases?

The syntax and functions taught are specific to SQL Server. However, the data cleaning concepts and strategies transfer to other SQL dialects with minor adjustments.

Does this course teach how to transform rows into columns?

Yes. The final chapter covers pivoting rows to columns and vice versa, along with combining, splitting, and aggregating column data.

What SQL Server experience is required before starting?

You need Introduction to SQL Server and Intermediate SQL Server. Basic comfort with queries, filtering, and aggregations in SQL Server is expected.

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