Data science can be defined as the process of taking data sets and analyzing them in order to provide insights into how to improve systems. It is the type of technology that has gained a lot of attention as an industry since it provides the ability to develop predictions that can make decisions in industries and help people make better decisions. There are three main areas that are broken down by data science. These are experimental design, interpretation and prediction.
Ø Experimental Design
The data scientist must conduct experiments in order to establish a hypothesis that the sample data from the experiment shows what is predicted to happen. He must then have the ability to test his hypothesis and see if the prediction is correct. If not, then he needs to have the ability to take the next step by starting a new experiment to see if his previous one is wrong.
The data scientist interprets the results of the experiments and then combines them with data from the previous experiments to determine if the hypothesis is correct. This is done based on how the data were analyzed and how they affected the predictions made by the data scientist.
Once a hypothesis is validated then the data scientist can use this information to predict what the future will be like based on the results of the experiments. This is often used in predicting the sales of products.
Other Prominent Areas In Data Science
As one can see there are three main areas that data science deals with. However there are also sub areas that are divided out according to the data scientist. The three main areas are data collection, interpreting and predictions.
Data collection is the process of collecting the data. The data collector would then review the collected data in order to determine the accuracy of the data. The collector will then analyze the collected data in order to determine the validity of the collected data. The collected data is then used to train the data scientists in the analysis and interpretation of the collected data.
Data interpretation is the process of using the collected data to determine what effect it has on the predicted outcome. There are many different types of data that could be used for this purpose. The data may be experiments on human behavior, ecological data, time series data or even stock price data. The analysis and interpretation of the collected data would provide insight into the collected data.
Data prediction is the process of using collected data to predict the effects of the collected data on the predicted outcome. This could be done using tests of prediction in order to measure the accuracy of prediction.
One of the most important aspects of data science is the ability to interpret the data. In order to gain insight into the collected data, the data scientist must be able to interpret the collected data and make predictions about how it can be used to improve their predictions.
Grasp first hand exposure to working on the techniques involved in Data Science & get transformed into a job-ready Data Science expert with the help of the advanced Data Science Training In Hyderabad program by Analytics Path.