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What is Data Science?

Data Science involves the blend of data inference, algorithm development and implementing technology to drive solutions for analytically complex problems. Data is the center of everything. Data is basically raw information, constantly flowing in and stored in the data warehouse which in technical terms is “Data Center.” We can build advanced solutions using data. Data Science involves the various methods we use to generate good business deals in our society.

What is Data Insight?

The whole aspect of data science is all about uncovering findings from data. Diving in at the detailed level of understanding the intricate pattern, trend, and needs of the customer. It’s data insight which helps companies to steer towards smarter business decisions. For example, Amazon Prime finds movie viewing patterns to understand what users find interesting and uses these decisions to decide what movie or TV series to show cast. Data insight starts with data exploration- When asked a question, data scientist, those who study the pattern of data, become investigators. They find leads and understand the pattern with which data moves. A common personality trait of data scientists is they are deep thinkers with passionate intellectual inquisitiveness. Data science is all about being curious – asking new questions, making discoveries, and learning new things. Ask data scientists those who are most gripped with their work about what exactly drives them in their job, and your question will not be met with "money." In fact, their real motivation is being able to use their creativity and imagination to solve difficult problems and constantly indulge in their curiosity. It’s about making an observation but rather uncovering the truth.

Misconception around Data Science

A popular misconception around data science is that it’s all about statistics. While statistics are important and used, it is not the only type of math which is being utilized. First of all, there are two branches of statistics – classical statistics and Bayesian statistics. When most people refer to statistics being used, they are generally referring to classical stats, but knowledge of both types is required and helpful. Furthermore, many inferential techniques and machine learning algorithms lean on knowledge of linear algebra.

Final word

For any company that wishes to enhance their business by generating more data-driven results, data science is the secret sauce. Data science projects can have exponential returns on investment (ROI), both from regulation through data insight, and expansion of data product. But, on the other hand, hiring people who carry this unique mix is easier said than done. There is simply not enough supply of data scientists in the market to meet the requirement (the salary for a data scientist is sky high). Thus, when you manage to hire data scientists, encourage them. Keep them engaged and involved. Give them independence to be their own detectives in tackling problems. In the big picture, drawing insight from a piece of data involves understanding how it fits and helps the organization. In a nutshell, combining statistics, computer science, applied mathematics and data visualization is what data science is all about, and when harnessed, it has the power to convert vast amounts to data into new knowledge.