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DataCamp > Data Science for Business Leaders > Flashcards

Data Science for Business Leaders Flashcards

https://www.datacamp.com/courses/data-science-for-business-leaders

1
Q

What is data science?

A

Data science is a set of methodologies for taking in thousands of forms of data that are available to us today, and using them to draw meaningful conclusions.

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2
Q

What can data do?

A
  • Describe the current state of an organization or process
  • Detect anomalous events
  • Diagnose the causes of events and behaviors
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3
Q

What are the three steps of the data science workflow?

A
  • Data collection
  • Exploration and visualization
  • Experimentation and prediction
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4
Q

What do we need for machine learning?

A
  • A well-defined question
  • A set of example data
  • A new set of data to use our algorithm on
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5
Q

What are some applications of data science?

A

Fraud detection, IoT, image recognition…

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6
Q

What are some common jobs in a data science team?

A

Data engineer, data analyst, machine learning scientist…

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7
Q

What are the responsibilities of data engineers?

A
  • Information architects: control the flow of information
  • Build the storage solutions and infrastructure
  • Maintain data access: ensure the data is easy to access and process
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8
Q

What tools do data engineers use?

A
  • SQL, to store and manage big data
  • Java, Scala or Python to process data and automate data related tasks
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9
Q

What are the responsibilities of data analysts?

A
  • Create dashboards
  • Hypothesis testing
  • Data visualization
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10
Q

What tools do data analysts use?

A
  • Spreadhseets for simple storage and analysis
  • SQL for large scale analysis
  • BI Tools (Tableau, Power BI, Looker) for dashboarding and sharing information
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11
Q

What are the responsibilities of a machine learning scientist?

A
  • Make predictions and extrapolations
  • Classify data
  • Predict stock prices
  • Process images
  • Automate text analysis
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12
Q

What tools do machine learning scientists use?

A
  • Python or R for creating predictive models
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13
Q

What are three types of team structures for a data science team?

A
  • isolated
  • embedded
  • hybrid
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14
Q

What are the characteristics of an isolated data science team?

A

An isolated data science team contains one or mutiple types of data employees, without engineering or product members.

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15
Q

What are the characteristics of an isolated data science team?

A

Each data employee is part of a squad containing engineers and product managers.

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16
Q

What are the characteristics of a hybrid data science team?

A

The hybrid structure is similar to the embedded structure, but includes an additional sync for all data employees across all squads, allowing uniform data processes.

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17
Q

What are some common sources of data?

A
  • Web events
  • Customer data
  • Logistics data
  • Customer transactions
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18
Q

What does PII mean?

A

Personally Identifiable Information

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19
Q

What information does PII include?

A
  • Name
  • Locatio
  • Email address
  • Any other piece of information that can be used to tie a web event back to a real human
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20
Q

What is data pseudonymization?

A

Assign a user a user ID, and store that information in a separate table with restricted access and regular logs audit. Events are then identified by the user ID rather than the user name.

21
Q

What is data anonymization?

A

Assigning the user a user ID, then destroying the table with the actual user names.

22
Q

What does GDPR mean?

A

General Data Protection Regulation

23
Q

What does GDPR consist in?

A
  • Applies to all data inside the EU
  • Give individuals control over their personal data
  • Regulates how long data can be stored
  • Mandates appropriate anonymization
  • Disclose data collection and gain consent
24
Q

What is solicited data?

A

Solicited data is data gathered when asking customers about their opinion.

25
Q

What is solicited data useful for?

A
  • Create marketing collateral
  • Attenuate decision making risk
  • Monitor quality
26
Q

What are some common types of solicited data?

A
  • Surveys
  • Customer reviews
  • In-app questionnaires
  • Focus groups
27
Q

What does NPS stand for?

A

Net Promoter Score

28
Q

What does the NPS measure?

A

The Net Promotioner Score measures how likely users are to recommend a product.

29
Q

What are the types of soliciated data?

A
  • Qualitative (very subjective, requires a lot of analysis)
    • Conversations
    • Open-ended questions
    • Good for generating hypotheses
  • Quantitative (can be easily summarized in a graph)
    • Multiple choice
    • Rating scale
    • Good for validating hypotheses
30
Q

What are the two types of preferences?

A

Sated and revelaed

31
Q

What is a stated preference?

A

A stated preference qualifies what a user says they want or believe.

32
Q

What is a revealed preference?

A

A revealed preference qualifies a preference made visible by a user’s action or purchasing decision.

33
Q

What is an example where stated and revealed preference differ?

A

People will state they prefer to go to the gym and exercise, but their behavior reveals that they prefer to go to the beach and relax.

34
Q

What are some best practices when soliciting data?

A
  • Be specific
  • Avoid loaded language
  • Calibrate (compare to known quantities)
  • Require actionable results (have a hypothesis for each question)
35
Q

What are some common ways to collect external data?

A
  • APIs
  • Public records
  • Mechanical turk
36
Q

What does API stand for?

A

Application Programming Interface

37
Q

What are some notable APIs?

A
  • Twitter
  • Wikipedia
  • Yahoo Finance
  • Google Maps
38
Q

What are some notable public records?

A
  • data.gov
  • data.europa.eu
39
Q

What does mechanical turk consist in?

A

Mechanical turk consists in getting humans to complete a task we eventually plan on computerizing (labeling pictures for image recognition). Several people will qualify a few images, with the same image being qualified by several people to ensure qualification quality. AWS M Turk can be used to recruit such people.

40
Q

What can mechanical turk be used for?

A
  • Label customer reviews
  • Extract text from a form
  • Highlight key words in a sentence
41
Q

What are some types of data storage?

A
  • Unstructured (document database)
  • Tabular (relational database)
42
Q

What are some examples of unstructured data?

A
  • Email
  • Text
  • Video and audio files
  • Web pages
  • Social media
43
Q

What query language do document databases use?

A

NoSQL

44
Q

What query language do relational databases use?

A

SQL

45
Q

What is a dashboard?

A

A dash board is a set of metrics, usually in the form of graphd, that update on schedule.

46
Q

What are some common dashboard elements?

A
  • Tracking a value over time
  • Tracking composition over time
  • Categorical comparison
  • Highlighting a single number
  • Displaying text
47
Q

Where can you build dashboards?

A
  • Spreadsheets: Excel or Google Sheets
  • BI Tools: Power BI, Looker
  • Customized tools: R Shiny or d3.js
48
Q

What is an ad hoc analysis request?

A
  • Not repeated on a weekly or daily basis
  • Can come from many places
    • Product
    • Finance
    • Engineering
49
Q
A

DataCamp (3 decks)

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