Data Engineering & Data Science in 30 hours

I have been commissioned to prepare a complete Big Data class to fit in 30 hours of teaching. The goal is to introduce practical Data Engineering and Data Science to technical personnel (corporate or academic). The class is very technical and hands-on. Most subjects are introduced by examples that students are expected to play with.

The class is designed for technical audience, reasonably fluent in general programming, operating systems, and exposure to Linux shell, databases,  SQL and Python.

In data engineering, we cover: databases (Oracle), data warehousing, Tableau, MongoDB, BigTable,  Hadoop, HDFS, Apache Spark, Amazon AWS, streaming (Kafka). In data science, we learn the basic data analytics routines: regression, prediction, classification, anomaly detection, deep learning – with Numpy, pandas, matplotlib, scikit-learn, TensorFlow, and Keras. The syllabus is here, along with materials you can download.

At the moment (late 2018), I am teaching the class at Cracow Technical University (Politechnika Krakowska), Faculty of Physics, Mathematics and Computer Science. My first audience are the final year students of the graduate Master’s degree Informatics studies, Data Analytics specialty. Courtesy of the Faculty, the lectures are open to all. If you live in Krakow, Poland, please come: every Wednesday 9:00 am, Room F020, ul. Podchorążych 1, Kraków (Politechnika Krakowska Wydz. Fiz Mat i Inf), until 23rd January 2019.

We have a number of visitors at each lecture, so do not worry – just show up at the class. However drop me a line first, to ensure there has been no last-minute change of schedule.

 

 

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