We would like to expose data science trainers to the entire pipeline of data collection, analysis, and communication of results using relevant examples. We plan to have participants deploy sensor systems at the DeKUT conservancy and farm, which will collect data that they will then analyse.
To benefit from this course, participants must have some background in programming, particularly programming with Python and machine learning. The registration process will include submission of worked examples in Jupyter notebooks.
School programme outline:
Day 1
Time |
Activity |
Material |
|
08:00-09:00 |
Arrival and Registration |
|
|
09:30-09:30 |
Welcome and Opening Remarks |
|
|
09:30-10:30 |
Lecture 1: Introduction to Data Science |
|
|
10:30-11:00 |
Break |
|
|
11:00-12:00 |
Lecture 2: Fundamentals of IoT |
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|
12:00-13:00 |
Practical Session: IoT |
|
|
13:00-14:00 |
Lunch |
|
|
14:00-15:30 |
Field work introduction, Prep IoT for FW1 |
|
|
15:30-17:30 |
Field Work 1: IoT Greenhouse deployment |
Day 2
Time |
Activity |
Material |
|
08:30-09:30 |
Anomaly detection, density estimation tutorial |
|
|
09:30-10:30 |
Python, Pandas and Jupyter tutorial |
|
|
10:30-11:00 |
Break |
|
|
11:00-12:30 |
TAHMO Weather station network and data control task |
|
|
12:30-14:00 |
Lunch |
|
|
14:00-15:30 |
Classification Tutorial & Research Software |
|
|
15:30-17:30 |
Field Work 2: Camera Trap Deployment |
Day 3
Time |
Activity |
Material |
|
08:30-09:30 |
Reinforcement Learning for ecosystem management |
|
|
09:30-10:30 |
Computer Vision and Image Analysis |
|
|
10:30-11:00 |
Break |
|
|
11:00-12:30 |
Data Visualization Tutorial |
|
|
12:30-14:00 |
Lunch |
|
|
14:00-15:30 |
Data Engineering and Infrastructure |
|
|
15:30-17:30 |
Field Work 3: Air Quality Sensor Deployment |
Day 4
Time |
Activity |
Material |
|
08:30-09:30 |
Bayesian Methods |
|
|
09:30-10:30 |
Deploying Models |
|
|
10:30-11:00 |
Break |
|
|
11:00-12:30 |
Bayesian Methods Practical |
|
|
12:30-14:00 |
Lunch |
|
|
14:00-15:00 |
Spatial Data Analysis |
|
|
15:00-15:30 |
Break |
|
|
15:30-17:30 |
Spatial Data Analysis Practical |
Day 5
Time |
Activity |
Material |
|---|---|---|
|
08:30-09:30 |
Engineering human vision: an introduction to convolutional neural networks | |
|
09:30-10:30 |
Introduction to Deep Learning | |
|
10:30-11:00 |
Break | |
|
11:00-12:30 |
Practical Deep Learning | |
|
13:30-14:30 |
Lunch | |
|
14:30-16:00 |
Deep Learning Practical: Tensorflow | |
|
16:00-17:00 |
Interpretability, Bias in Models |
Day 6
Time |
Activity |
Material |
|---|---|---|
|
08:30-09:30 |
Advanced Deep Learning | |
|
09:30-10:30 |
Introduction to Natural Language Processing (NLP) | |
|
10:30-11:00 |
Break | |
|
11:00-12:30 |
Deep Learning Practical II | |
|
12:30-14:00 |
Lunch | |
|
14:00-15:00 |
Adversarial Methods | |
|
15:00-15:30 |
Break | |
|
15:30-16:30 |
Closing Panel |