What Is Data Science?
Data science is a branch of science that studies enormous volumes of data and use cutting-edge tools and procedures to identify hidden patterns, extract valuable data, and make business decisions. Data scientists utilise complex machine learning techniques to develop prediction models.
Data for analysis can arrive from different sources and be provided in various formats.
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The Data Science Lifecycle
Let’s look at the data science lifecycle now that you’ve learned it. The data science lifecycle is divided into five stages, each with its own set of responsibilities:
- Capture: The data capture process includes phases such as data acquisition, data entry, signal reception, and data extraction. This stage comprises acquiring raw data, both unstructured and structured.
- Maintain: Consider data warehousing, data cleansing, data staging, data processing, and data architecture. This stage comprises turning the raw data into a format that may be used.
- Process: Data mining, clustering/classification, data modeling, and data summarization are all steps. Data scientists look at the patterns, ranges, and biases in the produced data to see how beneficial they will be in predictive analysis.
- Analyze: Exploratory/Confirmatory, Text Mining, Predictive Analysis, Regression, and Qualitative Analysis are some methods used to analyze data. This is when the lifespan gets interesting. This stage entails doing numerous data analytics.
- Communicate: Data Reporting, Data Visualization, Business Intelligence, and Decision Making are all things that need to be communicated. The analyses present the studies in clearly legible forms such as charts, graphs, and reports in the last step.
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Prerequisites for Data Science
Before learning data science, familiarise yourself with the following technical principles.
Machine Learning
Machine learning is at the heart of data science. Data scientists need a good grasp of machine learning as well as a basic understanding of statistics.
Modeling
Mathematical models permits you to accomplish quick estimates and forecasts based on what you already understand about the data. Modeling is a subset of Machine Learning that comprises selecting the optimal method for tackling a specific problem and how to train these models.
Statistics:
Statistics is the bedrock of data science. You can drag more intelligence and deliver more relevant outcomes if you have a solid understanding of statistics.
Programming:
A good data science project necessitates some knowledge of programming. The most extensively used programming languages are Python and R. Python is popular because it is easy to discover and helps a wide range of data science and machine learning libraries.
Databases:
A good data scientist should know how databases function, manage them and extract data from them.
Conclusion:
So far we discussed about prerequisites for data science and to know more about process of data mining Data Science domain join Data Science Course in Coimbatore.
Read More: Data Science Interview Questions and Answers