What is data science and big data analytics?

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Data science is a broad term in itself. It is generally used to mean extracting insights out of data. It could be as simple as predicting the number of customers a business is going to get in the next month based on previous data and it can be as complex as generating models that can automatically detect...
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Data science is a broad term in itself. It is generally used to mean extracting insights out of data. It could be as simple as predicting the number of customers a business is going to get in the next month based on previous data and it can be as complex as generating models that can automatically detect a persons face in a picture. Big Data analytics is quite similar to the traditional data analytics in which you look at the data and understand various trends and patterns in the data and generate reports with them. Big Data analytics is different only because the data volume is huge and because of that, the usual analytics solutions aren't that effective. That's why Big Data analytics is branched out. It involves analyzing huge volumes of data quite fast and generating metrics from that. read less
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I want to learn data science in home itself bcz i dont want much time to take any coaching and also most of the institutes are asking high amount for  training. Pease lemme know how i can prepare myself.

First of all you start leaning following. 1.Database(Sql,Nosql) 2 Python,Pandas,Numpy 3 Basic Linux,Big Data(Hadoop,Scala,Spark) 4. Machine Learning 5. Deep Learning
Vishal
What background is required for data science?
Data scientists typically need at least a bachelor's degree in computer science, data science, or a related field. However, many employers in this field prefer a master's degree in data science or a related...
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Is that possible to do machine learning course after b.com,mba Finance and marketing? 

Yes, you can. But as we know very well machine learning needs some programming fundamentals as well. So you have to go through a little touch up of programming and algorithms.
Priya

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