As per my understanding all the three fields i.e. Data Science, Machine Learning and Big Data are interrelated. I just wanted to clear the boundaries of these three fields. Please share your views about the topic. Any example or discussion would be useful.
These concepts are linked together :
- you can use machine learning algorithms to make data science
- you can use data science and machine learning techniques on big data .
Big data : it is when you deal with huge amount of data
Machine learning : field which concerns the implementation of methods which allow a computer to automatically learn from data. These data can often be big data, as there is a lot to discover in this data.
Data science: field which deals with the analysis of data to extract knwoledge. It includes many techniques like data visualization, probability, machine learning, statistics...
Hope I have helped
Dear Mayur Narkhede,
There is a recent article from Foster Provost and Tom Fawcett that talks about the concepts of data science and big data, and that you might find interesting:
Provost, Foster, and Tom Fawcett. "Data science and its relationship to big data and data-driven decision making." Big Data 1.1 (2013): 51-59.
Article Data Science and Its Relationship to Big Data and Data-Drive...
Interesting question, it has various answers. One possible answer is that these are independent fields but techniques of these fields are used in combination to solve various problems. Languages and frameworks of data science supports data analysis and machine learning.
But when we talk about data science, it is combination of advance data management, data analysis and machine learning tools. Data may be big so big data is management and processing strategies are part of data science. Data is collected to perform some analysis on it therefore data analysis is part of data science. All analysis problem or patterns can't be identified with statistical analysis therefore machine learning is used to solve / identify patterns from data.
These concepts are linked together :
- you can use machine learning algorithms to make data science
- you can use data science and machine learning techniques on big data .
Big data : it is when you deal with huge amount of data
Machine learning : field which concerns the implementation of methods which allow a computer to automatically learn from data. These data can often be big data, as there is a lot to discover in this data.
Data science: field which deals with the analysis of data to extract knwoledge. It includes many techniques like data visualization, probability, machine learning, statistics...
Hope I have helped
Data Science and Machine learning are the same. Literally they refer to the same thing although Machine learning sounds more 'geeky' and data science more 'matured'. Big Data is subset of data science where the emphasis is scalability, how we can expand algorithms of machine learning or data science to millions or billions of tuples or records hence the term big data. The concepts in Big data includes hadoop, virtualization, cloud computing etc.
Machine language is about the algorithms and cognitive method to drive meaning form the repository of mixed data i.e its a tool to extract information from the big data reserves and a sub-set of big-data techniques.
How ever the data science is more toward literal meaning of all and every thing with data and can be consider as the main stream of study from which Big data technologies had emerged and so do the machine languages.
data Science (knowledge stream) -> big data (as a technology) -> machine languages(as a tool)
Hello,
Machine learning is one of sources which can create Big Data and Data Science help scientists to analyze Big Data and get the desired results which can be used later for decision making.
Hello,
Machine Learning : are algorithms that try to build a decision rule (a rule that takes an input and try to give the right outpu based on historical data). This estimation is based on the collected historical data in the format (In_1,Out_1),(In_2,Out_2) ...(In_N, Out_N). This rule can be a classifyer, a clustring algorithm or regression method...
Examples: SVM, PAC Bays, Perceptron (In Nearal Networks).
Example of Tools: Apache Mahout.
Big Data: Is the science that try to find solutions to proocess, store and extraction of relevant information in a HUGE quantity of data, that can be relational (well arranges like Excel Sheets or databases) or non-relationales like Tweets, Images, video...
Example of Big Data Tools: Apache Hadoop (free) and Bluemix of IBM.
Data Science: Is a modern Science whose main goal is making best data store architecture (Relational model design and optimization, SQL, PL/SQL, and noSQL) and understand hidden phenomena on non-organized data and also Descriptif and inference statistics for data analysis. That includes also the data visualization.
Some Tools of a data scientist: R, MATLAB, All DB Management...
Data Science is a broad term. Machine Learning are algorithms and it is a part of Data science. Besides machine learning algorithms, data science also includes on statistics, artificial intelligence techniques, mathematics, programming, recommender systems and Business intelligence. Big data is the same data but with huge volume, variety and velocity. Data science and machine learning techniques are applied on data either small or big data. But for big data special big data technologies are used for storing, processing, managing and analysis For example Hadoop, Map Reduce, Spark, Storm, MongoDB, and Cassandra etc.
Machine learning is a subset of AI and data science is predict future from data using Machine learning model. Big data defines as bulk of data in terabytes and petabytes. We use big data find the occurring and recurring real world problem using AI, ML and data science.
As per my views data science landscape covers 3 major dawns such as artificial intelligence , machine learning and deep learning. Big data theme is voluminous data . it helps to produce intelligent solutions for real world problems using the top technologies such as Artificial IntelligenceI , Machine Learning and Data science .
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