There is a buzz among the students regarding the terms like machine learning and big data. Machine learning and Big Data are both technologies are derived from Data Science. Although there are many differences between them, many students get confused. This is not wrong to get confused because both these technologies are from a similar field. Choosing a career in either of these fields needs a clear vision and understanding of the topic. In this article, we will discuss the various aspects where machine learning and big data are similar and where they are different. Apart from this, we will compare the career options for both fields.

What is Machine Learning?

Machine learning is a process of teaching computers to identify patterns in data and make predictions or choices based on those patterns. Large volumes of data are generally sent to the computer to find patterns and make predictions about upcoming, unforeseen data. Experts can use each type of machine learning to address different types of problems. Let’s discuss the different types of machine learning −

• Supervised Learning − It requires training a specific model on the labeled dataset where the right outputs are known. Then, that particular model makes guesses on new and unseen data. Regression, decision trees, and support vector machines are instances of supervised learning.

• Unsupervised Learning − It requires training a model on the unlabeled set of data where the right output is unknown. The models have to search for patterns or structures in the existing information on their own.

• Reinforcement Learning − This type of machine learning involves training a model to make resolutions in the environment by performing the actions and getting penalties or rewards. The model learns to maximize its rewards over time. It is often used in robotics and gaming.

What is Big Data?

Big Data is a field in data science that is made up of two terms. If we split the term “Big Data”, there are two terms that are Big and Data. Data is simply a piece of information (in the form of text, images, videos, etc) that has some meaningful knowledge. Now, coming to Big Data is defined as a collection of a very large volume of data. These data keep increasing exponentially with the passing of time. These data are not easy to manipulate, manage, and process with traditional methods and data management tools. Some examples of big data are the New York Exchange which needs to process 1 terabyte of data daily. Social media such as Facebook needs to process approximately 500 terabytes of data daily. These are some famous fields where Big Data is needed.

There are three types of Big Data which are as follows −

Structured

The data that are generally static and it is stored in a fixed format is known as Structured data. With the passing of time, computers are now capable of dealing with such kinds of data. For example, A spreadsheet is created within a computer.

Unstructured

The data that are generally dynamic and it is stored in an unknown format which changes from time to time as managed by the organization is known as Structured data. For example, the results that we get after searching some keyword on Google search.

Semi-structured

It is a mix of structured and unstructured data, for example, the database DBMS.

Career Options for Machine Learning

Various career roles that a machine learning enthusiast can pursue are as follows −

Machine Learning Engineer

They are responsible for planning, designing, and deploying the ML models. They work in coordination with data scientists and software engineers to ensure the smooth development of machine learning models.

Business Analyst

They are responsible for analyzing the data using machine learning specially fetched from business culture and making predictions about the upcoming trends of the business. They work with the business management to understand the management scenario that helps them in improving business performance.

A.I. Engineer

They are responsible for making and deploying AI systems including machine learning models, computer vision systems, and natural language processing systems. Their scope of work includes finance, health care, and many more.

Data Scientist

They are responsible for the collection of large amounts of data, analyzing and interoperating them to observe the patterns of data that help them to make decisions regarding them.

Computer Vision Engineers

They are responsible for understanding and interpreting visual data using machine learning and making them enabled for computers to understand them.

Career Options for Big Data

Data Scientist

Data scientists have knowledge of Python, Ruby, Matlab, etc with a database management system that helps them to work with large datasets. They provide statistical and analytical solutions to the organization.

Big Data Engineer/ Developer/ Architect

They are responsible for designing and developing warehouses of big datasets. They must have knowledge of Oracle or MySQL databases with the concept of data warehousing.

Big Data DBA

They are involved in the installation and configuration of the Hadoop ecosystem. They have knowledge of database, security, and disk management concepts. They are also involved in the upgradation of Unix or Hadoop systems.

Big Data Admin

They have knowledge of operating systems such as Linux, Hadoop principles, and some scripting languages. They provide planning and infrastructure suggestion to the organization.

Production Support

They are good at shell scripting and Hadoop ecosystem technologies. They work for cluster maintenance, data recovery, investigation, and operations management.

Salary Compensation

In India, the annual salary received by a machine learning engineer ranges from 3.0 lakhs to 21.0 lakhs with an average of 6.8 lakhs per annum, and the annual salary received by a Big data engineer ranges from 3.8 lakhs to 20.9 lakhs with an average of 8.0 lakhs per annum.

Conclusion

Both the career options Machine learning and Big data have their own specialty and future scope. Without knowing and analyzing your skills, you can not grow and make your bright future in any field. So, one must be self-aware about their skill and knowledge to pursue any of the career options.