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2 A. DATA SCIENCE > What is Data Science > Flashcards

What is Data Science Flashcards

Introduction

1
Q

What has contributed to the recent remarkable growth in Data Science?

A

The abundance of electronic data, computing power, advancements in artificial intelligence, and its demonstrated business value.

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2
Q

What is the projected growth rate of Data Science jobs in the US according to the Bureau of Labor Statistics?

A

35%.

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3
Q

What is the current median annual salary for data scientists in the US?

A

An estimated $103,000.

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4
Q

Why has demand for skilled data scientists increased across industries?

A

Due to increased adoption across industries and the need for professionals who can tell compelling stories using data.

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5
Q

Who can benefit from this course besides aspiring data scientists?

A

Managers and executives who want to transform their organization into a more data-driven one.

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6
Q

Does this course require prior knowledge in data science or programming?

A

No, this course is designed for beginners and does not require prior knowledge or a degree in data science or programming.

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7
Q

What key concepts will you learn about in this introductory course?

A

Big Data, artificial intelligence, and how data science leverages these ideas to tell hidden stories.

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8
Q

What are some specializations and programs included with this course?

A

IBM Data Science Professional Certificate, Introduction to Data Science, Key Technologies for Business, IBM AI Foundations for Business.

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9
Q

What type of understanding will you gain through the instructional videos and readings?

A

A foundational understanding of data science through instructional videos, insights from professionals, readings, practice assessments, and glossaries.

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10
Q

What will you have as a final assignment at the end of the course?

A

A case study and a quiz based on it.

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11
Q

How many modules does the course have, and is there an optional module?

A

Three modules, plus an optional module.

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12
Q

What topics are covered in Module 1?

A

The definition of data science, the data scientist’s role, essential skills, and handling different file types.

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13
Q

What topics are covered in Module 2, Lesson 1?

A

The interaction of Big Data and Cloud Computing in driving digital transformation, foundational concepts, key tools, and data mining techniques.

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14
Q

What artificial intelligence concepts will you explore in Module 2?

A

Machine learning and deep learning.

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15
Q

What is the focus of Module 3?

A

Exploring the diverse and impactful realms where data science plays a pivotal role.

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16
Q

What is covered in the optional module?

A

Data literacy concepts, the data ecosystem, data sources, databases, data warehouses, data marts, data lakes, and data processing (ETL).

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17
Q

What support is available if you encounter challenges during the course?

A

You can find support and answers in the course’s discussion forums.

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18
Q

What is the purpose of the final peer-reviewed project?

A

To explore data science job listings.

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19
Q

What concepts related to data processing will you learn about in the optional module?

A

Extract, Transform, and Load (ETL) processes and data pipelines.

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20
Q

What qualities define a skilled data scientist?

A

Essential skills, ability to handle significant data, and understanding of data science topics and algorithms.

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21
Q

What is Data Science described as in the passage?

A

Data Science is a process of using data to understand different things and validate hypotheses or models.

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22
Q

How is Data Science similar to biological or physical sciences?

A

Just like biological sciences is the study of biology and physical sciences the study of physical reactions, Data Science is the study of data.

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23
Q

What is the role of storytelling in Data Science?

A

Storytelling is used in Data Science to generate insight and translate data into a story that can help make strategic decisions.

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24
Q

What forms of data does Data Science extract from?

A

Data Science extracts data from both structured and unstructured forms.

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25
When did the term 'Data Science' become popular?
The term 'Data Science' became popular in the 1980s and 1990s when professors looked at the statistics curriculum.
26
What are the key components of Data Science according to the passage?
Data and some science. It involves working with data to find answers to questions.
27
Why is Data Science relevant today compared to the past?
Data Science is relevant today because of the abundance of data, available algorithms, cheaper storage, and open-source tools.
28
What technological advancements have made Data Science more accessible today?
The availability of algorithms, free software, and inexpensive storage has made Data Science more accessible.
29
Section 3 Start
New set of flashcards
30
What do most people agree is a significant component of Data Science?
A significant data analysis component.
31
What makes data analysis different today compared to the past?
The vast quantity of data available from various sources and the computing power to analyze it.
32
What types of data sources are available for data analysis today?
Log files, email, social media, sales data, patient information files, sports performance data, sensor data, security cameras, and more.
33
How does data science help organizations?
It helps them understand their environment, analyze existing issues, and reveal hidden opportunities.
34
What is the first and most crucial step in a data science project?
Clarifying the question the organization wants answered.
35
What qualities do good data scientists have according to the passage?
Good data scientists are curious and ask questions to clarify the business need.
36
What two questions should be asked after clarifying the business need?
'What data do we need to solve the problem?' and 'Where will that data come from?'
37
What can data scientists analyze to solve problems?
Structured and unstructured data from many sources.
38
What do multiple models in data science help reveal?
Patterns and outliers, confirming or revealing new knowledge.
39
What is the role of a data scientist after data has revealed its insights?
To become a storyteller and communicate the results to the stakeholders.
40
How can data scientists help stakeholders understand the results?
By using powerful data visualization tools.
41
How is Data Science changing the way we work?
It is changing the way we work, use data, and how organizations understand the world.
42
Section 4 Start
New set of flashcards
43
When did the term 'Data Science' become widely recognized?
Between 2009 and 2011.
44
Who are credited with coining the term 'Data Science'?
DJ Patil and Andrew Gelman.
45
What field existed before Data Science became a recognized field?
Statistics.
46
What did one of the speakers initially want to pursue instead of Data Science?
Business, singing, and medicine.
47
How did another speaker's interest in Data Science begin?
Through mechanical engineering and strategic consulting firms.
48
What academic background led one speaker to Data Science after the economic crisis?
A math degree during the economic crisis led them to pursue statistics, which eventually led to Data Science.
49
What was one speaker's first experience with Data Science in their career?
Analyzing electronic point of sale data for retail manufacturers.
50
How did one speaker's civil engineering background lead to Data Science?
Civil engineering and transportation research, where they built models forecasting traffic and emissions.
51
What type of models did the civil engineer speaker build during their transportation research?
Large models to forecast traffic and determine congestion and emissions.
52
What type of data sets did the civil engineer work with in the mid-90s?
Household samples of 150,000 households making half a million trips.
53
Section 4 Start
New set of flashcards
54
When did the term 'Data Science' become widely recognized?
Between 2009 and 2011.
55
Who are credited with coining the term 'Data Science'?
DJ Patil and Andrew Gelman.
56
What field existed before Data Science became a recognized field?
Statistics.
57
Section 5 Start
New set of flashcards
58
What are the three essential traits an aspiring data scientist should have according to the speaker?
Curiosity, being judgmental, and being argumentative.
59
Why is curiosity important for a data scientist?
Curiosity is important because if you're not curious, you won't know what to do with the data.
60
Why should a data scientist be judgmental?
Being judgmental helps because preconceived notions give a starting point for analysis.
61
Why is it important for a data scientist to be argumentative?
Argumentativeness helps because it allows you to take a strong position and learn from the data.
62
What is secondary to curiosity and taking positions according to the speaker?
Comfort and flexibility with analytics platforms and software.
63
What is the final skill a data scientist needs after analyzing data?
The ability to tell a great story with the data findings.
64
Why is storytelling important in data science?
Without storytelling, findings would remain hidden, and the data scientist's prominence relies on their storytelling ability.
65
What should a starting point be for an aspiring data scientist?
A starting point should be figuring out the field or industry you're interested in.
66
How should an aspiring data scientist determine their competitive advantage?
A data scientist’s competitive advantage comes from understanding a specific aspect of life better than others, not just analytical skills.
67
Once you’ve identified your expertise, what should you acquire next?
They should acquire the analytical skills and tools specific to the industry they’re interested in.
68
Section 6 Start
New set of flashcards
69
What drives new insights and knowledge through data analysis?
Recent data access and enhanced computing power.
70
What role does computing power play in data science?
It helps analyze large amounts of data to reveal new knowledge and insights.
71
What does a data scientist do, according to the text?
They uncover insights and translate data into stories for strategic decision-making.
72
What types of data does data science deal with?
Structured and unstructured data.
73
What are the key steps in the process of gleaning insights from data?
Clarifying the problem, data collection, analysis, pattern recognition, storytelling, and visualization.
74
Why is curiosity important for a data scientist, according to Professor Aheter?
It helps data scientists explore data and come up with meaningful questions.
75
What skills are companies looking for in data scientists beyond statistics or programming?
Versatility, knowledge of a particular subject, programming experience, and strong communication skills.
76
What backgrounds might data scientists come from?
Economics, engineering, medicine, and more.
77
What should you focus on once you understand your strengths and interests in data science?
Mastering data analysis in your field and selecting suitable tools for your industry.
78
How will data scientist jobs evolve as technology changes?
They will change and develop as technology advances.
79
What is one key characteristic data scientists must maintain throughout their career?
Logical thinking, using algorithms, and a methodical approach.
80
Section 7 Start
New set of flashcards
81
What are algorithms?
A set of step-by-step instructions to solve a problem or complete a task.
82
What is a model in data science?
A representation of the relationships and patterns found in data to make predictions or analyze complex systems retaining essential elements needed for analysis.
83
What are outliers?
When a data point or points occur significantly outside of most of the other data in a dataset, potentially indicating anomalies, errors, or unique phenomena that could impact statistical analysis or modeling.
84
What is quantitative analysis?
A systematic approach using mathematical and statistical analysis to interpret numerical data.
85
What is structured data?
Data is organized and formatted into a predictable schema, usually related tables with rows and columns.
86
What is unstructured data?
Unorganized data that lacks a predefined data model or organization, making it harder to analyze using traditional methods. This data type often includes text, images, videos, and other content that doesn’t fit neatly into rows and columns like structured data.

2 A. DATA SCIENCE (2 decks)

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