Data Science Tutorial - Real Life Uses of Data Science
While the overwhelming growth of digital data is crossing all limits, putting the data into the right user context remains to be the biggest challenge. Data is considered as the new wealth for the world of businesses and public services because data-driven insights, data visualisation and data modelling can really help us taking accurate decisions at the right time.
This power of data in transforming our life cannot be realised without some befitting examples. Let us explain a few real life use cases of data science.
Energy Management
For modern enterprises saving energy through optimising the energy use practices is equally crucial for cost saving and building the brand image as an environment friendly one. Data analytics can play a crucial role in saving energy and optimising the energy uses for a variety of purposes. By some software companies data analytics is being integrated into the software management tool to provide consumer based energy saving solutions based on individual uses.
Fraud and Risk Control
Finance has been the early and one of the biggest beneficiaries of data science. By analysing the customer behaviour, transaction patterns and a variety of use cases, data analytics can detect irregularities and inconsistencies that smell any fraudulent activities underneath. Highly equipped data analytics and data modelling tools and applications can continuously update the management about irregularities and undergoing inconsistencies.
Healthcare Data Analytics
Healthcare sector is one of the biggest beneficiaries of the data analytics. In the healthcare industry, data analysis, data modelling and latest data-driven technologies like Machine Learning can offer precise solutions to several problems. Let's explain the role of data science in healthcare with the below mentioned use cases.
- Genetics and Genomics: Through the analysis of individual
genetics and DNA data in connection to medical history and drug
response, researchers can find deep-lying biological connections
between diseases and genetics.
- Drug Development: Drug development process is highly
complicated, time-consuming and cost intensive. By taking the
medical records and drug testing data under the purview of data
analytics, a lot of actionable and important insights can be
produced that can actually make this process faster and less
cost-intensive.
- Improved Patient Care: By scanning the patient data along with medical history, real-time analytics can suggest more effective patient care instructions for the caregivers and physicians.
Targeted Advertising
The digital marketers can reap the benefits of data science for targeted advertising to reach out to the intended audience more effectively. Based on the data driven insights about the intended audience, advertisements can be rolled out that can engage audience and convert business better.
Ecommerce Product Recommendations
Across modern ecommerce stores you often come across product recommendations that perfectly match your taste and preference. Well, often a recommendation engine based on a data-analytics algorithm does this. Based on previous purchases, browsing history, buying patterns and in-app behaviour, the algorithm suggests products that are more likely to be bought by the respective user.
Advanced Image Recognition
Based on the advanced image recognition input by a data driven algorithm, users of social media platforms are provided appropriate suggestions to tag friends and other people. This image recognition technology is getting sharper with the ability of more detailed and in-depth classification.
Conclusion
Obviously, the above mentioned examples are just a few of the hundreds of real-life examples where data science plays an important role. In the time to come, more professional and enterprise niches will be utilising data science to keep up with the technological demands.