Lecture 21: Big data - Introduction to Data Science (IDS) #datascience

Lecture 21: Big data - Introduction to Data Science (IDS) #datascience

In this lecture, Wil van der Aalst focuses on big data technologies, including Hadoop, MapReduce, streaming data, etc.

Data science has emerged as a new and important discipline. Data science can be viewed as an amalgamation of classical disciplines, such as statistics, data mining, databases, and distributed systems. This combination helps to turn data into value for the profit of individuals and society. In addition, new challenges are constantly emerging and make this field highly dynamic and appealing. These are not just in terms of size (“Big data”), but also regarding complexity of the questions to be answered. Data science provides numerous opportunities to develop exciting products and services. With technological evolution, the boundaries of what algorithms can perform will be pushed even further. This development raises significant questions that will be addressed in this course.

The course is mainly focused on data analysis and discusses a substantial range of analytical approaches and tools. All in all, the course aims to provide a comprehensive overview of data science using analytical tools applied to real-life and synthetic datasets.

The course discusses three main parts of data science:

(1) Data science infrastructure concerned with volume and velocity. The topics include instrumentation, big data infrastructures and distributed systems, databases and data management, and programming. The main challenge is to make making things scalable and instant.
(2) Data science analysis concerned with extracting knowledge from data. The topics cover statistics, data and process mining, machine learning and artificial intelligence, operational research, algorithms, and data visualization. In this part, the main challenge is to provide answers to known and unknown unknowns.
(3) Data science effects concerned with people, organizations, and society. The topics discuss ethics and privacy, IT laws, human-technology interaction, operations management, business models, and entrepreneurship. Here, the main challenge is to implement data practices in a responsible manner.

#datascience #machinelearning #datamining #processmining