Open Data Science Conference Agile Data

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To rephrase an old saying: ‘It takes a village to raise an Analyst.’ Data Analysts and Scientists are working in teams delivering insight and analysis on an ongoing basis. So how do you get the team to support experimentation and insight delivery without ending up in an IT Engineer vs Analyst vs Data Governance war? We present 5 shocking steps to get these teams of people working together with practical, doable steps that can help you achieve data agility.

Données & analyses

AGILE DATA
Christopher Bergh
Head Chef,
DataKitchen
O P E N
D A T A
S C I E N C E
C O N F E R E N C E_
BOSTON 2015
@opendatasci

AGENDA
Who Am I?
What Is The Problem?
A Look At Agile Through Data Lens
How To Do Agile Data In Five Shocking Steps

3K I T C H E N
DATA
Algorithm Nerd
Columbia, MIT, NASA-
Ames; ATC Automation
Into In 1990
Fuzzy Logic, Neural
Networks, Constraint
Satisfaction; Unix/C
Software Nerd
CTO, Dir Engineering, VP
Product Management
Into In 2000
Management of
Software Teams &
Startups; PowerPoint
Data Nerd
COO: ETL Engineers,
Analysts & Analytic Tool
Into In 2010
W. Edwards Deming,
Data, Bootstrapping;
Excel Hacking
WHO AM I

LOTSA People In Analytic Teams
DATA SCIENTIST
REPORTING ANALYST
ETL ENGINEER
DATABASE ARCHITECT
DEV OPS ENGINEERData Governance

LOTSA Missed Expectations
Analyze
Prepare Data
C
Analyze
Prepare Data
Business Customer Expectation Analyst Reality
Communicate The business does not
think that Analysts are
preparing data
Analysts don’t want to
prepare data

Complexity
Another Field, Software Development, Ran into
the Same Problems With Complexity ...
… They Used Something Called
‘Agile’ To Solve The Problem

AGILEMANIFESTO.ORG
5/31/2015 12
AGILEMANIFESTO.ORG

PRACTICES THAT ARE EASY TO APPLY
 Development Sprints
 User Stories
 Daily Meetings
 Defined Roles
 Retrospectives
 Pair Programming
 Burn Down Charts

SOME PRACTICES HAVE BEEN DIFFICULT TO APPLY
 Test Driven Development
 Branching And Merging
 Refactoring
 Small Releases
 Frequent Or Continuous Integration
 Experimentation For Learning
 Individual Development Environments

AGILE – WHAT IS UNIQUE TO ANALYTICS?
17
PUT THE
ANALYST AT
THE CENTER

AGILE – WHAT IS UNIQUE TO ANALYTICS?
ANALYICS
PERCIEVED
VALUE DECAY
CURVE

Why? Your work is just code: models, transforms, etc.
Use a source code control system (like GIT) to enable:
Branching
Merging
Diff
5/31/2015 20
1. MANAGE YOUR WORK LIKE CODE

2. TEST AND CONTAIN
1. Create and monitor tests
2. Test on separate data from production
3. Run tests early and often
4. Target 20% of code for tests
5/31/2015 21
Unit Tests & Systems Test … Keep Adding & Improving
1. Break up you work into components
2. Manage the environment for each
component (e.g. Docker, AMI)
3. Practice Environment Version
Control

3. PROVIDE SEPARATE ENVIRONMENTS FOR ANALYSTS
Why?
Analysts need
their data the
data to iterate,
develop &
explore.
5/31/2015 22

4. SUPPORT THREE TYPES OF WORKFLOWS
Small Team
Work directly on production
Feature Branch
Merge back to production branch
Data Governance
3rd party verification before production
merge
5/31/2015 23
Review
Test
Approve

5. GIVE ANALYSTS ABILITY TO EDIT DATABASE SAFELY
5/31/2015 24
Best-in-class companies take 12 days
to integrate new data sources into
their analytical systems; industry
average companies take 60 days;
and, laggards average 143 days
Source: Aberdeen Group: Data Management for BI: Fueling the analytical engine with high-octane information
Figure out how to
do this in
minutes

AGILE DATA Christopher Bergh
cbergh@datakitchen.io
Questions?
Comments?
BOSTON 2015
@opendatasci

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This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Detecting Credit Card Fraud: A Machine Learning Approach

Boston Institute of Analytics

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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