Why Data Science is Important?

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

Data Science + DataRobot

Ramping up data science efforts is difficult even for companies with near-unlimited resources. The  democratizes data science and AI, enabling analysts, business users, and other technical professionals to become Citizen Data Scientists and AI Engineers, in addition to making data scientists more productive. It automates repetitive modeling tasks that once occupied the vast majority of data scientists’ time and brainpower. t bridges the gap between data scientists and the rest of the organization, making enterprise machine learning more accessible than ever.

The emergence of artificial intelligence (AI) has played a key part in ushering in the Fourth Industrial Revolution. According to the World Economic Forum, “it is disrupting almost every industry in every country.”

“We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before.” – Klaus Schwab, Founder and Executive Chairman, World Economic Forum Geneva

Artificial intelligence is a conglomeration of concepts and technologies that means different things to different people – self-driving cars, robots that impersonate humans, machine learning, and more – and its applications are everywhere you look. The typical definition of AI looks something like this:

Why is Artificial Intelligence Important?

Artificial intelligence systems are critical for companies that wish to extract value from data by automating and optimizing processes or producing actionable insights. Artificial intelligence systems powered by machine learning enable companies to leverage large amounts of available data to uncover insights and patterns that would be impossible for any one person to identify, enabling them to deliver more targeted, personalized communications, predict critical care events, identify likely fraudulent transactions, and more.

Harvard Business Review gives key insight into how important AI is in today’s economic environment:

“The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, healthcare, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.”

Companies that fail to adopt AI and machine learning technologies are fated to be left behind:

• Global spending on AI will grow 50% compounded annually and will reach $57.6 billion by 2021.
• Industries like retail, marketing, healthcare, fintech, insurance, and more will all stand to benefit from AI and machine learning.
• Companies driven by insights from data will take $1.2 trillion a year from non-insight driven companies by 2020.
• 83% of early adopters are already gaining value from AI and machine learning initiatives.
• The net gain in jobs resulting from AI adoption will be over 5 million.

Machine learning is a subset of artificial intelligence (AI) in which algorithms learn by historical data to predict and uncover

Why is Machine Learning Important?

patterns not easily spotted by humans. For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more. While machine learning has been around since the 1950s While most statistical analysis relies on rule-based decision-making, machine learning excels at tasks that are hard to define with exact step-by-step rules. Machine learning can be applied to numerous business scenarios in which an outcome depends on hundreds of factors — factors that are difficult or impossible for a human to monitor. As a result, businesses use machine learning for predicting loan defaults, understanding factors that lead to customer churn, identifying likely fraudulent transactions, optimizing insurance claims processes, predicting hospital readmission, and many other cases.