Decoding Analytics Buzzwords
We thought that before starting in detail, let’s become familiar with the jargon commonly used in the analytics world.
This involves forecasting of future probabilities by extracting information from given existing data. The retail sector is one of the largest users of predictive analytics. One of the most popular examples is Amazon’s recommendations system which is based on other similar items that other buyers purchased. So, the next time when you buy something based on Amazon’s recommendation, make sure you appreciate the wonders of Data Analytics.
This refers to analytics using optimization and simulation algorithms to advise on several different possible actions that can be undertaken under certain circumstances and guiding them towards a solution that is often the most feasible and optimum. Google’s self-driving car is a perfect example of prescriptive analytics. It analyzes and prescribes the shortest route, optimum speed, which lane to take and so on, functioning just like a human driver.
This form of analytics revolves around asking the question “Why?”. It is used to determine the causes behind the occurrence of an event. For example, diagnostic analytics may be used to find out the reason for the low sales of a company in a particular year as compared to the previous year.
As the word suggests, inferential statistics is concerned with making inferences, that is, drawing logical conclusions from the data and making future predictions.
It enables the digestion of massive volumes of structured and unstructured data and transforming it into manageable content. In recent times, it has been more focused on a path of technologies that will imitate human information processing on a more sophisticated level than ever before.
Natural language processing
It involves the use of computational techniques for analysis and synthesis of natural language and speech. For example, we can use NLP to create systems like document summarization, speech recognition, spam detection, machine translation, autocomplete, predictive typing and so on.
It means making data anonymous by severing of links between people in a database and their records to prevent the discovery of the source of the records. This often acts as a measure to prevent data leaks and misuse of data.
Big data refers to those datasets that are too large and can’t be managed or analyzed by typical database software tools.
ETL (Extract, Transform and Load)
The process of extracting raw data, transforming the data to make it fit operational needs and loading the data into the appropriate repository for the system’s use. Even though it originated with data warehouses, ETL processes are used while taking/ absorbing data from external sources in big data systems.
IoT (Internet of Things)
It refers to the network of physical objects which can transfer or exchange data over a network with other connected devices without manual interference. Objects ranging from security systems, cars, electronic appliances, speaker systems, and even alarm clocks can fall under the scope of IoT.
Exploratory Data Analysis
It is one of the primary steps of the data analysis process. It includes performing initial investigations on data to discover patterns, spot anomalies, test hypothesis and to check assumptions with the help of summary statistics and graphical representations. It is generally a visual representation.
They are the models that are inspired by the real-life biology of the brain and are used to estimate mathematical functions and facilitate different kinds of learning algorithms.
It refers to a branch of machine learning that attempts to mirror the neurons and neural networks associated with thinking in human beings. It’s the enemy in many a dystopian sci-fi novel where robots become smarter than humans and cause the downfall of mankind. We’re not there yet, but the advent of deep learning has led to the development of speech recognition, translation, and image recognition software.
It refers to a common branch of machine learning in which a data scientist trains the algorithm to draw what he or she believes to be the correct conclusions. Linear regression for regression problems, random forests for classification and regression problems, and support vector machines for classification problems are a few examples of supervised learning.
Business Intelligence (BI)
BI refers to technologies that are much more centered towards business metrics focusing on important business decisions including cost optimization, trend analysis, reporting, and so on. It is descriptive, rather than predictive.
It involves pulling actionable insights out of a set of data and putting it to good use. This includes everything right from cleaning and organizing the data; to analyzing it to find meaningful patterns and connections; to communicating those connections in a way that helps decision-makers to draw conclusions and influence their decision-making process.
It refers to segregating the data based on similar traits existing in data. The purpose may vary from organization to organization. Its applications can be seen in various domains, varying from market segmentation to social network analysis
It is a process of transforming raw data into a useful form. The process might include data aggregation, data visualization, and other analyses as well.
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