ST-LE-2023-01
Curriculum: Starter TrackData science and big data
Motivation
Parallel
to the digital transformation, a novel scientific discipline has been
developed – data science. Data science allows new approaches for
interdisciplinary (big) data analyses through complex algorithms and
artificial intelligence (machine learning, deep learning etc.). Such
approaches extract information from the data sets beyond the current
scientific knowledge. Therefore, data science is of interest for nearly
all research as well as industry/economy fields and often termed as a
novel key discipline (e.g. Society of Informatics e.V., 2019). This
course provides a basic overview about data science applications.
To
produce reliable data science results a profound knowledge about the
data analyses methods, data management techniques and innovative
technologies is required. Additionally, to assess these results and
approaches an awareness of their ethical, legal, and social implications
is demanded (all topics are addressed in the following courses and
operator tracks).
Learning contents
1. History (timeline comparison with CPU power and storage costs) & clarification of terms
- Statistics > Machine Learning > Deep Learning
- Data Mining > Big Data
- Machine Learning vs. Artificial Intelligence
2. What is Data Science?
- Collection > Analysis > Visualization
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Big Data (data science with huge datasets, more memory of one PC required)
- Five Vs Model
- Privacy
3. Tools
Learning Outcomes
Basic overview about data science applications, methods, terms, tools and big data.
Prior knowledge and requirements
---
Further reading
---
Motivation
Parallel to the digital transformation, a novel scientific discipline has been developed – data science. Data science allows new approaches for interdisciplinary (big) data analyses through complex algorithms and artificial intelligence (machine learning, deep learning etc.). Such approaches extract information from the data sets beyond the current scientific knowledge. Therefore, data science is of interest for nearly all research as well as industry/economy fields and often termed as a novel key discipline (e.g. Society of Informatics e.V., 2019). This course provides a basic overview about data science applications.
To produce reliable data science results a profound knowledge about the data analyses methods, data management techniques and innovative technologies is required. Additionally, to assess these results and approaches an awareness of their ethical, legal, and social implications is demanded (all topics are addressed in the following courses and operator tracks).
Learning contents
1. History (timeline comparison with CPU power and storage costs) & clarification of terms
- Statistics > Machine Learning > Deep Learning
- Data Mining > Big Data
- Machine Learning vs. Artificial Intelligence
2. What is Data Science?
- Collection > Analysis > Visualization
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Big Data (data science with huge datasets, more memory of one PC required)
- Five Vs Model
- Privacy
3. Tools
Learning Outcomes
Basic overview about data science applications, methods, terms, tools and big data.
Prior knowledge and requirements
---
Further reading
---
When?
February 2, 2023, 9:30 AM - 11:30 AM
Where?
UNICOM 2
Haus 2 (Oxford)
Large Seminar Room 2.2090, 2nd floor
Mary-Somerville-Str. 2
28359 Bremen
and
Online via Zoom
Registration closed
Tings, Dr. Björn
German Aerospace Center
Remote Sensing Technology Institute
Researcher in working group Synthetic Aperture Radar (SAR) oceanography
Email: Bjoern.Tings@dlr.de
Cooperating in U Bremen Research Alliance:
