AI and related technologies have been widely embraced in recent years as the value of AI and similar technologies for business has grown. Artificial intelligence (AI) and associated technologies have begun to have an impact on and change the operational, functional, and strategic aspects of numerous industries. The Executive Post Graduate Certificate in Artificial Intelligence and Machine Learning is designed to upskill participants in this in-demand field, allowing them to take advantage of job prospects.
In today's society, a PG Diploma in machine learning and AI is a popular subject. It is clearly geared toward working professionals, so you won't have to leave your existing employment to complete the programme and receive certification. This course is offered by a number of reputable institutions.
Some of the primary advantages of a PG Graduate Program in Machine Learning and AI are listed below:
Machine learning, like almost any other business strategy, is most effective when it has a big financial impact. Many businesses employ data analytics teams to help them enhance supply chain management, sales operations, and cost control. However, just one out of every twelve people is currently monetizing data to its full potential. That is, they are investing in machine learning experts and data science technologies, but they are not generating enough income to sustain such efforts in the long run.
The data science job field's next big frontier is monetizing data. A recent paper from MIT's Sloan School of Management highlighted several examples of how businesses are monetizing data science to improve operations (internal procedures) and client services (external-facing processes). Geo-targeted clients for retail and tourism, fraud detection for financial institutions, smart targeting and click-stream information for digital marketers, and Internet of Things (IoT) applications to boost income are some of the areas these companies are focused on.
More specifically, John Deere introduced a new stream of revenue by providing farmers with data analysis tools such as crop insurance estimators and yield and risk management projections.
However, not every firm has a fully staffed internal working environment of machine learning experts to create the insights required to optimise operations, lower costs, and monetize data. Many businesses are now outsourcing their data intelligence requirements, resulting in the growth of the "insights as a service" market. Currently, 66 percent of businesses outsource between 11 percent and 75 percent of their business intelligence applications, and Forrester estimates that as insight subscriptions gain traction, the market for insights-as-a-service will double.
To facilitate shipping fuel savings, one insights-as-a-service company developed a decision-support model for ship operators. Their advanced analytics-enabled mobile app also provides a financial and performance benefit analysis of ship coating options in order to help investors make better selections. Insight-as-a-service will be used to supplement companies' internal data science capabilities.
Data science is growing increasingly important in the workplace, to the point where 57 percent of companies now have a dedicated Chief Data Officer (CDO) on their management teams. This year, more than half of those CDOs would report to the CEO, up from 40% the year before. The rising star of data science is bringing data scientists to the executive table. In a recent poll, 85 percent of respondents said their companies have initiated efforts to establish data-driven cultures.
Chief data officers and data scientists are in charge of instilling a data-driven culture, disseminating data insights across various lines of business, and discovering innovative methods to use data to improve company processes, products, and services.
Data science has become synonymous with artificial intelligence (AI) and machine learning, and these technologies are supercharging the subject. According to Indeed, nearly 75% of data scientist jobs used the terms "AI" and "machine learning" in their job descriptions. In the last three years, the need for workers with AI expertise has more than doubled. Data scientists that concentrate in AI and data science and machine learning techniques will have an advantage in the job market and will be able to secure more senior data science positions.
The top two paying AI jobs, according to Indeed, were not AI-specific titles (such as AI engineer), but rather data science titles: director of analytics and senior scientist. What is the most widely used programming language for artificial intelligence and machine learning? Python is the programming language. According to Indeed's on-trend search results for AI and machine learning, Python is the most popular language.
Conclusion
The number of opportunities for newly minted data scientists is rapidly increasing. And whether you want to lead and monetize a data science endeavour, provide third-party insight services, or advance your career in data analytics, there's a unique path to mastering data science just waiting to be discovered.