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[LE DSA] > UNIT 1 > Flashcards

UNIT 1 Flashcards

1
Q

It is an approach that offers new techniques to solve problems

A

Data Science and Analytics

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2
Q

What are the roles in analytics?

A

Collector/Data Steward, Data Engineer, Business Analyst, Modeler/Data Scientist

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3
Q

Also known as data scientist that models algorithm; makes sure data are correct

A

Modeler/Data Scientists

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4
Q

They are the business experts in the field of data science

A

Business Analyst

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5
Q

It oversees all roles in the data field

A

Project Manager

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6
Q

Hacking skills and Substantive expertise

A

Danger Zone

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7
Q

Substantive expertise and Math and Statistics Knowledge

A

Traditional Research

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8
Q

Hacking Skills and Math and Statistics Knowledge

A

Machine Learning

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9
Q

Due to its interdisciplinary nature, it requires an intersection of abilities: hacking skills, math and statistics knowledge, and substantive expertise in a field of science

A

Data science

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10
Q

It is necessary for working with massive amounts of electronic data that must be acquired, cleaned, and manipulated

A

Hacking skills

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11
Q

It allows a data scientist to choose appropriate methods and tools in order to extract insight from data

A

Math and statistics knowledge

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12
Q

In a scientific field, it is crucial for generating motivating questions and hypotheses and interpreting results

A

Substantive expertise

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13
Q

It lies at the intersection of knowledge of math and statistics with substantive expertise in a scientific field

A

Traditional research

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14
Q

Stems from combining hacking skills with math and statistics knowledge, but does not require scientific motivation

A

Machine Learning

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15
Q

Hacking skills combined with substantive scientific expertise without rigorous methods can beget incorrect analyses

A

Danger zone

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16
Q

Scope: Macro

A

Data Science

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17
Q

Goal: To ask the right questions

A

Data Science

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18
Q

Major Fields: Machine learning, AI, Search engine engineering, corporate analytics

A

Data Science

19
Q

Using Big Data: Yes

A

Data Science and Analytics

20
Q

Scope: Micro

A

Data Analytics

21
Q

Goal: To find actionable data

A

Data Analytics

22
Q

Major FIelds: Healthcare, gaming, travel, industries with immediate data needs

A

Data Analytics

23
Q

It is the mother of invention

A

Necessity

24
Q

History: Report Writing; Goal is automation

A

1970s

25
Q

History: Centralized System; Goal is to have Enterprise Resource Planning or Management Info System

A

1980s

26
Q

History: Business Intelligence; Goal is apps for everyone, applications for personal use were invented and made to share

A

1990s

27
Q

History: Internet Data and Mining

A

2000s

28
Q

History: Big data and data science used for real time analysis

A

2010s

29
Q

T/F: The needs of the industry, as demanded by the fast moving realities of the present time, also evolve the analytics

A

TRUE

30
Q

T/F: The value in the data “haystack” is not guided by your knowledge of the domain- but of the tools or techniques

A

FALSE

31
Q

T/F: Finding that value- the combination of all the skillsets that you need- is data science

A

FALSE

32
Q

Evolution: Describes historical data; Helps understand how things are going

A

Descriptive

33
Q

Evolution: Helps understand unique drivers; Segmentation, Statistical & Sensitivity analysis

A

Diagnostic

34
Q

Evolution: Forecast future performance, events, and results

A

Predictive

35
Q

Evolution: Analysis that suggest a prescribed action

A

Prescriptive

36
Q

Evolution: Proactive action; Learn at scale; Reason with purpose interact naturally

A

Cognitive

37
Q

Medical image analysis, Machine learning in disease diagnosis, Genetics and Genomics, Drug development, Virtual assistance for customer support

A

Data Science and Analytics in Healthcare

38
Q

Finding useful pattern in a data; It is the process of knowledge discovery, machine learning and predictive analytics

A

Data Mining

39
Q

Which of the following is NOT about data mining?

A

Descriptive statistics, Exploratory visualization, Dimensional slicing, Hypothesis testing, Queries

40
Q

It is a type of learning model in data mining which is directed data mining. The model generalizes the relationship between the input and output

A

Supervised

41
Q

It is a type of learning model in data mining which is an undirected data mining. The objective of this class of data mining techniques is to find patterns in data based on the relationship between data points themselves

A

Unsupervised

42
Q

What are the groups of learning models in data mining?

A

Classification, Regression, Clustering, Anomaly Detection, Time Series Forecasting, Association, Text and Sentiment Analysis

43
Q

What are the steps in data mining by CRISP-DM Framework?

A

Business understanding, Data understanding, Data preparation, Modeling, Testing and Evaluation, Deployment

[LE DSA] (2 decks)

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