Guest blog post by Mic Farris. Mic is a Decision Science & Analytics Leader at CenturyLink.
Two of the biggest buzzwords in our industry are “big data” and “data science”. Big Data seems to have a lot of interest right now, but Data Science is fast becoming a very hot topic.
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I think there’s room to really define the science of data science – what are those fundamentals that are needed to make data science truly a science we can build upon?
What follows are such a set of fundamentals:
Fundamentals of Data Science
Introduction
The easiest thing for people within the big data / analytics / data science disciplines is to say “I do data science”. However, when it comes to data science fundamentals, we need to ask the following critical questions: What really is “data”, what are we trying to do with data, and how do we apply scientific principles to achieve our goals with data?
Probability and Statistics
The world is a probabilistic one, so we work with data that is probabilistic – meaning that, given a certain set of preconditions, data will appear to you in a specific way only part of the time. To apply data science properly, one must become familiar and comfortable with probability and statistics.
Decision Theory
This section is one of the key fundamentals of data science. Whether applied in scientific, engineering, or business fields, we are trying to make decisions using data. Data itself isn’t useful unless it’s telling us something, which means we’re making a decision about what it is telling us. How do we come up with those decisions? What are the factors that go into this decision making process? What is the best method for making decisions with data? This section tell us…
Estimation Theory
Sometimes we make characterizations of data – averages, parameter estimates, etc. Estimation from data is essentially an extension of decision making, a natural next section from Decision Theory.
Coordinate Systems
To bring various data elements together into a common decision making framework, we need to know how to align the data. Knowledge of coordinate systems and how they are used becomes important to lay a solid foundation for bringing disparate data together.
Linear Transformations
Once we understand coordinate systems, we can learn why to transform the data to get at the underlying information. This section describe how we can transform our data into other useful data products through various types of transformations, including the popular Fourier transform.
Effects of Computation on Data
An often overlooked aspect of data science is the impact the algorithms we apply have on the information we are seeking to find. Merely applying algorithms and computations to create analytics and other data products has an impact on the effectiveness data-driven decision making ability. This section take us on a journey of advanced aspects of data science.
Prototype Coding / Programming
One of the key elements to data science is the willingness of practitioners to “get their hands dirty” with data. This means being able to write programs that access, process, and visualize data in important languages in science and industry. This section takes us on a tour of these important elements.
Graph Theory
Graphs are ways to illustrate connections between different data elements, and they are important in today’s interconnected world.
Algorithms
Key to data science is understanding the use of algorithms to compute important data-derived metrics. Popular data manipulation algorithms are included in this section.
Machine Learning
No data science fundamentals course would be complete without exposure to machine learning. However, it’s important to know that these techniques build upon the fundamentals described in previous sections. This section gives practitioners an understanding of useful and popular machine learning techniques and why they are applied.
Originally posted here.
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Hi,
Nice blog on the fundamentals of datascience. This is a comprehensive and helpful piece of information.
Vincent, very nice compendium of things "Data Science". Of course on could still add components of data engineering and data management.
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