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.
#SPSS #DataWrangling #DataVisualization #Analytics #EntryLevelAnalyst She added a carousel of her SPSS charts (exported via ), tagged her professor and college, and clicked post. The Unexpected Result Within 24 hours, her post got 5,000+ impressions. A senior data scientist from a tech company commented, “Love seeing SPSS get love for wrangling, not just stats. Small multiples for the win.” A recruiter messaged her about a senior analyst role. 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. Within two hours, her dataset was tidy: no
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. Small multiples for the win
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.
