When people think about data science and analytics, the first thing that comes to mind is often the technical aspects of working with data wrangling, model building, deployment, or visualizations. These are undoubtedly essential to a modern data-driven business and product, but what is often ignored is the role of experimentation.

Looking at big tech companies (FAANG and others), the ability to run experiments at scale is one of their key competitive advantages. These experiments are what allows them to optimise products, learn about consumers, and continuously drive improvements across their organization.

In this webinar, we will cover the basics of designing and evaluating experiments, as well as when to (and when not to) use them. We will also apply this knowledge to analyzing a case study of a simulated typical A/B test.

With Michael Han (Data Intelligence Lead at Spenmo, Ex-Grab), we will share some insights on:

• To understand comprehensively about data experimentation and understand the
  step-by-step process how to build data experimentation
• Usage of experimentation in modern tech companies
• Principles of designing and evaluating an experiment
• When to use experiments, and when to avoid
• What statistical methods to apply when analyzing simple experiments, and doing so using stats model package in Python

What Will You Get?

• Full practical insights and knowledge from the Expert
• Speaker's presentation material
• E-certificate

This Class is Suggested for:

• Fresh Graduates who are intrested in Data & Analytics
• Data Enthusiast
• Data Scientist
• Data Analyst
• Data Engineer
• Business Intelligence

Syarat & Ketentuan