In this blog, I will cover mainly the Life Cycle of Data Science. And, somewhat a brief introduction of Data Science and some examples of why we need Data Science? In previous days, we are facing some problems like increasing data daily as well as storage also increase. Now, this problem can successfully solve by Hadoop. But, another problem is there with data. Processing the data is very difficult to understand. We can take up the data and submitted to the organization in an understandable way. It is the most important task. For this problem, Data Science arrives and solve many problems and through this organizations reach the top position. Take an attention, learn what is Data Science and how it is more important to the business. From this blog, I am going to explain the Life Cycle of Data Science.
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What is Data Science?
It is a mix up of different tools, algorithm developments, and technology to solve analytically difficult problems. The main aspect of Data Science is uncovering finding from data. Understand complex solutions, interfaces, and trends. The especially hidden insight that helps the organization to make decisions very smarter for business. Now, I am going to explain why we need Data Science? with some examples.
In previous days we have structured data and the size is very small. We could analyze these data by using simple BI tools. These BI tools are enough to analyze the data. But, now it is not possible with simple BI tools. Because of most of the data unstructured, we can analyze with BI tool is difficult. Most of the 80% of the data is unstructured. The data is generated from different sources like multimedia forms, sensors, text files, and instruments. This is the reason why we can't use small BI tool. We need more advanced and complex analytical tools to draw a meaning data which can understand by organizations. To make smart decisions for the business.
Let us take a different example to understand the Data Science for decision making. The car has the self-control to drive and collects live data from cameras, sensors, radars, and lasers. It can create a map of its surroundings. Based on this data, it will take decisions. Let's take another example of weather forecasting. With Data Science we can forecast the result not only data from ships, radars, satellites but also it collects information regarding the occurrence of any natural calamities. Finally, after completion of some examples take a look on Life Cycle of Data Science.
Life Cycle of Data Science
I will give a small overview of data science life cycle
Business Understanding:
It is not an easy thing to understand the data. They were some peoples in the company who ensure that every decision made in the company is supported by data and guaranteed to get output. Before going start the process, we need some information on how to solve the critical issues. Understanding the power of data, and how to utilize this data for these question. For these problems, the solution will come from the experience. But, One shortcut is there to gain the experiences learn what other peoples say about the topic. This shortcut may use to get some experiences to solve critical issues.
Data Mining:
It is a process to collect the data from different resources. I think most peoples have some doubts like what data do I need? How can I obtain it? so, many questions are running in your right! Finding data is take more time and effort. Suppose our data is present in the database, our work is very simple. Otherwise, we need to search the data and scrap. By using SOUP scrap the web pages for data.
Data Cleaning:
This process will take 50 to 80 percent of their time. Because there are so many cases for cleaning the data. Missing data causes a lot of errors, we should be very careful about data cleaning.
Data Exploration:
After we got a sparkling set of data then start the analysis process. This is a brainstorming stage for data analysis. In this process, we can analyze the random subset of the data by using Pandas.
Featured Engineering:
It is a process to solve the business problem. By using domain knowledge we can transform the data into an informative feature. This process represents the business problem.
Predictive Modeling:
This model means the machine learning finally comes into our data science project. Some experts, use this model because to get the good result. And, comprehensive statistical methods and tests to get the outcomes.
Data Visualization:
It is the most difficult task because, it seems very simple but, it is the difficult thing to do best. We can combine the data from all field and indicate with a different key in the project. So, that understand the project very easily. I hope you get some idea from this blog Life Cycle of Data Science.
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