Lesson 01

Question 1

Objectives

Answer 1

• define the nature of data, data types, and unstructured data;
• define the data science process as iterative steps;
• identify the data science steps repeated to understand a problem; and
• begin basic programming in Python including: loading data, creating and executing functions, creating charts.

Question 2

What is data?

Answer 2

Data are observations that are put into context
Given that every observation has a context, an
observation is a datum

Question 3

What is a data type? Give examples.

Answer 3

A data type is called a unit. Examples: Meters per second, kilograms, joules, seconds

Question 4

What is Data Science Summary?

Answer 4

• Science is the set of methods to extract
  meaning from data.
• Data is observations in context.
• Data Science involves predictive,
  prescriptive, and machine-learning analytics
  efforts.

Question 5

What is the scientific method?

Answer 5

The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge.

Question 6

Describe the steps in the traditional scientific method.

Answer 6

1. Observe and collect data
2. Formulate hypothesis based on the data
3. Test hypothesis against new observations

Question 7

What is a model?

Answer 7

A model is a hypothesis based on data and a method (alogrithm)

Question 8

What is a hypothesis?

Answer 8

A explanation of a dataset that allows a prediction.

Question 9

What is falsification?

Answer 9

Falsification is the process that attempts to disprove a hypothesis

Question 10

What is a theory?

Answer 10

A fact-based explanation for an observation

Question 11

Describe the data science cycle.

Answer 11

• Specify the needed data
• ETL
• Prepare Data for the Model
• Build Model
• Apply Model and Derive Insight
• Present and Use Insight

Question 12

Where is most of the time in the data science cycle spent?

Answer 12

ETL & Preparing the data for the model

Question 13

Which task in the data science life cycle are specific to the data scientist?

Answer 13

• data preparation
• building the model
• build model and derive an insight

Question 14

What skills does building models require?

Answer 14

• Stats/Math
• Software Engineering
• Domain Understanding / Communication Skills

Question 15

Which step of the data science lifecycle experience the most failures?

Answer 15

Present and Using the Insight

Question 16

Name the types of analytics that are used.

Answer 16

Descriptive, Diagnostic, Predictive, Prescriptive

Question 17

Name the analytics that answer questions about the past.

Answer 17

Descriptive, Diagnostic

Question 18

Name the analysis techniques that answer questions about the future.

Answer 18

Predictive, Prescriptive

Question 19

Define descriptive analytics and give examples.

Answer 19

Descriptive analytics answer the question “What happened?”

year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber, averages.

Question 20

Define diagnostic analytics and give examples.

Answer 20

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining and correlations.

Question 21

Define predictive analytics and give examples.

Answer 21

Predictive analytics examines the question “What will happen next?” Examples, models like classifications and regressions.

Question 22

Define prescriptive analytics and give examples.

Answer 22

Prescriptive analytics examines the question “What should we do?”Examples, Logistic Regression Coefficients,
recommender engines, partial
dependency functions

Question 23

Why is Python so popular in Data Science?

Answer 23

1. Great libraries.
2. Open Source and Free
3. Scripting lang that does’t require compilation.