Mining Your Data Using Data Science

By Lillian Pierson

In the era of big data, it seems like organizations of all shapes and sizes are on a hiring quest. They want to hire data scientists so that they can use data and data-informed decision making to add value to their organization and stay competitive. Unfortunately, most organizations and their hiring managers don’t truly understand big data nor the roles that data engineering and data science play in extracting valuable insights from big data.

Data science and data engineering are different animals. Both fields are incredibly complex. You may be able to find someone who has done a little work in both areas, but he’s not likely to be strong in data science if he does complex data engineering, and vice versa.

Data engineering is dedicated to overcoming data-processing bottlenecks and data-handling problems for applications that utilize large volumes, varieties, and velocities of data, whereas data science involves using statistical methods, mathematical modeling, and machine learning methods to derive and visualize deep and valuable data insights. It requires skills in math, statistics, coding for data analysis and visualization, subject-matter expertise, and a solid ability to communicate.

Using data science to extract meaning from data

Mathematical models, statistical techniques, and machine learning methods are all useful when you’re working to derive deep meaning from raw data. Multi-criteria decision making (MCDM) and Markov chains are two types of mathematical decision models that are useful in data science.

Statistical techniques are used throughout data science to do anything from forecasting and predictions to hypothesis validation and parameter estimation. In machine learning, you deploy statistical, mathematical, and even spatial algorithms to learn from large datasets, in order to detect meaningful patterns and relationships from within them.

Types of value you can generate using data science

Now that you know a little more about what data science is and how it’s done, you may be wondering why it’s significant. In a business environment, data science is almost always used for the sole purpose of increasing the bottom line — by either saving costs or increasing revenues. These results can be achieved through many routes, from business-process optimization to customer-churn reduction, from price-model optimization to sales and marketing ROI increases — the possibilities go on and on.

But data science is useful for more than just increasing earnings. It’s also being used in civic, humanitarian, and environmental efforts, to save or improve human lives and to protect the environment from future harm.