Tactical Data Preparation

Executive Summary

Tactical data science zooms in on the mental models behind common data preparation tasks, and aim to equip beginning practitioners with some practical advice on wrangling data in a productive manner. The 6 main takeaways are illustrated in both R and Python code, using a real world dataset.

1. Favor a problem-solving style that is efficient and yields a short feedback cycle
2. Isolate contexts by diagnosing your data before exploring it
3. Diagnostic questions are sanity checks. They asks: "does the state of data conform to my expectations and statistical reality?"
4. Exploratory questions are concerned with pattern discovery. They asks: "how can the information in the data be applied?"
5. Incorporate prior knowledge and domain knowledge in your data preparation tasks
6. Syntactic equivalence != Logical equivalence

All example code are provided in R and Python, but only basic familiarity in both language is assumed.

Workshops

Jakarta

• Date: 27 November 2019
• Venue: Gedung Pusat Perfilman Usmar Ismail, Jl. H.R Rasuna Said
• Delivery: English

Bali

• Date: 13 December 2019
• Venue: Kembali Innovation Hub, Kuta
• Delivery: English

Badge of Completion

To earn a badge of completion, attempt the quizzes on https://corgi.re. Corgi is an aggregation tool for courses on github (hence the name) with a primary focus on data science and computer programming.

Link to earn a badge: Tactical Data Preparation | Corgi