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1
Introduction
2
Who is a data scientist
3
Agenda
4
Soft Skills
5
Gathering Data
6
General Statistics
7
Factor
8
Test Differences
9
Causation and Correlation
10
Outliers
11
Removing outliers
12
Multivariate search for outliers
13
Things we inadvertently forget
14
Data normalization
15
Clustering
16
Normalization
17
Correlation
18
Visualization
19
Problem rules
20
Prediction
21
Recap
22
Questions
Description:
Explore common pitfalls in data science and statistics during this 55-minute conference talk from Analytics 2018. Learn how to avoid mistakes when preparing and engineering data using T-SQL or other database systems. Gain insights into soft skills, data gathering techniques, general statistics principles, and the proper handling of outliers. Discover the importance of data normalization, clustering, and correlation in your analyses. Understand the nuances of causation versus correlation and improve your visualization techniques. Master the art of prediction and problem-solving in data science while enhancing your overall approach to statistical analysis and database management.

Common Data Science Mistakes

PASS Data Community Summit
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