Linkedin Spss: Data Visualizing And Data Wrangling -

Last week, I faced 10K rows of chaos: missing values, duplicate IDs, and inconsistent dates. Here’s my 3-step SPSS workflow for data wrangling + visualizing:

That evening, she opened SPSS and stared at the dataset: 10,000 rows, missing values, inconsistent date formats, and duplicate customer IDs. Her first instinct was to panic. Instead, she remembered a phrase from her favorite professor: “Clean data is the difference between a story and a lie.” Emma started with the basics. She used Transform > Recode into Different Variables to fix the messy date column. For missing values, she ran Transform > Replace Missing Values , choosing “Series Mean” for numeric feedback scores. Duplicates were handled with Data > Identify Duplicate Cases , keeping only the first entry per customer. linkedin spss: data visualizing and data wrangling

Within two hours, her dataset was tidy: no blanks, no duplicates, consistent scales. Now for the magic. Emma wanted to show her manager how sentiment varied by product category and region. Last week, I faced 10K rows of chaos:

Then came the trickier part: creating a new “Customer Sentiment” variable from open-ended text responses. She used to turn categories (“very unhappy” to “very happy”) into numbers 1–5. A quick Frequencies check showed the distribution looked plausible. Instead, she remembered a phrase from her favorite