Introduction
As the world enters big data, the requirement for its storage also increases. However, it was a concern for the enterprise until 2010. At that time, the aim was to create a framework and solutions to store data. Now that Hadoop and a few other frameworks have solved the storage problem, the focus has shifted to data processing. Here, it is the secret sauce. All the ideas you see in Hollywood sci-fi movies can turn into reality through Data Science. Also, it is the future of Artificial Intelligence. Therefore, it is crucial to understand what Data Science is and how you can add value to your business using this technology.
Data Science: Meaning
Data science is the study that deals with vast volumes of data using the latest techniques to find unseen patterns, derive meaningful information, and make business decisions. Also, data science uses complex machine learning algorithms to create predictive models. However, the data used for analysis can come from many different sources, and you may present such information in various formats. In order to know more about this tool, we suggest you go for Data Science Online Training. Such training will help you get familiar with the related concepts.
Life cycle of Data Science
Now that you understand what data science is, let us look at its lifecycle. You can divide the data science lifecycle into five separate stages, each with its own set of tasks:
Capture
The professionals acquire raw and unstructured data during this stage. Under this step, the major components include data collection, input, signal receiving, and extraction.
Maintain
Under this step, you may transform data into a usable format. Data warehousing, data cleansing, data staging, data processing, and data architecture are all part of the maintenance stage.
Process
At this level, data is reviewed for patterns and biases to check how well it will operate as a predictive analytic tool. A few stages of the process are data mining, clustering and classification, data modeling, and data summarization.
Analyze
At this step, you may perform many sorts of analyses on the data. As a result, data reporting, visualization, business intelligence, and decision-making are all part of the analysis step.
Communicate
At this step, data scientists and analysts provide the data in reports, charts, and graphs. Exploratory and confirmatory analysis, predictive analysis, regression, text mining, and qualitative analysis are all components of the communication stage.
Prerequisite of Data Science
Here is some technical terms you should be familiar with before diving into data science.
Machine Learning
Machine learning is the core of data science. As a result, the professionals must be well-versed in machine learning and have a basic understanding of statistics.
Modeling
Mathematical models allow you to make quick calculations and predictions based on what you already know about the data. Modeling is a subset of Machine Learning that entails determining which algorithm is best for solving a given problem and how to train these models.
Statistics
Statistics are at the heart of data science. A firm understanding of statistics can assist you in extracting more intelligence and obtaining more meaningful results.
Programming
To carry out a successful data science project, you must require some level of programming. However, Python and R are the most popular programming languages. Python is particularly popular because it is simple to learn and supports multiple libraries for data science and machine learning.
Databases
An effective data science professional must understand how databases operate, how to manage them, and how to extract data from them.
Major Uses of Data Science
List down below are the major use cases of Data Science:
- It can help to detect patterns in data that appear unstructured or unconnected, allowing drawn conclusions and predictions.
- Tech companies that collect user data can make use of strategies to turn that data into profitable information.
- Also, It is making inroads into the transportation industry, as evidenced by driverless cars. However, it is simple to reduce the number of accidents by using driverless cars. In such a case, the algorithm receives data training, and then you may examine data using data Science approaches, such as the speed limit on the highway, busy streets, etc.
- Through genetics and genomics research, Data Science applications improve therapeutic customization.
Conclusion
Hopefully, you may find this article informative. We have compiled an overview of data science which you may find beneficial. So, if you wish to make a career in this domain, we suggest you enroll in the Data Science Online Training in India. Such training will make you competent in this domain and make you stand apart from the crowd.
