PinnedInTDS ArchivebyOlivia Tanuwidjaja·Jan 31, 2023How to Stay Ahead as a Data Analyst..and make yourself relevant and competitive in the professional marketA response icon11A response icon11
InTDS ArchivebyOlivia Tanuwidjaja·Nov 12, 2023Exploring Time-to-Event with Survival AnalysisIntroduction of Survival Analysis and its application in PythonA response icon3A response icon3
InTDS ArchivebyOlivia Tanuwidjaja·Aug 25, 2023Legal and Ethical Perspectives on Generative AIExploring the implications of AI-generated content from the legal and ethical aspectsA response icon2A response icon2
Olivia Tanuwidjaja·Jun 17, 2023When a Data Analyst makes a mistakeMistakes can happen anywhere, including at work. Here’s how I cope and learn from them.A response icon2A response icon2
InTDS ArchivebyOlivia Tanuwidjaja·May 7, 2023Prompt Engineering GuidePrinciples, Techniques, and Applications to Harness the Power of Prompts in LLMs as a Data AnalystA response icon1A response icon1
InTDS ArchivebyOlivia Tanuwidjaja·Apr 11, 2023Guide to Successful ML Model Deployment for Data AnalystsHow data model deployment differs from other analytics projectsA response icon1A response icon1
InProduct CoalitionbyOlivia Tanuwidjaja·Mar 5, 2023Customer Lifetime Value ExplainedWays to calculate Customer Lifetime Value (CLV) and adjust your product priorities.A response icon3A response icon3
InTDS ArchivebyOlivia Tanuwidjaja·Jan 17, 2023Pitfalls in Product ExperimentationCommon to-not-do-lists often overlooked in product experimentation causing poor and unreliable results
InTDS ArchivebyOlivia Tanuwidjaja·Oct 24, 2022Life Lessons I Learned from Working as a Data AnalystIt starts with following the curious mind
InTDS ArchivebyOlivia Tanuwidjaja·Sep 20, 2022Image (Meta)data Feature Extraction in PythonExploring the metadata and color-related features of a photo image for further use in Analytics and ML
