AI Skills for Engineers: Data Engineering and Data Pipelines

Delft University of Technology via edX

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Overview

Artificial Intelligence and Machine Learning have become central techniques for most services and products, ranging from web-based systems to medical procedures, self-driving cars even intelligent coffee makers. Alongside algorithms, data is central to AI applications. Without solid data management, AI projects typically underperform or even fail. Unfortunately, the relevance and complexity of handling data is frequently underestimated. Thats why we developed this course which covers foundational questions like Why is data important to AI? and What data does...

Syllabus

Week 1:

We ask why we should care about data management for Artificial Intelligence and Machine Learning (ML) systems.
We examine which data are needed in the ML lifecycle and what properties that data should have.
We discuss the effort and time needed for data management activities, and look at possible data sources.

Week 2:

The basic key concepts of data management, such as databases, data models and data schemas are all introduced.
The Relational Data Model is explained and contrasted with the Single-Table Model (like CSV and Excel) and Document Models.

Week 3:

We show how to extract data from existing relational databases using SQL queries and converting the query results into CSV files for further processing using pandas in Python notebooks.

Week 4:

The different ways setoff setting up and running Python notebooks are covered, including cloud-based notebooks and local notebooks.
We will take you step by step through the process of setting up your conda environment and installing Jupyter and pandas libraries.
You will learn how to run notebooks in VS code.

Week 5:

Become a pandas expert.
Explore the essential functionalities of pandas and, most importantly, write elegant and efficient Python pandas code to process and engineer tabular data.

Week 6:

You will learn how to make simple and clear scientific figures in Python using the Seaborn library.
Use the core functions provided by Seaborn to make beautiful statistical plots.