This document provides an overview of using Excel for data analysis as a journalist. It discusses what constitutes data in Excel, which is information organized into columns and rows. It then demonstrates how non-data can be transformed into data by structuring personal details into a table. The document outlines why Excel is useful for analyzing data, as it can handle large datasets and help users identify patterns. It also demonstrates some of Excel's core functions for working with data, including importing data from different formats, sorting, filtering, transforming data using formulas and functions, and summarizing data into categories using pivot tables.

1. Excel for Journalists
Steve Doig
Arizona State University
USA

2. What is “data”?
— Information in table form
— Columns are the variables
— Name, date, time, address, age, etc.
— Rows are the records
— Persons, incidents, etc.

3. Information, but not data
— Steve Doig is a 63-year-old professor who teaches at Arizona
State University.

4. Now it’s data!
Last
name
First
name Age Title
Doig
Jones
Steve 63
Bob
45
Professor
Reporter
Phoenix
Miami
Smith
Tom
Reporter
New York
34
City

5. Why use Excel?
— Good stories can be found in the patterns of data
— Human mind alone can’t see the patterns in large sets of
data
— Excel has tools to help us see the patterns in data in table
form
— Excel can handle large tables
— More than 16.000 columns
— More than 1 million rows

6. A blank spreadsheet

7. What Excel can do
— Import data from many formats
— Sort data by one or more variables
— Filter data to show only selected rows
— Transform data using functions and formulas
— Summarize data into categories

8. Importing data
— Common formats
— *.xls (or *.xlsx)
— Fixed-width text
— Delimited text (comma, tab, etc)
— HTML tables
— Data Import Wizard will help

9. Delimited text example

10. Fixed-width text

11. Sorting a table

12. Now it’s sorted

13. Filtering: Data…Filter…Autofilter

14. Pick a category…

15. …and see just that

16. Transforming data
— Math/stats functions
— Add, subtract, multiply, divide
— Average, median, maximum, minimum
— Rank, z-scores
— Date/Time functions
— Day of week, days between
— Text functions
— Extract parts of text strings
— Combine strings
— Search and replace text

17. Function Wizard (ƒx)

18. Function Wizard (ƒx)

19. Summarizing data
— We often want to take a big collection of individual records
and pile them into categories
— Trick: Visualize the piece of paper that would give you the
answer you seek
— Tool: Pivot tables

20. Pivot table example
— Data: Region, town name, crimes, etc.
— Question: “How many crimes occurred in each region?”
— Visualize the piece of paper that would answer the question

21. Building a pivot table

22. Pivot table

23. Sorted pivot table

24. Questions?