Linkedin Spss: Data Visualizing And Data Wrangling -

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.

Whether you’re a student or a new analyst, combining data wrangling, thoughtful visualization, and a generous LinkedIn post can open doors you didn’t even know existed. And it all starts with a single, clean dataset. linkedin spss: data visualizing and data wrangling

More importantly, her manager started sending her the messy datasets first, saying, “Emma cleans and sees the story.” That evening, she opened SPSS and stared at

Her favorite find: the option in Chart Builder, which created small multiples—one chart per region, side by side. Instantly, she saw that the West region loved electronics but hated clothing returns. Step 3: The LinkedIn Post On Friday, Emma presented a clean dashboard of charts to her manager, who was impressed. “Now write that LinkedIn post,” he reminded her. She used Transform > Recode into Different Variables

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: