My Journey into Buzzword Bingo
summary
A junior data scientist recounts jumping straight from a CS degree into practical work, discovering that most time goes to business understanding, data acquisition and cleaning rather than modeling, and that projects require SQL, Python, Jupyter, pandas/numpy, scikit‑learn (TensorFlow later), and visualization with Matplotlib/Seaborn or Tableau. Early experiments use a Kaggle dataset and many ready‑made libraries; the surprise is how little hand‑coding of algorithms matters and how important stakeholder questions, feature choice, deployment and community support turn out to be.
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chapters (7)
- 0:00Introduction and background
- 1:13Data science myths and reality
- 4:50Machine learning basics
- 8:21First year struggles
- 13:21Data science toolbox
- 16:31Lessons learned and growth
- 20:25Q&A session
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