Data Science Introduction
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This document provides an overview of the key concepts in data science including statistics, machine learning, data mining, and data analysis tools. It also discusses classification, regression, clustering, and data reduction techniques. Additionally, it defines what a data scientist is and how they work with data to understand patterns, ask questions, and solve problems as part of a team. The document demonstrates some examples of admissions data and analyses simpson's paradox to illustrate data science concepts.
Data Science Introduction
- 2. What is Data Science
- 6. Admit Gender Dept Freq Admi.ed Male A 512 Rejected Male A 313 Admi.ed Female A 89 Rejected Female A 19 Admi.ed Male B 353 Rejected Male B 207 Admi.ed Female B 17 Rejected Female B 8 Admi.ed Male C 120 Rejected Male C 205 Admi.ed Female C 202 Rejected Female C 391 Admi.ed Male D 138 Rejected Male D 279 Admi.ed Female D 131 Rejected Female D 244 Admi.ed Male E 53 Rejected Male E 138 Admi.ed Female E 94 Rejected Female E 299 Admi.ed Male F 22 Rejected Male F 351 Admi.ed Female F 24 Rejected Female F 317 56% 44% acceptec rejected 65% 35% Men Women
- 10. Data Mining or Machine Learning
- 13. Key Tools • Classification • Regression • Cluster • Data Reduction
- 15. Cluster
- 16. Data Reduction
- 17. Process of ML
- 18. Understand Data
- 22. Data Scientist
- 23. What is Data Scientist • Know which question to ask • Understand the data • Know how to interpret the data • Works in a team
- 25. How to become a data scientist ?
- 28. Data is Tricky