In the tradition of previous Africa Data Science workshops, a summer school on machine learning and data science was held prior to the main workshop. This summer school targeted graduate students, researchers, and professionals working with huge amounts of data or unique datasets.
The summer school focused on introductory and advanced lectures in data science and machine learning, as well as moderate to advanced practical and tutorial sessions where participants got their hands wet wrangling and munging datasets and applied cutting-edge machine learning techniques to derive inference from the data. Lectures were given by distinguished world-renowned researchers and practitioners, including researchers from Sheffield University, IBM Research, Facebook, Pulse Lab Kampala, and the AI and Data Science (AIR) Lab–Makerere University.
The school also involved end-to-end tutorial sessions from professionals who walked the participants through a real data analytics problem from data acquisition to data presentation. To benefit from this course, participants were encouraged to have some background in programming, particularly programming with Python.
School programme outline:
Pre-workshop
Stuff to install..
Stuff to install..
To ensure we hit the ground running, it is essential you install the prerequiste software and test it out and make sure it is working on your computer. The venue for the summer school will have some computers on which the software will have been installed but you are advised to come with your own laptop with the software installed.
Luckily all the software required has already been prepackaged in a bundle called Anaconda. You can download the various versions of the software for your laptop OS and architecture from the Anaconda website
Stuff to do..
To ensure that the software is working fine on your machine and to get you up and running, download the following jupyter notebook and do the exercises in there. If you have not used jupyter notebook before, make friends with Google - a friend in need …
Summer School Day 1
Time |
Activity |
Material |
|---|---|---|
|
08:00-08:30 |
Arrival and Registration | |
|
08:30-09:00 |
Opening Remarks | |
|
Session 1 (Machine Learning and Data Science) | ||
|
09:00-10:00 |
Lecture 1: Introduction to Data Science and Machine Learning | |
|
10:00-10:30 |
Break | |
|
Session 2 (Machine Learning) | ||
|
10:30-12:00 |
Lecture 2: Introduction to Classification | |
|
12:00-13:00 |
Lecture 2: Practice Session | |
|
13:00-14:00 |
Lunch | |
|
14:00-15:20 |
Lecture 2: Practice Session | |
|
15:20-15:30 |
Break | |
|
Session 3 (Data Science) | ||
|
16:00-16:30 |
Lecture 3A: Spatial Data Analysis | |
|
16:30-17:00 |
Lecture 3B: From raw data to meaningful features | |
|
17:00-18:00 |
Lecture 3: Practice Session |
Summer School Day 2
Time |
Activity |
Material |
|
Session 4 (Data Science) |
||
|
09:00-10:00 |
Lecture 4: Data Wrangling with Pandas |
|
|
10:00-10:30 |
Break |
|
|
10:30-12:00 |
Lecture 4: Practical Session |
|
|
Session 5 (Machine Learning) |
||
|
12:00-13:00 |
Lecture 5: Classification Continued |
|
|
13:00-14:00 |
Lunch |
|
|
14:00-15:20 |
Lecture 5: Practical Session Malaria Detection |
|
|
15:20-15:30 |
Break |
|
|
Session 6 (Data Science) |
||
|
15:30-16:30 |
Lecture 6: Data Exploration and Visualization |
|
|
16:30-18:00 |
Lecture 6 Practical Session |
Summer School Day 3
Time |
Activity |
Material |
|
Session 7 (Data Science) |
||
|
09:00-10:00 |
Lecture 7: Text Mining |
|
|
10:00-10:30 |
Break |
|
|
10:30-12:00 |
Lecture 7: Practical Session |
|
|
Session 8 (Machine Learning) |
||
|
12:00-13:00 |
Model Selection |
|
|
13:00-14:00 |
Lunch |
|
|
14:00-15:20 |
Lecture 8: Practical Session |
|
|
15:20-15:30 |
Break |
|
|
Session 9 (Data Science) |
||
|
15:30-16:30 |
Lecture 9 |
|
|
16:30-18:00 |
Lecture 9: Practical Session |
|
|
18:00-19:30 |
Cocktail |