CLP Data Science

Session II

How have you been?

What's on your mind?

SESSION I : CODE, STATS,

TOOLS & TECHNIQUES

SESSION II : QUESTIONS. ANSWERS. MINDSET.

SESSION II SCHEDULE

Clean up the mess as soon as the data comes in, to avoid your analysis from starting to smell.

DS2.1 : DATA HYGIENE

The world isn't normal and linear; how can we adapt our regression models to reflect that?

DS2.2 : Adv. REGRESSION

With powerful machines come powerful predictions; but how can we use them responsibly and not blindly lead ourselves into a Random Forest?

DS2.3 : ENSEMBLE METHOD

Features that come in with your dataset are often just surface representations; can we use representation learning to go beyond the surface?

DS2.4 : Adv. CLASSIFIERS

There's statistical tests for every letter of the alphabet; when aren't they just significant but essential?

DS2.5 : HYPOTHESES

What can language use tell you about a person? 

DS2.6 : NLP

How do you account for irregular events like Hong Kong's hectic holiday calendar? 

DS2.7 : TIMESERIES

Based on a certain sequence of meter readings, can you tell me what appliances are used in that household?

DS2.8 : DISAGGREGATION

Your code brings the numbers to the screen, now you communicate to share what the predictions mean.

DS2.9 : COMMUNICATION

We'll be looking at patterns where machine learning is half the solution, human interpretation the other.

DS2.10 : CAPSTONE

CONSTITUTION

HOW TO PREPARE?

REVIEW MATERIALS

HOW TO PREPARE?

WARM-UP CHALLENGE

HOW TO PREPARE?

GGPLOT CHEATSHEET

CAPSTONE PROJECT

PATTERN RECOGNITION

CAPSTONE PROJECT

ONE of FIVE

CAPSTONE PROJECT

PRESENT a SOLUTION

CAPSTONE PROJECT

REFLECT on FEEDBACK

CAPSTONE PROJECT

REFLECT on CONSTITUTION

How can we go faster, farther?