Data Science Fundamentals

Question 1

What is Data Science?

Answer 1

• Using data to answer questions
• Data Science is a broad field, hence the broad description
• A data scientist is broadly defined as someone who combines the skills of a software programmer, statistician, and storyteller/artists to extract the nuggets of gold hidden under mountains of data

Question 2

Three qualities of big data

Answer 2

1. Volume
2. Velocity
3. Variety

Question 3

What does Volume stand for in Big Data?

Answer 3

How much data there is

Question 4

What does Velocity stand for in Big Data?

Answer 4

The rate at which data is being generated

Question 5

What does Variety stand for in Big Data?

Answer 5

The many forms the data comes in

Question 6

Diagram of Data Science skills overlap

Answer 6

Question 7

Data Science steps

Answer 7

1. Subject Matter Expertise so we have enough expertise in the area that we want to ask about in order to formulate our questions.
2. Cleaning and Formatting Data typically requires some programming
3. Analyze Data typically requires stats and math knowledge

Question 8

What can you do with R

Answer 8

• Access data
• Experiment with the data
• Analyze the data
• Plot the data

Question 9

Why are data scientists in so much demand?

Answer 9

• Because most of the answers are not already outlined in textbooks.
• A data scientist needs to be somebody who knows how to find answers to novel problems.

Question 10

What is data?

Answer 10

• A set of values
• In statistics, the population you are trying to discover something about

Question 11

What is a variable?

Answer 11

Measurements or characteristics of an item

Question 12

What is a qualitative variable?

Answer 12

Measurements or information about qualities

Question 13

What is a quantitative variable?

Answer 13

Measurements or information about quantities or numerical items

Question 14

Common types of messy data

Answer 14

1. Sequencing data
2. Population census data
3. Electronic Medical Records (EMR) or other large databases
4. Geographic information system (GIS) data (mapping)
5. Image analysis and image extrapolation
6. Language and translations
7. Website traffic
8. Personal/Ad data (eg. Facebook, Netflix predictions, etc)

Question 15

What is sequencing data?

Answer 15

• Data produced by sequencing machines
• For example, DNA or RNA sequencing data

Question 16

What format is sequencing data often found in?

Answer 16

• FASTQ format
• This is a raw file format produced by sequencing machines.
• These files are often hundreds of millions of lines long.

Question 17

Why is Image analysis messy data?

Answer 17

There is a lot of information coded in an image or video and it has to be extracted.

Question 18

Why is census information considered messy data?

Answer 18

• Almost all members of a country answer a set of standardized questions
• When you have that many respondants, the data is large and messy

Question 19

Is data of secondary or primary importance?

Answer 19

Secondary
Data is important, but a good data scientist asks questions first and seeks out relevant data second.