What is data science and its use in machine learning and analysis?
Data science is a very common term, so what exactly does it mean? What is the difference between Data Science and BI? What skills would you need for Data Scientist? How are forecasts and decisions made in data science? Here are some important questions that we will answer here.
First, do we understand exactly what Big Data Science is? It is a mixture of different algorithms, tools and machine learning values for the sole purpose of discovering hidden models from raw data. How is it different from statisticians over the years? The answer is the main difference between predicting and explaining.
Data Science v/s Data Analyst
The data analyst usually explains what is going on simply by processing the data history. Data Scientist only does an exploratory analysis to discover the ideas, however, it uses different advanced learning algorithms that will identify the occurrence of a particular event in the future. Data Scientist analyzes this data from different angles, and sometimes the angles have not been known before. Therefore, data science is mainly used to make predictions and decisions through the use of normative analysis, machine learning and predictive causal analysis.
Prescriptive analytics: Suppose you want the model that has the intelligence to make its own decisions and the ability to modify with dynamic parameters, you definitely need the prescriptive analytics for the task. This new area is about giving good advice. Therefore, it not only predicts, but suggests different actions and prescribed results.
Machine learning to make predictions: Suppose you have the financial company transaction data and want to build the model that will determine your future trend, machine learning algorithms will be the right choice. It is part of a supervised learning paradigm. This is called supervised because it contains data on which you can guide your machines. For example, the fraud detection model is formed using the history of fraudulent purchases.
Predictive causal analysis: Suppose you want the model that will predict the possibilities of an event in the future, you need to request predictive causal analysis. And, if you give money on credit, the likelihood of customers making future payments on time is worrying. Here you can create the model that will perform predictive analysis in your customer's payment history to find out if future payments are timely or not.
Machine learning for model discovery: Suppose you don't have the right parameters to make predictions, you need to find hidden models in a dataset to make meaningful predictions. It is an unsupervised model because it has no predefined grouping levels. The most commonly used algorithm for model discovery is clustering.
Suppose you work in the telephone company and want to establish the network by placing certain towers in the area. You can use grouping methods to find tower locations that ensure optimal signal strength for all users.
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