What is Data Science?
Data Science is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.
But I’ve seen people getting confused with many terms available on the internet. Here are some terms with their meanings and their usage areas and application.
Management Information System(MIS)
MIS is the use of information technology, people, and business processes to record, store and process data to produce information that decision-makers can use to make day to day decisions. The purpose of MIS is to extract data from varied sources and derive insights that drive business growth.
Applications:
- Decision-makers need the information to make effective decisions
- MIS systems facilitate communication within and outside the organization
- Record Keeping
Forecasting
Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time. This is typically based on the projected demand for the goods and services offered.
Applications:
- Supply Chain Management
- Economic Forecasting
- Earthquake Forecasting
- Food Forecasting
- Weather Forecasting
Business Intelligence(BI)
BI is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions. It is a suite of software and services to transform data into actionable intelligence and knowledge.
Applications:
- Sales Intelligence
- Visualization
- Reporting
- Performance Management
Big Data
Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.
Applications:
- Big data has applications in almost every sector like Education, Communication, Media, Manufacturing and Natural Resouce and many others. Although companies face a lot of industrial challenges along the way.
Machine Learning
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Applications:
Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data — often in real-time organizations are able to work more efficiently or gain an advantage over competitors.
Predictive Modeling
Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. The goal of predictive modeling is to answer this question: “Based on known past behavior, what is most likely to happen in the future?
Once data has been collected, the analyst selects and trains statistical models, using historical data. Although it may be tempting to think that big data makes predictive models more accurate, statistical theorems show that, after a certain point, feeding more data into a predictive analytics model does not improve accuracy. The old saying “All models are wrong, but some are useful” is often mentioned in terms of relying solely on predictive models to determine future action.
Applications:
- Predictive modeling is often associated with meteorology and customer retention and has many applications in business.
- One of the most common uses of predictive modeling is in online advertising and marketing. Modelers use web surfers’ historical data, running it through algorithms to determine what kinds of products users might be interested in and what they are likely to click on.
Conclusion
Here, we are at the conclusion. All these techniques work on data, although the user of the technology and the process of utilizing the data vary in many ways. These techniques also vary in the applications.
Are we missing some relevant terms? Do let us know in the comment section. Is the explanation insufficient? Any errors and improvements. Do let us know your feedback in the comment section below.
