How data science can affect the analysis process
In recent years data science has gained a lot of popularity which spans many different industries, whether it’s retail, healthcare, or government. These days organizations are generating so much data that requires the right means to do something useful. Data science, a fast-growing field helps analyze this data to provide organizations with necessary information about their consumers, market, and employees.
A Data Scientist is one who practices data science. This term has been used because a Data Scientist draws a lot of information from the scientific fields and applications whether it be statistics or mathematics. Data scientists are people who solve difficult data problems as they work with several fields such as mathematics, statistics, computer science, and more. They use the latest technologies to find solutions that are important for the growth and development of an organization. This data is much more useful and practical than the raw data that is given to them in structured and unstructured forms.
Data science is essentially used to make decisions and predictions, using predictive analysis, prescriptive analysis, and machine learning
- Predictive analysis– This is a model that predicts the likelihood of future possibilities for a certain event. Say, for example, if you’re providing customers with credit money, the likelihood for those customers to make the credit payment on time is a matter of concern. This is where you can apply a predictive analysis model on the payment history of past customers to predict whether those customers will return payments on time or not
- Prescriptive analysis-This model is a relatively new field; it has the intelligence to make decisions on its own with the ability to modify it. This model requires prescriptive analysis and it’s all about providing advice, meaning, not only does it predict but can suggest a diverse range of actions and outcomes. One of the best examples is Tesla’s self-driving cars. Tesla’s which is growing by approximately 5,000 cars per week, uses artificial intelligence and machine learning to harness human driving habits to build autonomous cars. This data enables cars to make decisions on when to turn, slow down, speed up, or which route to take, and much more.
- Machine learning – If you have available data, then machine learning algorithms are the best, for example, if you have data on fake purchases, you can train your machine based on past fraudulent cases to make future predictions on whether a future case would arise. In case data isn’t available to make predictions, then finding the patterns within a set of data is required to make meaningful predictions.
Data science is also used in Artificial Intelligence (AI). AI exceeds at finding patterns in large data files and does this at a scale and speed that humans just can’t see or do. Data science can also analyze hundreds of different websites to tell the “dos’s and dont’s” and predict outcomes of successful courses of action Marketing also uses the help of data science. A few examples are search engine optimization, customer engagement, responsiveness, real-time marketing campaigns. New ways to apply data science in marketing emerge every day, some of these are advertising, micro-targeting, micro-segmentation, and many more
The future belongs to the Data Scientists. More and more incoming data will provide opportunities to drive business decisions and soon, it’ll change the way we look at this world engulfed with data around us.
