Case Studies:

1. Predictive Modeling for Maintaining Oil and Gas Supply

Crude oil and gas industries face a major problem of equipment failures which usually occurs due to the inefficiency of oil wells and their performance at a subpar level.

With the adoption of a successful strategy that advocates for predictive maintenance, the well operators can be alerted of crucial stages for shutdown as well as can be notified of maintenance periods. This will lead to a boost in oil production and prevent further loss.

Data Scientists can apply Predictive Maintenance Strategy to use data in order to optimize high-value machinery for manufacturing and refining oil products. With the telemetry data extracted through sensors, a steady stream of historical data can be used to train our machine learning model.

This machine learning model will predict the failing of machine parts and will notify the operators of timely maintenance in order to avert oil losses.

A Data Scientist assigned with the development of PdM strategy will help to avoid hazards and will predict machine failures, prompting the operators to take precautionary steps.

2. Pharmaceutical Industries

With the enhancement in data analytics and cloud-driven technologies, it is now easier to analyze vast datasets of patient information. In Pharmaceutical Industries, Artificial Intelligence and Data Science have revolutionized oncology. With new pharmaceutical products emerging every day, it is difficult for the physicians to keep themselves updated on the treatment products.

Moreover, more generic diagnostic treatment options find it difficult to tap into a complex competitive market. However, with the advancements in analytics and through the processing of parallel pipelined statistical models, it is now easier for pharmaceutical industries to have a competitive edge over the market.

3. Education

Data Science has also changed the way in which students interact with teachers and evaluate their performance. Instructors can use data science to analyze the feedback received from the students and use it to improve their teaching.

Data Science can be used to create predictive modeling that can predict the drop-out rate of students based on their performance and inform the instructors to take necessary precautions..

4. BioTech

The human gene is composed of four building blocks – A, T, C and G. Our looks and characteristics are determined by the three billion permutations of these four building blocks. While there are genetic defects and defects acquired during lifestyle, the consequences of it can lead to chronic diseases. Identifying such defects at an early stage can help the doctors and diagnostic teams to take preventive measures.

Due to the explosion in data, we can understand complex genomic sequences and analyze them on a large scale.

Data Scientists can use contemporary computing power to handle large datasets and understand patterns of genomic sequences to identify defects and provide insights to physicians and researchers.

Furthermore, with the usage of wearable devices, data scientists can use the relationship between the genetic characteristics and the medical visits to develop a predictive modeling system.