Components Of Data Science
Data science is the umbrella under which all these
terminologies are housed. Data science is like a whole subject that has
different stages within itself. Suppose a retailer wants to forecast the sales
of an X item present in its stock next month. This is known as a business
problem and the science of data aims to provide optimized solutions for it.
Data
science allows us to solve this business problem with a series of well-defined
steps.
Step 1: Collecting data
Step 2: Preprocessing of data
Step 3: analyzing data
Step 4: orienting insights
Step 5: reports
Data
mining
Data mining is defined as a process used to extract
usable data from a larger set of raw data. It involves the analysis of data
patterns in large batches of data using one or more software. Data mining has
applications in several fields, such as science and research. Data mining
involves the collection and storage of effective data, in addition to computer
processing. To segment the data and evaluate the probability of future events,
data mining uses sophisticated mathematical algorithms. Data mining is also
known as data knowledge discovery (KDD) .
Data
Science
Data science is the field of study that combines
domain knowledge, programming skills, and mathematical and statistical
knowledge to extract meaningful insights from data. On the other hand, these
systems generate insights that analysts and business users translate into
tangible commercial value.
Machine
Learning.
Machine
learning is the science of making computers act without being explicitly
programmed. Machine learning is so widespread today that you probably use it
dozens of times a day without knowing it. Many researchers also believe that
this is the best way to progress towards AI at the human level.
Big
Data
Big Data refers to a process that is used when
traditional mining and data management techniques can not discover insights and
data. the meaning of the underlying data. Unstructured, time-sensitive or
simply very large data can not be processed by relational database mechanisms.
This type of data requires a different processing approach, called big data,
which uses massive parallelism in readily available hardware.
Big
data has become so important:
• Most of the data collected now is unstructured and
requires different storage and processing.
• The
available computing power is rising rapidly, which means there are more
opportunities to process a large date. .
• The
Internet has democratized data, continually increasing available data and, at
the same time, producing more and more gross data.
Data
Analysis
Data Analysis (DA) is the process of examining data
sets to draw conclusions about the information they contain , more and more
with the help of specialized systems and software..
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