I want to highlight a new presentation about Data Preparation in Data Science projects:
“Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects”
Data Preparation as Key for Success in Data Science Projects
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session discusses how this is related to visual analytics tools (like TIBCO Spotfire). Therefore, it also shows best practices for how the data scientist and business analyst should work together to build good analytic models.
Key Takeaway: Inline Data Wrangling Within Visual Analytics Tooling
Key takeaways of this session:
– Learn various options for preparing data sets to build analytic models
– Understand the pros and cons and the targeted persona for each option
– See different technologies and open source frameworks for data preparation
– Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
The following shows the slide deck:
Video Recording: Data Preparation vs. (Inline) Data Wrangling
Here is the video recording: