DATA SCIENCE AND NEURAL NETWORK

Description

Data can be proved to be very fruitful if we know how to manipulate it to get hidden patterns from them. This logic behind the data or the process behind the manipulation is what is known as Data Science. From formulating the problem statement and collection of data to extracting the required results from them the Data Science process and the professional who ensures that the whole process is going smoothly or not is known as the Data Scientist.

Data science is the process of using computer scientists, statisticians, and subject matter experts to collaborate on and solve real-world problems with collected data. It mixes math, statistics, and programming to find hidden patterns and come up with answers. But to discover its full potential, we need to follow specific steps known as the data science process. This post explains the key stages of the data science process in simple terms. We’ll examine the lifecycle, tools, model building, team roles, data prep, and ethics. Understanding the process is the key to valuable data science results.

What is Data Science?

Data can be proved to be very fruitful if we know how to manipulate it to get hidden patterns from them. This logic behind the data or the process behind the manipulation is what is known as Data Science. From formulating the problem statement and collection of data to extracting the required results from them the Data Science process and the professional who ensures that the whole process is going smoothly or not is known as the Data Scientist. But there are other job roles as well in this domain as well like:

1.  Data Engineers
2.  Data Analysts
3. Data Architect
4. Machine Learning Engineer
5. Deep Learning Engineer

 What is the Data Science Process?

The data science process gives a clear step-by-step framework to solve problems using data. It maps out how to go from a business issue to answers and insights using data. Key steps include defining the problem, collecting data, cleaning data, exploring, building models, testing, and putting solutions to work.

Some steps are necessary for any of the tasks that are being done in the field of data science to derive any fruitful results from the data at hand.