Till now in my blogs about Big Data, I have acquainted you with different aspects of Big Data, from What it actually means to facts and do’s and don’ts of it. In the previous blog we saw some Big Data Analytics Techniques. Taking the List further in this blog.
- Pattern Recognition
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled “training” data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).
- Predictive Modelling
Predictive analytics comprise a variety of techniques that predict future outcomes based on historical and current data. In practice, predictive analytics can be applied to almost all disciplines – from predicting the failure of jet engines based on the stream of data from several thousand sensors, to predicting customers’ next moves based on what they buy, when they buy, and even what they say on social media. Predictive analytics techniques are primarily based on statistical methods.
See Also: A Beginner’s Guide to Big Data Analytics
- Regression Analysis
This is a technique that take the use of independent variables and how they affect dependent variables. This can be a very useful technique in determining social media analytics like the probability of finding love over an internet platform.
- Sentiment Analysis
Sentiment Analysis helps researchers determine the sentiments of speakers or writers with respect to a topic. Sentiment analysis is being used to help:
- Improve service at a hotel chain by analyzing guest comments.
- Customize incentives and services to address what customers are really asking for.
- Determine what consumers really think based on opinions from social media.
- Signal Processing
Signal processing is an enabling technology that encompasses the fundamental theory, applications, algorithms, and implementations of processing or transferring information contained in many different physical, symbolic, or abstract formats broadly designated as signals. It uses mathematical, statistical, computational, heuristic, and linguistic representations, formalisms, and techniques for representation, modelling, analysis, synthesis, discovery, recovery, sensing, acquisition, extraction, learning, security, or forensics. Sample applications include modeling for time series analysis or implementing data fusion to determine a more precise reading by combining data from a set of less precise data sources (i.e., extracting the signal from the noise).
- Spatial Analysis
Spatial analysis is the process by which we turn raw data into useful information. It is the process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Spatial analysis extracts or creates new information from spatial data.
In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modelling or hypothesis testing task. Statistical techniques are also used to reduce the likelihood of Type I errors (“false positives”) and Type II errors (“false negatives”). An example of an application is A/B testing to determine what types of marketing material will most increase revenue.
See Also: 40 Mind-Boggling Facts about Big Data
- Supervised learning
Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
- Social Network Analysis
Social network analysis is a technique that was first used in the telecommunications industry, and then quickly adopted by sociologists to study interpersonal relationships. It is now being applied to analyze the relationships between people in many fields and commercial activities. Nodes represent individuals within a network, while ties represent the relationships between the individuals.
Modeling the behavior of complex systems, often used for forecasting, predicting and scenario planning. Monte Carlo simulations, for example, are a class of algorithms that rely on repeated random sampling, i.e., running thousands of simulations, each based on different assumptions. The result is a histogram that gives a probability distribution of outcomes. One application is assessing the likelihood of meeting financial targets given uncertainties about the success of various initiatives
- Time Series Analysis
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series data often arise when monitoring industrial processes or tracking corporate business metrics. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Examples of time series analysis include the hourly value of a stock market index or the number of patients diagnosed with a given condition every day.
- Unsupervised Learning
Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution – this distinguishes unsupervised learning from supervised learning and reinforcement learning.
However, unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data.
Data visualization is the preparation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, you can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed.
Big data analytics has been one of the most important breakthroughs in the information technology industry. In fact, Big Data has shown its importance and need almost in all sectors, and in all the departments of those industries. There is not a single aspect of life which has not been affected by Big Data, not even our personal lives. Hence we need Big Data Analytics to manage this huge amounts of Data efficiently.
As said before this list is not exhaustive. Researchers are still experimenting on new ways of Analyzing this huge amounts of Data which is present in a variety of forms whose speed of generation is increasing with time to derive values for our specific uses.