This presentation talks about:
- How to apply data science to business problems?
- How data storytelling is helping business users consume insights easily and make data-driven decisions?
- What shall be the approach of business leaders to recover from the covid-19 recession?
- How to stay relevant in data science and grow your career?
3. 3
INTRODUCTION
Ganes Kesari
Co-founder & Head of Analytics
âSimplify Data Science for allâ100+ Clients
Insights as Stories
@kesaritweets Help start, apply and adopt Data Science
5. 5
Senior Data ScientistPrincipal AI StorytellerChief Data Wizard
FEELING LUCKY? HEREâS A DATA SCIENCE TITLE GENERATOR!
Data
Statistical
ML
AI
Chief
Principal
Senior
Junior
Associate
Deputy
Assistant
Scientist
Engineer
Analyst
Designer
Developer
Designer
Storyteller
Ninja
Chef
Wrangler
Evangelist
Rock Star
Wizard
Alchemist
Vanity keywords Areas Activities
7. 7
THE JOURNEY FROM DATA TO DECISIONS
Data Engineering
MaturityPhases
Data Science
Data as
âCultureâ
Data
Collection
Data Storage
Data
Transformation
Reporting Insights Consumption Decisions
Source: Article â When and how to build out your data science team
8. 8
THE JOURNEY FROM DATA TO DECISIONS
Data Engineering Data Science
Data
Collection
Data Storage
Data
Transformation
Reporting Insights Consumption
MaturityPhases
Source: Article â When and how to build out your data science team
Data as
âCultureâ
Decisions
9. 9
REPORTING: DESCRIPTIVE SUMMARIES
2019 Boston Chicago Detroit New York
Month Price Sales Price Sales Price Sales Price Sales
Jan 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
Feb 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
Mar 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
Apr 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
May 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
Jun 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
Jul 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
Aug 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
Sep 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
Oct 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
Nov 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Average 9.0 7.50 9.0 7.50 9.0 7.50 9.0 7.50
Variance 10.0 3.75 10.0 3.75 10.0 3.75 10.0 3.75
Revenue numbers from four Cities
10. 10
INSIGHT: PREDICTING TELCO CUSTOMER CHURN
Tenure (months)
0 - 12 36+12-36
Data Usage >
1.5 GB
01
YN
Bill > $65
0
N Y
⢠Simple Decision-tree model offered ~30% reduction in churn
⢠Advanced black-box models offered ~50%, but with low explainability
0Low Risk
1
High Risk
Source: Gramener
12. 12
CONSUMPTION: WHEN ARE PEOPLE BORN IN THE US?
Source: https://gramener.com/posters/Birthdays.pdf
..so, conceptions
might happen here
Very high births..
Love the Valentineâs?
Too busy holidaying?
Avoid April
Foolâs Day?
Unlucky 13th?
More births
Fewer births
13. 13
More births
CONSUMPTION: WHATâS THE BIRTH PATTERN IN INDIA?
Source: https://gramener.com/posters/Birthdays.pdf
Fewer births
Most births in
the first half
A striking birth pattern seen on the 5th, 10th,
15th, 20th and 25th of each monthâŚ
Very low births
Aug onwards
Why? Birthdates are âchangedâ to
aid early school admissions
.. this is a typical
indication of fraud!
18. 18
1. Most Data Science projects solve the wrong Problem..
Tip #1: Master the application of knowledge
19. 19
PROFESSIONALS STRUGGLE WITH APPLYING THEIR DATA SKILLS
Scenario Approaches to Apply
Just 1 weekâs data (single data point!)
Data for the past 3 weeks
6 monthâs data, with moderate patterns
Use heuristics and business
judgement
Simpler techniques - Moving
averages, extrapolations..
Statistics and simple time series
forecasting techniques
2 yearâs data with useful signals Advanced techniques with time
series and causal approaches
20. 20
GUIDELINES TO APPLY DATA SCIENCE TO BUSINESS PROBLEMS
⢠Whatâs the business problem youâre solving?
⢠Who is your audience and what do they need?
⢠What data do you have and what approaches
are relevant?
⢠What insights are important and are they
actionable?
⢠Take feedback and iterate
21. 21
AI IS COMING FOR THE DATA SCIENCE JOBS
AI and automation will
do away with most of
the grunt work in the
data science workflow
today.
Applied knowledge will
keep you relevant for
much longer.
22. 22
2. Data Analytics needs a lot more than Data & Analytics..
Tip #2: Learn non-core skills
24. 24
..AND BREAK IT DOWN INTO THE BUILDING BLOCKS
Domain
Design
Analytics
Development
⢠Impact analytics
⢠Clustering techniques
⢠Business workflow
⢠Influencing factors
⢠Frontend/backend coding
⢠Data transformation
⢠User journey
⢠Visuals & aesthetics
Project
Management
⢠Piecing it all together
⢠Change management
25. 25
HERE ARE THE 5 ROLES & SKILLS CRITICAL FOR DATA SCIENCE
Data
Translator
ML
Engineer
Information
Designer
Data
Scientist
Data Science
Manager
Comic characters from Gramener Comicgen library
Domain
Design
Analytics
Development
Project
Management
⢠Domain expertise
⢠Business analysis
⢠Solutioning
⢠Software engineering
⢠Front/back-end coding
⢠Data pipelining
⢠Information design
⢠User centered design
⢠Interface/visual design (parts)
⢠Stats & ML
⢠Interpret insights
⢠Scripting skills
⢠Project management
⢠Business analysis/solutioning
⢠Team handling
26. 26
3. Data cleaning takes up a majority of time on projects..
Tip #3: Sharpen ability to handle data
27. 27
In data science, 80% of the time is spent preparing data,
and the other 20% on complaining about preparing the data!
- Kirk Borne
â
28. 28
BE PREPARED FOR DATA TO BE UNSUITABLE FOR ANALYSIS
Source: Kaggle Survey
Gathering and cleaning data is a critical pre-requisite for âmeaningfulâ
insights
29. 29
LEARN DATA HANDLING AND BUDGET TIME FOR IT IN YOUR WORK
Data
deduplication
Data
standardization
Data
normalization
Quality check
Exploratory
analysis
Data Cleaning
& Preparation
30. 30
4. Technology goes obsolete faster in Data Science..
Tip #4: Learn new tools quickly
31. 31
WHAT DOES THE DATA TOOLS LANDSCAPE LOOK LIKE?
The tool does not matter. A personâs skill with the tool does.
Pick an ability to learn new tools rapidly
Source: https://mattturck.com/data2019/
32. 32
EXAMPLE: WHAT ARE YOUR TOOL OPTIONS TO VISUALIZE DATA?
Code-based
Plug-n-play
Flexibility
Complexity
Google Data Studio
Excel
Google Sheets
Tableau
Raw
Vismio
Datawrapper
Timeline JS
Polestar
Vega
Vega-lite
d3,
matplotlib
C3
High charts
Nvd3
Gramex
ggplot, bokeh
Plotly
Choose tools based on flexibility, your background and tool availability
33. 33
Tip #4: Learn new tools quickly
Tip #2: Learn non-core skills
Tip #3: Sharpen ability to handle data
Tip #1: Master the application of knowledge
36. 36
..THE US LOST ALL JOBS GAINED SINCE THE GREAT RECESSION
Source: Tax Policy Center
Over 26M jobs lost⌠âŚin just 5 weeks
Source: CNBC, Dept of Labor, Bureau of Labor Statistics
37. 37
WHAT DOES THE RECESSION MEAN FOR JOBS IN DATA SCIENCE?
Source: McKinsey report â Lives and Livelihoods
Data jobs and specialized professions
are relatively less impacted
Industries with the lowest wages and
lowest educational attainment are hit
the hardest
38. 38
HEREâS WHY DATA IS KEY FOR COVID-19 AND THE RECESSION
Enterprises
B
Community
C
Remote workforce & collaboration
Market demand & Cash flows1
2
Supply chain & Logistics3
Identifying vulnerability and contact-tracing
Tracking the COVID-19 patient lifecycle1
2
Predicting infection rates and spread2
Public Health
A
Understand behavioral shifts
Mapping the effectiveness of shutdown1
2
Address people concerns during Covid-193
Source: Gramener â NYC 311 analysisSource: Kinsa Health weather map Source: Gramener â Supply Chain flow
39. 39
HOW DO YOU STAY RELEVANT AND GROW IN YOUR CAREER PATH?
Do your own
data projects
Read/Write on
data science
Maintain a
public portfolio
Compete, learn
& re-apply
Source: Article â How to demonstrate your passion for Data