Data science and machine learning are buzzwords today across industries and organizations. With the amount of data created every second of every day, there has been an increase in the need for business graduates and working professionals with skills in the various aspects of data, which has led them to seek certification courses in data analytics. Top universities and institutions have responded by offering data analytics courses online for the convenience of working professionals to learn the skills while still working.

 

Often, people tend to think of all these buzzwords as synonymous. In fact, they are not; they are vastly different.

Data Science vs Machine Learning

Data Science is a combination of tools, techniques, algorithms, processes and systems that are used to clean, prepare and align data and thereafter, extract information and insights from unstructured data. Using such meaningful insights and information, data scientists build further algorithms and products to meet the needs of their clients to fulfill their business objectives.

 

Machine Learning (ML) is a set of techniques and tools that provide computers with the ability to observe large sets of data, learn patterns, improve its own knowledge systems and use the learnings for better decisions and actions in the future without human interference or explicit programming in every step. ML can effectively analyze and large volumes of data faster and can provide accurate results for businesses to improve their tangible and intangible outcomes.

 

Why is ML an integral part of data science?

One question automatically arises then, “Will data scientists become redundant with the rise of machine learning, considering computers can automate so much, do forecasting for businesses, etc.?” No. Data scientists will not become redundant, the nature of their work may change but the demand for their services will only keep increasing because machine learning is an integral part of data science and can be leveraged by data scientists for better outcomes. For instance, automated cars use machine learning algorithms to operate based on the objectives (safe driving, lesser accidents, etc.) fed into them. But data scientists will be required to feed in these needs and objectives. There is also a need to know what potential customers look for in these cars and personalize marketing campaigns for them, which is the duty of data scientists. Data scientists will be required to provide advanced data solutions for complex business goals and problems.

 

Today, the volume of data is humongous, and it is extremely difficult and time-consuming to use manual instructions and modes for developing algorithms or unearthing patterns. ML works well with large data sets and saves time, energy and resources by unearthing patterns, trends, etc. even with abstract objectives are fed in. Data scientists can use these insights to aid better strategizing and outcomes for the business. Data science together with ML also works wonders with predictive analyses, predicting phenomena in advance and helping businesses be better prepared for these.

 

Modern data scientists are required to be adequately trained in Machine Learning to be accepted as qualified specialists. So, enroll yourself in a certification course in data analytics today and forge ahead of the crowd.

Want to know how can this course help in your profile?