Difference Between Big Data and Machine Learning
Data drives the modern organizations of the world so don’t
be surprised if I call this world a data-driven world. Today’s business
enterprises owe a huge part of their success to an economy that is firmly
knowledge-oriented. The volume, variety, and velocity of available data have
grown exponentially.
How an organization
defines its data strategy and its approach towards analyzing and using
available data will make a critical difference in its ability to compete in the
future data world.
As there are a lot of
options available in the data analytics market these days so this approach
includes a lot of choices that organizations need to make like which framework
to use? .
Big Data
Big data is a term that describes the data characterized by
3Vs: the extreme volume of data, the wide variety of data types and the
velocity at which the data must be processed. Big data can be analyzed for
insights that lead to better decisions and strategic business moves.
Core skills needed
for Big Data:
- Programming skills (Java, Python, SQL)
- Analytical skills
- Database skills
- Mathematics and Statistics
- Data structure and algorithms
- Machine Learning
- Parallel programming
- NLP
Check here the BigData Hadoop Topics (FREE PDF) to learn to be a professional hadoop developer.
Machine Learning
Machine Learning is a field of AI (Artificial Intelligence)
by using which software applications can learn to increase their accuracy for
the expecting outcomes. In layman’s terms, Machine Learning is the way to
educating computers on how to perform complex tasks that humans don’t know how
to accomplish.
Core skills needed
for ML:
- Programming skills (Java, Python, R)
- Mathematics
- Statistics and Probability
- Data modelling and evaluation skills
- Strong foundation in API
Check here the Machine Learning Topics (FREE PDF) to learn to be a professional Machine Learning Engineer
Key Difference of Big
Data and Machine Learning
Both data mining and machine learning are rooted in data
science. They often intersect or are confused with each other. They superimpose
each other’s activities and the relationship is best described as mutualistic.
It is impossible to see a future with just one of them. But there are still
some unique identities that separate them in terms of definition and
application.
Here’s a look at some of the differences between big data and
machine learning and how they can be used.
- Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed.
- Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.
- Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can’t see the relationship between existing pieces of data with the same depth that machine learning can.
- Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.
- Machine learning performs tasks where human interaction doesn’t matter. Whereas, big data analysis comprises the structure and modeling of data which enhances decision-making system so require human interaction.
The Future of Big
Data vs Machine Learning
By 2020, our
accumulated digital universe of data will grow from 4.4 zettabytes to 44
zettabytes, as reported by Forbes. We’ll also create 1.7 megabytes of new
information every second for every human being on the planet.
We’re just scratching the
surface of what big data and machine learning are capable of. Instead of
focusing on their differences, they both concern themselves with the same
question: “How we can learn from data?” At the end of the day, the only thing
that matters is how we collect data and how can we learn from it to build
future-ready solutions.
