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Published September 26th, 2017 by

The Future Of Machine Learning And Data Science

Machine Learning And Data Science

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Before analyzing the future of machine learning and data science, let’s check first what machine learning is.  According to definition, Machine learning is a sub division of artificial intelligence (AI) as it empowers computers to arrive at a state of self earning mode but without getting integrally programmed. In other words, machine learning in an intuitive process where a machine gets to learn itself when gets integrated or exposed to new data. As a result of machine learning, a machine can learn, modify, develop by own course in accordance to new data. In other way the competence of machine learning largely depends on data.

Data science is an analytical process where scientific algorithm is employed for extracting valid and relevant information from data. It is somehow similar to data mining process. The success of machine learning largely depends on the input of data, therefore the future of these two is interrelated and to a large extent quite reciprocal.

In a broader sense, a data scientist deals with so many aspects of data management and data science demands wide array of skills, machine learning is one of them. So if data science is the broader field, it’s one of the core integral areas of data analysis, which is executed by machine learning. Technically, machine learning is an intuitive discipline that deals with data deciphering related to statistics, computer-science, as well as mathematics.

Data science Vs Machine learning

The key divergence between data science and machine learning is that data scientists work on big data, invests time and effort for cleaning it up and then fix on the statistical scheme or machine learning algorithm what they were supposed to process the data or further analysis and deriving result.

Machine learning includes techniques for creating algorithms that explore and create decisions based on big data, which is dealt and analyzed by data scientists for deciphering next level strategy/planning/hypothesis, etc.

The future of machine learning

Amidst the hullabaloos and challenges surrounding prospects of machine learning, a few guaranteed trends for the future can be easily predicted:

  • The supply-demand gap in data science and machine learning skilled workforce will continue to go up till adequate academic programs and industry allied workshops will equip and train industry ready professionals.
  • Most of the businesses will hit into algorithmic simulation for their operational and CRM based functions.
  • Patented machine learning algorithms will perform as a key factor in business.

Users can apply machine learning skill to perform both supervise as well as unsupervised techniques. Predominantly, you may use machine learning algorithms to foresee, clustering, categorize data based its type and resource. For instance, machine learning can be used to categorize an email if it is spam or not.

The future of data science

The early use of data science was mostly centered in descriptive analytics, or unfolding what was happened before based on data. But the prospect of data science is expected to create a turning point for advanced analytics—especially use of predictive analytics and real-time analytics is expected to be used for meticulous detection of business goals, such as betterment of customer experience, improvising products and services, custom software solutions and reducing costs and overall investment, etc. It has multifaceted utility and with big data management facility in hand, the prospect of data science is quite promising with the scope of worldwide exposure.


The use of data management is increasing and keeping a pace with this rising demand, data science and machine learning algorithm is earning its high level popularity. As a whole future or both these skill sets is highly demanding in terms of job prospects and industry acceptance. One point is important: it is always important to learn and practice the skill under qualified and industry savvy mentors with hand-on experience in competitive projects.

Ethan Millar

Technical Writer at Aegis Softtech
Having more than 6+ years of experience to write technical articles especially for Hadoop, Big Data, Java, CRM and Asp.Net at Aegis Softtech.

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