Linkedin Spss: Data Visualizing And Data Wrangling _hot_ • No Ads

More importantly, her manager started sending her the messy datasets first, saying, “Emma cleans and sees the story.”

Emma froze. She knew SPSS from college, but mostly for running t-tests and ANOVAs. Data wrangling? Visualizing for a business audience? And posting about it on LinkedIn? That felt like three different jobs. 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. More importantly, her manager started sending her the

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. Visualizing for a business audience

Emma had just landed her first data analyst role at a midsize retail company. She was excited—until her manager handed her a messy Excel file of customer feedback and said, “I need insights by Friday. Use whatever you want, but make it look professional. Oh, and post a summary on LinkedIn.”

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.