Understanding Data Science

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Questions and Answers

Which of the following best describes the role of a data scientist in relation to business trends?

To exclusively focus on historical data analysis.
To ensure that all business units adhere to the same statistical model, regardless of its relevance.
To primarily manage IT infrastructure.
To manipulate raw data into actionable information, predict trends, and help businesses make strategic decisions. (correct)

According to Andrew Gelman, statistics is the most important part of data science.

False (B)

Name three programming languages that are essential for data scientists.

R, Python, and SAS

The SAS language was developed by ______ as a statistical analysis tool.

Anthony James Barr Signup and view all the answers

Match the following roles with their corresponding tasks:

Data Scientist = Transforms messy data into usable formats, solves business problems using data, and identifies business trends from data. Statistician = Applies statistical methods to analyze data. Signup and view all the answers

Which of the following actions is least aligned with the responsibilities of a data scientist?

Managing network infrastructure. (C) Signup and view all the answers

Data science is strictly confined to the fields of statistics and mathematics.

False (B) Signup and view all the answers

What is the primary goal of data science, according to the text?

To transform data into insights and decisions. Signup and view all the answers

Data science can be summarized as a multidisciplinary field where the center is ______.

data, especially Big Data Signup and view all the answers

Which statement best describes the relationship between data mining and big data?

They share the same core concept: leveraging powerful hardware and algorithms to solve complex problems. (A) Signup and view all the answers

Flashcards

Data Science Definition

Scientific discovery and practice involving the management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data.

Data Science as Interdisciplinary

A field that synthesizes statistics, informatics, computing, communication, management, and sociology to transform data into insights and decisions.

Purpose of Data Science

Primarily used to make decisions and predictions using predictive causal analytics, prescriptive analytics, and machine learning.

Skills of a Data Scientist

Collect messy data, solve business problems with data, use programming languages, understand statistics, learn analytics, and spot trends.

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What is R?

A language and environment for statistical computing and graphics allowing users to extrapolate data into a wide variety of statistical and graphical techniques.

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What is Python?

An object-oriented, interpreted, and interactive programming language that combines remarkable power with very clear syntax. Compatible with other programming languages depending on the user's preferences.

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What is SAS?

A commercial statistical analysis tool offering a variety of functions and a good user interface that can be easily learned.

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Study Notes

Definition of Data Science

Data science encompasses the collection, management, processing, analysis, visualization, and interpretation of heterogeneous data across scientific, translational, and inter-disciplinary applications.
Data science sits at the intersection of statistical and computational sciences, incorporating quantitative information with statistical and computer science theories.
Data mining paired with powerful hardware, programming systems, and efficient algorithms can solve problems in diverse fields.
Data science is an interdisciplinary field synthesizing statistics, informatics, computing, communication, management, and sociology
The purpose is to transform data into insights and decisions through a data-to-knowledge-to-wisdom approach.
Data science is primarily used for decision-making and prediction through predictive causal analytics, prescriptive analytics, and machine learning.
The core focus of data science is data, especially big data.
The objective of data science is gaining knowledge from data to improve decision-making.
Data science applies theories and technologies from multiple fields.

Data Science versus Statistics

Some academics and journalists view data science as synonymous with statistics.
Data science involves databases, coding, and potentially statistics.
Data science differs from traditional data analysis by seeking actionable patterns for predictive use, rather than solely explaining datasets.

Data Scientist

Data science emerged from the statistics and mathematics community as an interdisciplinary field.
Data scientists collect and organize large, complex datasets to identify trends, predict outcomes, and visualize information facilitating consumption.
Businesses want data scientists due to their ability to transform raw data into insights that can boost sales or predict critical trend.

Tools of the Trade

Data scientists must collect and transform unstructured data into usable formats.
They solve business problems using data-driven methods.
Data scientists uses programming languages like SAS, R, and Python.
A strong understanding of statistical principles, including tests and distributions, is required.
Data scientists apply analytical techniques for machine learning, deep learning, and text analytics.
Data scientists identify data patterns and trends to improve a business's performance.
Data scientists communicate and work with IT and business.

Programming Languages

R is a language and environment for statistical computing and graphics, enabling users to apply statistical and graphical techniques.
R is extensible and compatible across various operating systems.
Python is an object-oriented programming language with clear syntax that can work with other languages based on user preferences.
SAS is a statistical analysis tool with ample functions and a user-friendly interface but is the most expensive.