Machine Learning & Data Analytics Life Cycle: What’s the Difference?

Many people get confused when it comes to the Data Science life cycle and Machine Learning life cycle. Are they the same? Are they different? How similar or different these technologies are? And many such questions pop-up in their mind. 

Well, there is a good reason to get confused as both these technologies fall in the same domain. Yet, both these technologies have specific meaning and application of their own with a few overlaps. 

Data Science and its Scope

 Data Science is a stream of learning with a wide range of data systems and processes. The general aim of Data Science is to maintain data sets and derive meaning from them. Data tools, algorithms, tools, and principles are used to gain insights from random data sets. Digitalisation has taken the world by storm.

This has resulted in the creation and collection of a vast amount of data. With so much data everywhere, it becomes difficult to store, manage, and monitor it. The ever-growing data sets are managed by using data warehousing and data modelling. The analysis and information collected by the application of Data Science are used to assist decision-makers in reaching business goals.

Business intelligence is a stream that gets directly influenced by Data Science. Data scientists perform the initial analysis on huge data chunks and produce analysis in terms of patterns and more. They generate reports to be understood and used by business intelligence experts. 

Business intelligence experts pick up the work done by data scientists and present a course of action and relevant forecasts based on the information shared by them. 

Another relevant role is a business analyst. It is a combination of data scientists and business intelligence experts. They understand both sets of skills.

 Multiple formats of data analytics are used by data scientists to analyse data. Two such formats are Predictive casual analytics and Prescriptive analysis.

Read: Career in Data Science

Predictive Analysis

It is the branch of the data analytics used by data scientists to forecast future business events. In this data analytics life cycle, a data scientist uses many techniques, including data mining, statistics, modelling, Machine Learning, and artificial intelligence. These technologies help them to derive insights from the given data and make predictions about the future.

This helps organisations in becoming proactive by anticipating future behaviour or outcomes based on Data Science instead of an assumption or hunch. The seven steps of predictive data analytics life cycle are defining a project, collecting data, analysing data, doing statistical analysis, predictive modelling, predictive model deployment, and model monitoring.

Prescriptive Analysis

It is a branch of data analytics used by data scientists to prescribe a set of actions based on predictive analytics, which are most likely to succeed. It uses the insights/ inferences from the predictive model and helps companies by providing the best possible ways to achieve business goals. It automates a complex decision and provides updated recommendations.

Data scientists use a wide range of data-oriented technologies like Hadoop, Python, R, and SQL. Extensive use of techniques like data visualisation, distributed architecture, statistical analysis, and more are also done to obtain useful inferences from data sets.

During the life cycle of any Data Science project, these skilled professionals wear multiple hats and switch roles, as per the project requirement. They can work with AI (artificial intelligence) and ML (Machine Learning) with equal ease. Many times, they need Machine Learning skills to perform various tasks like pattern discovery and predictive reporting.

Machine Learning is used to set parameters in data reports. Clustering is one of the most popular algorithms used for pattern discovery.

Machine Learning and its Various Components

Machine Learning is a part of artificial intelligence. Machine Learning is a technology, which means that machines/ devices can learn and improve automatically from experience. This technology is primarily about independent learning methods for machines, so they don’t have to be programmed for continuous improvement. 

Machine Learning means analysing data to recognise patterns and establish logical reasoning based on inferences. The four critical components of Machine Learning are supervised Machine Learning, unsupervised Machine Learning, semi-supervised Machine Learning, and reinforcement Machine Learning. 

Supervised Machine Learning

Supervised Machine Learning creates a model that predicts based on evidence during uncertainty. It takes a recognised set of input data and a recognised set of output data. Based on the behaviour of these historical data sets, it instructs a model to produce logical predictions for the response to unrecognised data. They play a vital role in mapping the input-output pair. Learn more about types of supervised machine learning.

Unsupervised Machine Learning

As the name says, it is a Machine Learning process that requires minimum to no human effort. Unsupervised Machine Learning algorithms use unclassified or non-labelled parameters to discover patterns and trends. These algorithms use clusters, anomaly detection, neural networks, and more. Learn more about unsupervised machine learning.

Semi-supervised Machine Learning

It is a combination of supervised and unsupervised Machine Learning. It utilises classified as well as unclassified data to derive more accurate insights. It is considered to be a cost-efficient solution when labelling or classifying data is an expensive procedure.

Reinforcement Machine Learning

If you have ever played Mario, then you must know that you have already experienced the rewards of reinforcement Machine Learning. Reinforcement Machine Learning helps in understanding the best possible way to attain an intricate objective after multiple steps.

What is the difference between Machine Learning and Data Science?

Data Science and Machine Learning are two different domains of technology. They both work on different aspects of a business. Data Science uses data to help companies in understanding the trends and predict behaviours. Machine Learning enables devices to self-learn and executes various tasks.

Since these both technologies are interconnected, a basic knowledge of both is required to apply any of them for business growth and development. Data Science is already an integral part of almost all the companies, while demand for Machine Learning is growing at a rapid pace. Both technologies are going to be highly relevant and useful for companies in the coming future. 

Both the technologies and skills are highly in demand. Many young professionals are keen on learning these skills. They get confused between a wide range of courses offered by various institutes. It is crucial to understand and analyse your current skill set to decide which skill can propel your career upwards.

When selecting a certification, course or degree, it is essential to consider the time you can dedicate towards the learning. For young students and professionals, who want to keep earning or start earning quickly, short-term courses are more suitable. Those who can focus solely on learning for a year or more, a complete degree is a better option. 

upGrad’s Online courses

upGrad offers a lot of short-term and full-time courses. It is an excellent online education platform, especially for the popular, high-demand professional skills. It offers courses like- 

Hope this helps you understand and comprehend both the cycle of Machine Learning and Data Analytics.

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