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.
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.” 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. Then came the trickier part: creating a new
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. Emma had just landed her first data analyst