Research Paper Reads - DS Development Challenges

Paper #1 - Automating Data Science: Prospects and Challenges

Key Notes

• Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization
• The breadth and complexity of these and many other data science scenarios means that the modern data scientist requires broad knowledge and experience across a multitude of topics

• In classical goal-oriented projects, the process often consists of activities in the following order: Data Exploration, Data Engineering, Model Building and Exploitation.

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Key Notes

The stages addressed in terms of Machine Learning Model Development

• A Model requirements stage which is related to the agreement between stakeholders and the way the model should work.
• Data processing stage which involves data collection, cleaning and labelling (in case of supervised learning).
• Feature engineering stage which involves the modification of the selected data.
• Model training stage which is related to the way the selected model is trained and tuned on the (labeled) data.
• Model evaluation stage which regards to the measurements used in order to evaluate the model.
• Model deployment stage which includes deploying, monitoring and maintaining the model.

Data Science Methodologies: Current Challenges and Future Approaches

Key Notes

• Leveraging data science within a business organizational context involves additional challenges beyond the analytical ones. 

Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment

Key Notes

Keep Thinking!!!