The deck describes: The importance of Project Management in context of Data Science The Spotle SMART (Specify-Measure-Analyse-Roll-out-Test) model for managing Data Science projects The CRISP-DM standard for data mining and analytics projects Stakeholders Tools and Methodologies used in Data Science projects Common risks in Data Science Projects

1. Spotle.ai Study Material
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Planning
Successful Data Science Projects

2. Spotle.ai Study Material
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Project Management is the practice of initiating,
planning, executing, controlling, and closing the
work of a team to achieve specific goals and meet
specific success criteria at the specified time.
- Wikipedia
What Is Project Management?
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3. Spotle.ai Study Material
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✓ Strategic Alignment – Aligns
all stakeholders on a
common goal and roadmap
✓ Manage Budget – Ensures a
work is delivered within
budget
✓ Manage Time – Ensures a
work is delivered within
budget
✓ Manage Scope – Control to
prevent scope creep
✓ Risk Management –
✓ Reuse Success Stories –
Codified best practices that
can be successfully reused
✓ Builds a clear Go-To-Market
path – Creates a unified plan
from requirement gathering
to live deployment and user
acceptance
Why Is It Important?
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Project Management
basically gives a way
of managing chaos
and giving a
predictable path to
success.
4

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In Data Science Projects, Project
Management Becomes Critical:
๏ Large In Scale
๏ Cross Functional Teams
๏ CXO Involvement
๏ High Priority
๏ High Risk
๏ Multi-Agency
๏ High Complexity
๏ Data Issues
๏ Iterative Process

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85% Of
Big Data
Projects
Fail
Source: eweek.com

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Delivering A
Successful Data
Science Project
Requires Smart
Planning And
Execution.

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8
The
Methodology
For
Managing
Data
Science
Projects
SMART

9. Spotle.ai Study Material
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Problem
Definition
• Understand the
business problem
• Review literature
• Background
Research
Data
Acquisition And
Preparation
• Identify Data Sources
• Define Data Collection Plan
• Data Collection – Primary and
Secondary Data
• Data cleaning and preparation
Model Selection
And Fitment
• Compare and
select model
Validation
• Validate model with actual
(mock/ scrambled) data
• User Acceptance Testing
Deployment
• Deployment of model in
live
Monitoring
• Monitor
accuracy in
live scenario
The Spotle.ai 6-Phase SMART Model
For Data Science Projects. This
model is based on industry
guidelines such as CRISP-DM and
the experience of practitioners.
Specify and Prepare
Model
Analyse
Roll-out
Test and Monitor
Feedback
9
SMART

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Problem
Definition
Data
Acquisition And
Preparation
Model Selection
And Fitment
Validation
Deployment
Monitoring
10
Indicative Tools
Required At
Each Phase
• Microsoft Project
• Visio
• Word
• Powerpoint
• JIRA/ Other Project
Management Tool
• Tableau
• Power BI
• Excel
• Ggplot using R
• Scala/ Python/ Tensorflow
• Hadoop
• MySQL/ Other Database
• Statistics
• R
• Python
• SQL
• R
• Python
• Scala
• Tensor Flow
• SQL
• Azure ML/ IBM Blue
mix
• Hadoop
• R/ Python/ Scala
• Server Tools
• Monitoring and
logging tools
• JIRA to record
feedback
SMART

11. Spotle.ai Study Material
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Problem
Definition
Data
Acquisition And
Preparation
Model Selection
And Fitment
Validation
Deployment
Monitoring
11
Key Stakeholders At
Each Stage Of A Data
Science Project
• Business User
• CXO/ Project Sponsor
• Business Analyst
• Data Scientist
• Data Analysts
• Data Collection Agencies
• Analyst Relations
• Data Scientist
• Data Scientist
• Software
Engineering
Team
• Data Scientist
• Business Analyst
• Business User
• Quality Assurance
• Data Scientist
• Software Engineer
• Business User
• Project Sponsor/
CXO
• Data Scientist
SMART

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The CRISP Methodology For
Managing Data Science Projects
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CRISP-DM stands for
cross-industry process
for data mining.
The CRISP-
DM methodology is a
structured approach to
planning a data mining/
data science/ analytics
project.

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Business Understanding
Data Understanding
Data Preparation
Modelling
Evaluation
Deployment
The
CRISP
Model
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Why
Data
Science
Projects
Fail?
Lack of
access to
data
Data Issues
Lack of
cross-
functional
expertise
Competency
Issues
Complex
models/
Under or
Over-
Fitment
Other Issues
Faulty
data/Data
misclassific
ation
Data
Scientist
lacks
experience
Lack of firm
support from
CXOs/
sponsors
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16. Spotle.ai Study Material
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Learn Data Science In The Most Intense Course Ever: https://spotle.ai/learn/data-science
1 Manage Cross-
Functional Team
6
Effectively Manage
Scope, Budget And
Time
4 Focus On Measurable
Results
2 Engage Effectively With
Senior Leaders
3
Develop Domain And
Technology
Understanding
5 Tolerate Uncertainty
To Be A Successful Data Science Project
Manager, You Should Be Able To