Data Science
Statistical Model - correct answer-A statistical model is a class of
mathematical model, which embodies a set of assumptions concerning the
generation of some sample data, and similar data from a larger population. A
statistical model represents, often in considerably idealized form, the
data-generating process.
The assumptions embodied by a statistical model describe a set of probability
distributions, some of which are assumed to adequately approximate the
distribution from which a particular data set is sampled. The probability
distributions inherent in statistical models are what distinguishes statistical
models from other, non-statistical, mathematical models.
A statistical model is usually specified by mathematical equations that relate
one or more random variables and possibly other non-random variables. As
such, "a model is a formal representation of a theory".
All statistical hypothesis tests and all statistical estimators are derived from
statistical models. More generally, statistical models are part of the foundation
of statistical inference.
Data Science - correct answer-Data science is an interdisciplinary field about
processes and systems to extract knowledge or insights from data in various
forms, either structured or unstructured,[1][2] which is a continuation of some
of the data analysis fields such as statistics, machine learning, data mining,
and predictive analytics,[3] similar to Knowledge Discovery in Databases
(KDD).
Data science employs techniques and theories drawn from many fields within
the broad areas of mathematics, statistics, operations research,[4] information
science, and computer science, including signal processing, probability
models, machine learning, statistical learning, data mining, database, data
engineering, pattern recognition and learning, visualization, predictive
analytics, uncertainty modeling, data warehousing, data compression,
computer programming, artificial intelligence, and high performance
computing. Methods that scale to big data are of particular interest in data
science, although the discipline is not generally considered to be restricted to
such big data, and big data technologies are often focused on organizing and
,preprocessing the data instead of analysis. The development of machine
learning has enhanced the growth and importance of data science.
Data science affects academic and applied research in many domains,
including machine translation, speech recognition, robotics, search engines,
digital economy, but also the biological sciences, medical informatics, health
care, social sciences and the humanities. It heavily influences economics,
business and finance. From the business perspective, data science is an
integral part of competitive intelligence, a newly emerging field that
encompasses a number of activities, such as data mining and data
analysis.[5]
Data Scientist - correct answer-Data scientists use their data and analytical
ability to find and interpret rich data sources; manage large amounts of data
despite hardware, software, and bandwidth constraints; merge data sources;
ensure consistency of datasets; create visualizations to aid in understanding
data; build mathematical models using the data; and present and
communicate the data insights/findings. They are often expected to produce
answers in days rather than months, work by exploratory analysis and rapid
iteration, and to produce and present results with dashboards (displays of
current values) rather than papers/reports, as statisticians normally do.[6]
Data Vizualization - correct answer-Data visualization or data visualisation is
viewed by many disciplines as a modern equivalent of visual communication.
It involves the creation and study of the visual representation of data, meaning
"information that has been abstracted in some schematic form, including
attributes or variables for the units of information".[1]
A primary goal of data visualization is to communicate information clearly and
efficiently via statistical graphics, plots and information graphics. Numerical
data may be encoded using dots, lines, or bars, to visually communicate a
quantitative message.[2] Effective visualization helps users analyze and
reason about data and evidence. It makes complex data more accessible,
understandable and usable. Users may have particular analytical tasks, such
as making comparisons or understanding causality, and the design principle of
the graphic (i.e., showing comparisons or showing causality) follows the task.
Tables are generally used where users will look up a specific measurement,
while charts of various types are used to show patterns or relationships in the
data for one or more variables.
, Data visualization is both an art and a science. It is viewed as a branch of
descriptive statistics by some, but also as a grounded theory development tool
by others. The rate at which data is generated has increased. Data created by
internet activity and an expanding number of sensors in the environment, such
as satellites, are referred to as "Big Data". Processing, analyzing and
communicating this data present a variety of ethical and analytical challenges
for data visualization. The field of data science and practitioners called data
scientists have emerged to help address this challenge.[3]
Exploratory Data Analysis - correct answer-In statistics, exploratory data
analysis (EDA) 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 modeling or hypothesis testing task. Exploratory data analysis was
promoted by John Tukey to encourage statisticians to explore the data, and
possibly formulate hypotheses that could lead to new data collection and
experiments. EDA is different from initial data analysis (IDA),[1] which focuses
more narrowly on checking assumptions required for model fitting and
hypothesis testing, and handling missing values and making transformations
of variables as needed. EDA encompasses IDA.
Big Data - correct answer-Big data is a term for data sets that are so large or
complex that traditional data processing applications are inadequate.
Challenges include analysis, capture, data curation, search, sharing, storage,
transfer, visualization, querying, updating and information privacy. The term
often refers simply to the use of predictive analytics, user behavior analytics,
or certain other advanced data analytics methods that extract value from data,
and seldom to a particular size of data set.[2] Accuracy in big data may lead to
more confident decision making, and better decisions can result in greater
operational efficiency, cost reduction and reduced risk.
Analysis of data sets can find new correlations to "spot business trends,
prevent diseases, combat crime and so on."[3] Scientists, business
executives, practitioners of medicine, advertising and governments alike
regularly meet difficulties with large data sets in areas including Internet
search, finance, urban informatics, and business informatics. Scientists
encounter limitations in e-Science work, including meteorology, genomics,[4]
connectomics, complex physics simulations, biology and environmental
research.[5]
Statistical Model - correct answer-A statistical model is a class of
mathematical model, which embodies a set of assumptions concerning the
generation of some sample data, and similar data from a larger population. A
statistical model represents, often in considerably idealized form, the
data-generating process.
The assumptions embodied by a statistical model describe a set of probability
distributions, some of which are assumed to adequately approximate the
distribution from which a particular data set is sampled. The probability
distributions inherent in statistical models are what distinguishes statistical
models from other, non-statistical, mathematical models.
A statistical model is usually specified by mathematical equations that relate
one or more random variables and possibly other non-random variables. As
such, "a model is a formal representation of a theory".
All statistical hypothesis tests and all statistical estimators are derived from
statistical models. More generally, statistical models are part of the foundation
of statistical inference.
Data Science - correct answer-Data science is an interdisciplinary field about
processes and systems to extract knowledge or insights from data in various
forms, either structured or unstructured,[1][2] which is a continuation of some
of the data analysis fields such as statistics, machine learning, data mining,
and predictive analytics,[3] similar to Knowledge Discovery in Databases
(KDD).
Data science employs techniques and theories drawn from many fields within
the broad areas of mathematics, statistics, operations research,[4] information
science, and computer science, including signal processing, probability
models, machine learning, statistical learning, data mining, database, data
engineering, pattern recognition and learning, visualization, predictive
analytics, uncertainty modeling, data warehousing, data compression,
computer programming, artificial intelligence, and high performance
computing. Methods that scale to big data are of particular interest in data
science, although the discipline is not generally considered to be restricted to
such big data, and big data technologies are often focused on organizing and
,preprocessing the data instead of analysis. The development of machine
learning has enhanced the growth and importance of data science.
Data science affects academic and applied research in many domains,
including machine translation, speech recognition, robotics, search engines,
digital economy, but also the biological sciences, medical informatics, health
care, social sciences and the humanities. It heavily influences economics,
business and finance. From the business perspective, data science is an
integral part of competitive intelligence, a newly emerging field that
encompasses a number of activities, such as data mining and data
analysis.[5]
Data Scientist - correct answer-Data scientists use their data and analytical
ability to find and interpret rich data sources; manage large amounts of data
despite hardware, software, and bandwidth constraints; merge data sources;
ensure consistency of datasets; create visualizations to aid in understanding
data; build mathematical models using the data; and present and
communicate the data insights/findings. They are often expected to produce
answers in days rather than months, work by exploratory analysis and rapid
iteration, and to produce and present results with dashboards (displays of
current values) rather than papers/reports, as statisticians normally do.[6]
Data Vizualization - correct answer-Data visualization or data visualisation is
viewed by many disciplines as a modern equivalent of visual communication.
It involves the creation and study of the visual representation of data, meaning
"information that has been abstracted in some schematic form, including
attributes or variables for the units of information".[1]
A primary goal of data visualization is to communicate information clearly and
efficiently via statistical graphics, plots and information graphics. Numerical
data may be encoded using dots, lines, or bars, to visually communicate a
quantitative message.[2] Effective visualization helps users analyze and
reason about data and evidence. It makes complex data more accessible,
understandable and usable. Users may have particular analytical tasks, such
as making comparisons or understanding causality, and the design principle of
the graphic (i.e., showing comparisons or showing causality) follows the task.
Tables are generally used where users will look up a specific measurement,
while charts of various types are used to show patterns or relationships in the
data for one or more variables.
, Data visualization is both an art and a science. It is viewed as a branch of
descriptive statistics by some, but also as a grounded theory development tool
by others. The rate at which data is generated has increased. Data created by
internet activity and an expanding number of sensors in the environment, such
as satellites, are referred to as "Big Data". Processing, analyzing and
communicating this data present a variety of ethical and analytical challenges
for data visualization. The field of data science and practitioners called data
scientists have emerged to help address this challenge.[3]
Exploratory Data Analysis - correct answer-In statistics, exploratory data
analysis (EDA) 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 modeling or hypothesis testing task. Exploratory data analysis was
promoted by John Tukey to encourage statisticians to explore the data, and
possibly formulate hypotheses that could lead to new data collection and
experiments. EDA is different from initial data analysis (IDA),[1] which focuses
more narrowly on checking assumptions required for model fitting and
hypothesis testing, and handling missing values and making transformations
of variables as needed. EDA encompasses IDA.
Big Data - correct answer-Big data is a term for data sets that are so large or
complex that traditional data processing applications are inadequate.
Challenges include analysis, capture, data curation, search, sharing, storage,
transfer, visualization, querying, updating and information privacy. The term
often refers simply to the use of predictive analytics, user behavior analytics,
or certain other advanced data analytics methods that extract value from data,
and seldom to a particular size of data set.[2] Accuracy in big data may lead to
more confident decision making, and better decisions can result in greater
operational efficiency, cost reduction and reduced risk.
Analysis of data sets can find new correlations to "spot business trends,
prevent diseases, combat crime and so on."[3] Scientists, business
executives, practitioners of medicine, advertising and governments alike
regularly meet difficulties with large data sets in areas including Internet
search, finance, urban informatics, and business informatics. Scientists
encounter limitations in e-Science work, including meteorology, genomics,[4]
connectomics, complex physics simulations, biology and environmental
research.[5]
