Spring 2019 course
This course is for World Bank staff who need data science methods for their analytical or operational work. The instructors are Nick Jones (GFDRR Labs), Charles Fox (GOST) and Dharana Rijal (DEC).
Objectives: (i) expand the cohort of Bank staff with intermediate-level Python skills; (ii) contribute to quality of WB operations and analysis through increased uptake of data science methods; and (iii) expand the internal user base for World Bank APIs and code libraries.
Prerequisites and format: No prior coding experience is needed. The sessions will comprise short lecture-style teaching by the instructors, a 'lab' session where participants write code on their own laptops, and 'show and tell' sessions where an experienced Python user shows an example of their work through a code demonstration.
Participant outcomes: With no coding knowledge before the course, participants should be able to access and combine a diverse set of datasets, conduct data exploration and visualization, utilize Python libraries for geospatial data and machine learning, and be able to self-teach next steps in their specialized domain.
To gain credit, participants are required to submit a final project. The format is a Jupyter Notebook. The notebook should ingest, analyze and visualize data using Python, to address a business need of the participant's unit. Short 'project clinic' sessions will be held on May 2 and May 16 to provide support (eg. identifying relevant libraries to address specific project needs).
The course meets on Thursdays at 3.30pm - 5.00pm.
| Date and venue | Room | Topic |
|---|---|---|
| April 4 | MC7-860 | Python syntax and data structures, intro to control flow. |
| April 11 | n/a | no class due to Spring Meetings |
| April 18 | MC C1-100 | Further data structures, control flow, file handling. |
| April 25 | MC 7-860 | Intermediate Python, intro to Pandas and Numpy. |
| May 2 | MC 7-860 | Data wrangling and analysis. |
| May 9 | MC C1-200 | Data wrangling and analysis II. |
| May 16 | MC C1-100 | APIs and geospatial data. |
| May 23 | MC C1-100 | Intro to machine learning. |
| May 30 | MC C1-200 | Flexible teaching session. |