Data analytics lifecycle

Question 1

What are the main reasons to use frameworks?

Answer 1

efficient use of time
nothing gets forgotten
scale projects

Question 2

why use frameworks in data science?

Answer 2

acts as a guide
ensure focus is on ds not bi
needs a collaborative approach

Question 3

what are the 2 key project roles that get a sponsor presentation?

Answer 3

Business user

project sponsor

Question 4

what are the 2 key project roles that get the code and technical documents?

Answer 4

data engineer

data scientist

Question 5

what are the 2 key project roles that get an analyst presentation?

Answer 5

BI analyst

Database administrator

Question 6

what are the 6 key project roles?

Answer 6

business user
project sponsor
project manager
bi analyst
data engineer
database administrator
ds

Question 7

what is the data lifecycle? (6 phases)

Answer 7

discovery
data prep
model planning
model building 
communicate results
operationalise

Question 8

In discovery what are the seven main areas?

Answer 8

learn business domain
learn from the past
resources 
frame the problem
interviewing 
formulate initial hypothesis
identify data sources

Question 9

In discovery learn the domain - what do you not need to do?
A)determine amount of domain knowledge 
B) determine general analytic problem
C) decide what technique to use
D)if you have no idea. Conduct research.

Answer 9

C) decide what technique to use

Question 10

In discovery learn from the past what do you need to do?

Answer 10

have there been any previous attempts

why did they fail?

Question 11

who is a business user?

Answer 11

someone who benefits from end results

Question 12

who is the project sponsor?

Answer 12

person responsible for genesis of the project

Question 13

who is a project manager?

Answer 13

ensure key milestones are met

Question 14

who is the BI analyst?

Answer 14

business domain expert

Question 15

who is the data engineer?

Answer 15

deep technical skills

Question 16

who is the DBA?

Answer 16

provisions and configures database

Question 17

who is the DS?

Answer 17

SME for techniques for overall analytic objectives being met

Question 18

what is crisp DM?

Answer 18

cross-industry process for data mining

Question 19

what are the 6 phases of CRISP-DM?

Answer 19

business understanding
data understanding
data prep
modeling
evaluation
deployment

Question 20

In discovery resources what do you need to access

Answer 20

available tech
data
people
time

Question 21

In discovery frame the problem what are the objectives

Answer 21

What is the goal
What is the failure criterion
Identify the success criteria

Question 22

In discovery formulate initial hypotheses what do you need to do? (2)?

Answer 22

gather and assess hypothesis

data exploration to inform discussions

Question 23

In discovery identify data sources what do you need to do? (4)

Answer 23

aggregate sources
review the raw data
determine the structures and tools
scope the kind of data needed

Question 24

How big is an analytical sandbox?

Answer 24

10x

Question 25

In data prep what are the phases?(5)

Answer 25

prepare sandbox
perform ELT
familiarise with the data
data conditioning
survey and visualise

Question 26

in model planning what are the phases? (6)

Answer 26

determine methods
techniques and workflow
data exploration 
variable selection 
model selection 
test & train

Question 27

how much time is spent in data prep?

a) 50%
b) 60%
c) 70%

Answer 27

70%

Question 28

what should you do in communicate results?

Answer 28

make recommendations
compare results
identify key findings

Question 29

what should you do in operationalise?

Answer 29

run a pilot
assess benefits
implement model

Question 30

why run a pilot?

Answer 30

make sure the model is robust

Question 31

what type of tools can be used for phase 2?

Answer 31

SQL
Hadoop
MapReduce

Question 32

what type of tools can be used for phase 4?

Answer 32

R
SQL
SAS