Introduction to data science

This blog post serves as a brief overview and introduction to data science. Data science is the study of data to understand the world. In essence, data science is the science of data. Similar to how the natural sciences, such as biology, chemistry, and physics, describe the natural world. Data science involves big data and is used in many industries.  Data Science, for example, is used to prevent water toxicity by predicting algae blooms. Another application is water treatment, where data science safeguards the water supply. Using data collected from sensors, data science can identify bottlenecks and inefficiencies in the water treatment process.

What is big data?

According to IBM, big data refers to the dynamic, large, and disparate volumes of data being created by people, tools, and machines. Big data requires new, innovative, and scalable technology to collect, host, and analyze the vast amount of data gathered to derive real-time business insights relating to consumers, risk, profit, performance, productivity, management, and enhanced shareholder value. Big data was started by Google when Google tried to figure out how to solve its PageRank algorithm. Sergey Brin and Lawrence wrote a page titled “The Anatomy of a Large-Scale Hypertextual Web Search Engine” while they were graduate students at Stanford University.

Mathematical regression

Mathematical regression is one of the main tools used in data analysis. Regression shows a probable relationship between two variables. Breaking the third wall, I studied regression in many courses in both my bachelor’s degree and master’s degree programs. Generally, in engineering, regression involves trying to match data using a polynomial. The higher the polynomial degree, the more accurately the polynomial equation matched the data. This was generally done in Microsoft Excel(because my university didn’t generally keep up with new technologies) but can also be done in programming languages like R and Scala.

With the advances in artificial intelligence, data scientists commonly use machine learning. A subset of machine learning is deep learning. Deep learning is a specialized subset of machine learning that uses layered neural networks to simulate human decision-making.  Neural networks rely on large data sets to train models to learn from examples, solve problems independently, and make accurate predictions.

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