Westchester Community College, State University of New York

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision-making. The goal of “R Programming for Data Science” is to help you learn the most important tools in R that will allow you to do data science. As you progress through this course, you’ll learn how to approach a variety of data science challenges, using the best parts of R.

Data is one of the important assets in every organization because it helps business leaders make decisions based on facts, statistical numbers and trends The importance of data science is based on the ability to take existing data that is not necessarily useful on its own and combine it with other data points to generate insights an organization can use to learn more about its customers and audience.

Today’s data science teams are expected to answer many questions. Business demands better prediction and optimization based on real-time insights

With the volume and variety of social, mobile and device data, along with new technologies and tools, data science today plays a broader role than ever before. Business considers data science and AI to be a technology-enabled strategy.

The short answer is yes. Data science is one of the fastest growing fields today and is expected to continue into the next decade. As most of the fields are emerging continuously, the importance of data science is increasing rapidly. Data science has influenced various areas. Its effect can be observed in multiple sectors such as the retail industry, healthcare, government, financial and education.

It has become an important part of almost every sector. It provides the best solutions that help to fulfill the challenges of the ever-increasing demand and maintainable future.  As the importance of data science is increasing day by day, the need for a data scientist is also growing.  If you have the skills, there are jobs available not to mention those currently in technical careers (e.g. programming) climbing the career ladder with additional skills such as a data science practitioner.  

As the growth of data accelerates, so does the importance of data science and the teams of data scientists formed to turn this data into useful information, insight and knowledge.   While companies prepare for big data integration, business leaders need to adapt their roles as team leaders for their data science employees.  Your data science team should have the expertise to process data with freedom, but business leaders still need to understand the basic structures of what’s happening to create value from that data.  

Why is this important for you or your organization?   A New Era of Business Leader
Put into context in today’s business environment, there’s no situation where it’s okay to say as the leader, I don’t know what’s going on but my team does and that’s good enough.  Yet many business leaders don’t know the most basic principles of data science.  Business leaders (managers, directors, executives, vice presidents, etc.) don’t need to know the intimate details of data science processes but as the line between big data and business operations disappear, it’s more important than ever for business leaders to speak (understand) a little data science.  This translates into to having some basic foundational knowledge. 

Why it’s important to understand the basics:
Data science can be good storytelling but it is still science.  Telling a story can often obscure the facts or make links where there aren’t any.  Having the foundational knowledge or basic proficiency can help you avoid:

• Getting taken - manipulating the data, not telling the whole story, targeted information gaps, all this things could make it easier to coerce or persuade you into a bad decision
• Asking the wrong questions – data pulls are only as good as the questions you’re asking. Data must be evaluated regularly and that requires starting with the right question(s).
• Replicating bias – data is neutral, but it’s aggregation and results are often the product of our preconceived ideas.  Understanding the basics of data science helps you sort our the messiness of data in the real world.