20 Common Data Analytics Terms Defined
If youâ€™re interested in data analytics, youâ€™ve probably noticed that much of the literature about analytics is full of unfamiliar terms and acronyms. Data analytics terms can seem complicated and confusing, but if you master some of the fundamentals, youâ€™ll understand the literature on a deeper level. While the following is not a comprehensive list of data analytics terms, it should cover the basics and help you get started on your data analytics journey.
The Fundamentals of Data Analytics
There are certain terms that relate to fundamental data analytics topics. It would behoove the interested learner to master these terms first since they are related to concepts that form the foundation of data science literature and research.
When you purchase something that needs to be assembled, such as a bookcase, the item comes with instructions and components. The instructions help you translate those components into a useful piece of furniture. Algorithms are instructions that tell computers how to assemble values into a usable form. Algorithms can be very simple or build very complex predictive equations. While they vary in size, complexity, and utility, they are always a set of instructions.
There are also various types of algorithms. For example, fuzzy algorithms use fuzzy logic to decrease a scriptâ€™s runtime. While they are less precise than Boolean-based algorithms, they are faster. Their increased speed makes them more useful in situations where precision is less important than efficiency.
Greedy algorithms work by breaking problems into sequential steps. Then, they look for the best solution for each individual step. At the end, it can analyze all of the step-specific best solutions to deliver an overall solution that can best solve the original problem.
Training, Testing, and Fitting in Data Analysis
Machine learning refers to a process computers use to learn and understand a set of data in order to make predictions based on that understanding. While there are many forms of machine learning techniques, most fall into supervised or unsupervised classifications.
When youâ€™re utilizing machine learning, you need to test and train the model of predictive analysis thatâ€™s in play. Training comes first in the machine learning workflow. In this stage, youâ€™ll offer the model a set of training data
Fitting refers to how much information you offer a model. Underfitting occurs when you offer a model insufficient information. For instance, if you want to track fluctuations in rainfall during a day but you only give the machine the highest and lowest values, that would be an example of underfitting. Where you might expect a bell curve of some kind, youâ€™ll get a straight line. Overfitting, then, is giving the machine too much information. When you do this, itâ€™s like asking someone to read a book using a microscope. All the patterns that make the task possible disappear.
Types of Machine Learning Problems in Data Analytics
Classification is one type of machine learning problem. It is a supervised problem and works to categorize, or classify, a particular data point by determining its similarity to other data points in the set. This tends to be a problem where all the data points have been assigned to a category except for one. The machine then looks at the common traits among those data points that have already been assigned to categories and assesses the traits of the new data point to see where it might fit in.
Regression is also a supervised problem which focuses on how one specific data point changes when other values in the set change. This kind of problem is usually appropriate for situations such as determining how certain factors affect home prices in a given area and other problems that include continuous variables.
Data Analysis Workflow Components
Now that you know a handful of basic terms, itâ€™s important to know what a typical data analysis workflow looks like. While every process is necessarily different, there are several workflow components that tend to be present in every data analysis workflow because they are so critical to the process.
In data analysis, data exploration is the stage where analysts try to establish the context of the data they’ve collected by asking basic questions. The result of this stage provides foundational knowledge to which the analyst will refer frequently in order to obtain guidance for more in-depth analysis. Having an understanding of the data context can also help an analyst notice when particular results are surprising or warrant further investigation.
Data mining is a part of the analysis process that involves extracting meaning, patterns, and connections from a set of data. Some of the activities an analyst might engage in during this stage include cleaning data, organizing data, finding patterns and connections among data points, and even communicating the insights gleaned from the mining process.
Data Wrangling or Data Munging
As the name implies, data wrangling or data munging is the part of the data analytics process involves taming the data. Taming is a way to describe the process of manipulating data until it works better in a broader project or workflow. This could include ensuring that values are consistent with a comparable, larger data set, replacing or removing values that might affect performance at a later time, or otherwise adversely affect the analysis.
Data pipelines help data analytics processes flow together efficiently. Essentially, these pipelines are a collection of functions or scripts that pass data along in a prescribed series so that the output of a certain method becomes the input of the next. This process typically continues until the data is appropriately transformed, cleaned, or otherwise prepared for whatever project is at hand.
Extract, Transform, and Load (ETL)
The extract, transform, load or ETL process is essential to all data warehouses. Typically, ETL systems are designed and created by data engineers. They are created to run behind the scenes. ETL describes the three stages that data goes through as data analysts compile it from various sources. Extracting the data, transforming it, and loading the data all help prepare it for use and transfer its raw form to an on-screen display where itâ€™s usable and ready for data analysts.
Sometimes, itâ€™s necessary for an analyst to pull data from the source code of a website. This is referred to as web scraping. Typically, this step is done by writing a script thatâ€™s programmed to seek out specific information as decided and indicated by the person writing the script. The script will run, find the information, and pull it onto a file. The data analyst can then use that information at a later date and conduct analysis on it as necessary.
Machine Learning Techniques in Data Analysis
Weâ€™ve talked about machine learning problems briefly, but what are the techniques used in machine learning? The following terms and concepts can help you understand some of the basic machine learning techniques that exist.
Neural Networks are very loosely based on the brain and the interconnected neurons that exist there. In a machine learning context, a neural network is a system of nodes that are connected to each other and are petitioned into three distinct layers: input, output, and hidden layers. There can be, and usually are, many hidden layers. These layers run the predictive features of the machine. Values in the data set are filtered from one layer to the next through these nodes, or
As the name implies, clustering techniques are attempts at categorization. They seek to group, or cluster, sufficiently similar sets of data points or points that are close to one another. What constitutes similarity or closeness largely depends on the project and how distance is measured for the purposes of it. The more features a data analyst adds, the more complex the problem becomes. Clustering techniques help machines learn by telling it what features belong together.
Decision trees are useful when the data analytics problem at hand deals with branching questions or observations. In this machine learning technique, the branching questions and observations the machine makes are used to develop predictions about a given target value. Since data sets tend to grow very large when using this method, overfitting tends to occur. To minimize overfitting, random forest algorithms can be put into place.
The process of taking human knowledge and translating it into the quantitative language of computers is referred to as feature engineering. Any image you see on a computer monitor is an example of this. Someone had to take a picture of, say, a giraffe and translate the image into a numeric representation using pixels with varying intensities. Taking information and translating it into a form that computers can understand is a critical task that data analysis activities could not be performed without conducting.
Deep learning models rely on neural networks. In a deep learning context, these neural networks are extremely large and are called â€œdeep netsâ€. The beginning stages of the model deal with basic pattern recognition. As the model advances, complexity builds along with it. If the model is successful, the end result will be a net with a very nuanced understanding of the data. It will be capable of accurate value prediction, classification, or both.
While this is by no means an inclusive list of all the terms you need to know in data analytics, itâ€™s a good start. All of these terms will help those who want to work in data analytics understand the literature. These concepts are used daily and help data analytics professionals do their jobs efficiently and accurately. Understanding basic terminology will help you get more out of what you read, watch, and learn. As you learn more about each term here and continue to build your understanding of the field, youâ€™ll realize how interconnected many of these concepts are, so your learning will compound until you have a working knowledge of the basics — and beyond.