Getting ready for a career in Analytics and Data Science
summary
Explains that a career requires concrete skills: strong foundations in calculus, linear algebra, probability and statistical inference and regression, plus proficiency in R and Python and database tools (SQL, NoSQL) and distributed systems like Hadoop and Spark. Gives concrete use cases—churn prediction, chest X‑ray triage, hospital length‑of‑stay, predictive maintenance, box‑office forecasting—and notes big‑data’s three V’s (volume, variety, velocity), when small labelled datasets suffice, and the necessity of continual unlearning and relearning over a multi‑decade career.
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chapters (24)
- 0:00Introduction
- 3:00Sponsors
- 4:00About the speaker
- 5:00Questions
- 6:00Purpose
- 7:00What is Analytics
- 9:00Alternatives
- 10:00Applications
- 13:00Big Data
- 14:00Variety
- 15:00Velocity
- 17:00Not all data sets have all attributes
- 27:00Analytics Skills
- 28:00Long Time horizon
- 29:00Pay Package
- 30:00How to prepare
- 31:00Skills needed
- 32:00Techniques needed
- 33:00Tools needed
- 34:00Changing nature of data
- 35:00Learning attitude
- 36:00Data requirements
- 37:00Data engineering
- 38:00Conclusion
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