Decoding Data Science: A Useful Guide to Basic Terminology (Part 1)

Are you new to the world of data science and feeling a little overwhelmed by all the jargon and technical terms? Don’t worry, we’ve got you covered! In this 2-part series, we’ll be breaking down the basics of data science terminology to make it easy and accessible for everyone.

Today, we’ll be exploring five key terms that are essential to building a solid foundation in data science.

  1. Data Science

The field of combining programming, mathematics, and statistics to analyze data and provide insights for decision making and problem solving. Data Scientists have high-level technical skills and use various programming languages to collect, interpret, format, model, predict, and manipulate data.

  1. Algorithm

A set of repeatable instructions, usually expressed mathematically, used to process data. Algorithms can be constructed by humans or machines and adjusted until the desired result is achieved. Machines are now commonly used due to advancements in Artificial Intelligence.

  1. Data Analytics 

The process of using existing information to answer questions for better business decision-making. It involves continuous collection and analysis of data, with a focus on ensuring data integrity and accurate evaluation of research results.

  1. Data Mining 

The process of sorting large data sets to identify patterns and relationships that can help solve business problems. It is a component of Data Analysis and one of the core disciplines of Data Science. Data mining techniques and tools can be used to predict future trends and make more informed business decisions.

  1. Big Data 

A term used for data that is too large and complex for traditional data management tools to store and process efficiently. Big Data can provide more complete and precise answers by confirming information from multiple data sources.

Understanding these key terms is essential for anyone looking to get started in data science. Stay tuned next week as we continue to explore key concepts and terminology in data science.

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Daniel Meyer is the head of Sonic Analytics, an analytics firm that has been in the Big Data industry for over 20 years and has offices in Manila, the San Francisco Bay Area, and Ocala, Florida. He is an accomplished author, public speaker, and business expert specializing in virtual staffing and process automation.

Dan is known for providing big data analytics solutions, including business intelligence and data storytelling, to small businesses seeking to improve their use of data, virtual staffing, and technology. He strongly believes in using analytics for civic responsibility, and offers training, consulting, and education to promote this advocacy.

With his experience in training over 10,000 Filipinos, Dan is passionate about empowering the youth with valuable skills, such as graphic design, video editing, and data analytics. His objective is to equip them with the necessary abilities to harness the dynamic employment opportunities that lay ahead for millions of Filipinos.


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