Let’s learn Data Science in simple words. In the early 20s, data analysis has been covering quite a lot of ground when it comes to smaller data. As the world evolved into a new ear of big data, they needed a place to store this incredible data. So, in 2010, a platform called Hadoop and other frameworks were developed for the sole purpose of storing this data.
The data stored are not useful without proper analysis. Since the problem with the storage has been solved, processing the data is now the problem. Data Science came around and made a public announcement, “I am the secret sauce capable of processing and analyzing your stored big data. Data Science serves as a vital future for artificial intelligence, AI. So, it is essential to perfectly understand what data is data science and how it can be of help in improving and adding value to your business.
In this article, we will look at a brief description of data Science in simple words and its purpose regarding data manipulation.
What is Data Science?
Data science is the combination of various algorithms, tools, and machine learning guided by a principle to discerning hidden patterns from raw data. If this is the definition of data science, how does it make a difference from what a data analyst has been doing for years? It is all about predicting and explaining.
A data analyst or statistician usually explain a current or recent occurrence by processing available data. Data Science not only helps explain the data, but it also helps in predicting future events using the data available through various advanced machine learning algorithms.
Data Science is fundamentally used in explaining and predicting a particular occurrence in the future while making use of three principal analyses, Prescriptive analysis, Predictive causal analysis, and machine learning.
- Predictive casual analytics: The predictive casual analysis helps you predict the possibility of an occurrence in the future.
- Prescriptive Analytics: This gives your machine more flexibility to make its own decision and modify the decision using dynamic parameters. You can see this analysis as an adviser. It generates a range of actions and associated outcomes based on the data supplied.
- Machine learning for predictions: This type of data science prediction is beneficial in the financial sector when you want to build a model to generate future trends. Predictions are possible when you already have data to train your machine with it.
- Machine learning for pattern discovery: This is useful when you can’t find the necessary parameters to build your model. Instead, your machine will help find meaningful patterns to help make explainable predictions.
How does Data Science generally work?
Data science generally works within a five-stage cycle, which includes:
- Data capture: signal reception, data entry, data extraction or data acquisition
- Maintain: data cleansing, data staging, data warehousing, data processing, and data architecture
- Process: Clustering/classification, data summarization, data mining, data modeling.
- Communication: data visualization, data reporting, decision making, business intelligence
- Analyze: Predictive analysis, Exploratory/confirmatory, qualitative analysis, regression, text mining.
All the options in the five stages listed above are dependent on the skillset, program, and they require different techniques.
In conclusion, data science applies to all industries to create new products and make life around them more comfortable. Some of these industries include but not limited to the health sector, logistics, cybersecurity, Entertainment, and finance.
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