Pao Chi The Story Teller
I like change. I welcome change. I embrace change. Especially minuscule, almost imperceptible change. Change is an asymptotic process. Studying every little detail increases the accuracy of my judgement. As a Data Scientist, my interest is in understanding how and why things are the way they are.
Pao Chi’s Role
A Data Scientist’s day is filled with more than just numbers. Numbers represent data collected from people. My job involves understanding those numbers, and more than that, to look through those numbers and understand the people behind. To tell a story of numbers and translating those user insights into our product.
Transparency is data and data is transparency. Every user interaction is a data point. Everything we want to know is in the data. The only way to uncover them is through comprehensive data analysis, and with an open communication environment at Titansoft, we make magic happen.
Transparency allow us to see all problems before they occur.
Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs
The go-to handbook for implementing Objectives and Key Results (OKRs), a goal setting method with a simple approach to drive improvement. It includes a thorough explanation of OKRs, how to use them and case studies of diverse implementations from various companies which makes it easy for the reader to understand the principles behind OKR. Gaining support across the organization to drive the OKR process is difficult, but Niven and Lamorte have provided some tips in this book.
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An Introduction to Statistical Learning: with Applications in R
The first book centred around Machine Learning for beginners (with an understanding of statistics) to start off with, for a good introduction and overview of the different methods used in the field. It is approachable with the right amount of theory and practice, presents techniques in a clear manner and provides example datasets containing potential issues involved with real-world use. Beginning with linear regression, the reader is introduced to more complex concepts including principal component analysis, tree based methods and hierarchical clustering.
The Elements of Statistical Learning
This is a classic, rigorous and mathematically dense book on machine learning techniques from a statistical perspective. It summarizes almost everything you need to know about statistics and machine learning, and is very useful in terms of presenting insights behind a huge variety of different methods, such as why a method works. Read this after An Introduction to Statistical Learning: with Applications in R!