Big data analysis and machine learning are the common terminologies in the field of technology. Both are the sub-branches of data science with some unique identities and differences. Many people also enroll in machine learning courses in Noida to understand the uses of the two technologies.

Below we will try to explain how both of these are different and how they relate to each other.

Big Data

This is a collection of data that is large, complex, and difficult to store when processed with a traditional database. The large volume of data sets may consist of information like customer choices, market trends, and other business details. This information helps an organization make customer-oriented decisions and analyze insights for strategic business moves.

Characteristics of big data can be explained with the help of five ‘V’s:

  • Volume – Total size of data
  • Velocity – Processing data with accuracy and high speed
  • Variety – Various data types, like Structured, Unstructured, and Semi-Structured
  • Veracity – Consistency of data
  • Value – Quantity of useful data extracted

Machine Learning

Automated data processing and decision-making algorithms are known as machine learning. Machine learning keeps improvising itself with growing and changing data streams, thus providing valuable insights to an organization. Basically, machine learning is a part of artificial intelligence and the subfield of data science. So, machine learning describes teaching programs themselves making a job better. 

Some of the machine learning applications in our everyday life may include:

  • Price determination, wait time minimization on various riding apps
  • Detection of frauds in all categories by financial institutions
  • Self-driving cars
  • Real-time price adjustments for online shoppers
  • Personalized product recommendations based on your browsing history

A side by side comparison will be more helpful to understand the major differences between Big data and machine learning.

Big DataMachine Learning
Data UseIt is more of extraction and analysis of information from volumes of dataIt is more of using input data and algorithms for estimating future results
TypesThere are Structured, Unstructured, and Semi-Structured big data typesThere are Supervised, Unsupervised, and Reinforcement Learning types of Machine Learning
Pattern RecognitionIt reveals patterns through classification and sequence analysisIt is one step ahead as it uses the same algorithms to automatically learn from the data collection 
VolumeIt is a large scale data setSmall data set where over-fitting is the problem
PurposeIt provides a way of handling bigger and unstructured data sets using tools like Apache Hadoop, MongoDBIt is the way of analyzing input datasets with various algorithms and tools like Numpy, Pandas, Scikit Learn, Keras, etc.
FunctionIt pulls raw data for patterns to analyze strong decision making for the companiesIt acts like a human by making effective predictions using algorithms
EaseIt is difficult to extract relevant required data as the volume of data sets is highAs it works with limited dimensional data, so it is easier for recognizing features
UseIt is helpful for handling various purposes like Stock Analysis, Market Analysis, etc.It is good to provide virtual assistance, Product recommendations, Email spam filtering, etc.
ScopeIn the future, it can be used to handle large volumes of data, optimizing data storage in a structured format to ease up data analysisIts scope is to improve quality of predictive analysis, faster decision making, robust, cognitive analysis, improved medical services, and much more

So what is the difference?

As described, data science focuses on data visualization and presentation, while machine learning focuses more on learning algorithms with the help of real-time data. 

Remember that you cannot choose either of the two. Both data science and machine learning go close as hand and gloves. Machines can’t learn without data; similarly, data science is good for machine learning. 

The future is expected with data scientists of have a basic understanding of machine learning to interpret big data generated daily and all the time. 

Machine learning is the future of the technology industry. There are several machine learning training centers in Noida, which are also ideal for working professionals. The programs are delivered through an interactive learning model with active customer support. It includes live sessions by global practitioners and masterclasses from top professionals in the field. 

Conclusion

Keep in mind that data is the main focus for data science, while learning is the main focus in machine learning. Big data tells us about all the information, while machine learning describes a way to analyze this data. The one thing that matters most is that how to collect big data and how to learn from it to make future-ready solutions to help grow our organizations. 

It’s best to get enrolled in a machine learning training course in India to understand more about it and become eligible for a related job in the field.

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