This document discusses data science trends, applications in retail, and opportunities. It outlines how data science is moving from big enterprises to smaller businesses and aims to reduce human intervention. It also describes how data science can help with demand forecasting, storage planning, price optimization and more in the retail industry. The document then discusses opportunities for data science in education, business and science.

3. DATA SCIENCE, RETAIL
& OPPORTUNITIES
Alexander Efremov

4. Agenda
Trends in DS
DS & Retail
Opportunities

5. Trends in DS
From Big Enterprises to SME
Reduce human intervention
Mixed Data

6. DS & Retail
 Retail Industry
Why A4Retail?
Demand forecast, Storage planning, Price Optimization, etc.
Retail System – Spec.
• Dynamic, Time-Varying, Non-Linear, MIMO system
Retail Data – Spec.
• Missings, Outliers, Uninformative vars, Seasonality, Trend, etc.

7. Automated Workflow
………
Retail
System
product1:
price, disc., adv., disp...
sale1
…
salen
…
weather
social events
other
productn:
price, disc., adv., disp...

8. Automated Workflow
Data Prep Modelling Validation
Business knowledgeData
no yes
Sales
Forecast
& Stats

9. Automated Workflow
Data Prep
Cleaning
- Miss, Outs, LowVar
Preprocessing
- Vars decomp. /detrend,
deseas./
- Transform
- Standardization
- Remove multicol., etc.
Modelling & Validation
Multistage modelling
SWR - Model dev
Model val
Sales forecast & Stats
Model destandardization
Detransformation
Add trend & seas

10. Data Prep
 Missings  Seasonality

11. Modelling
 SWR & Multistage Modelling
Modelling 2
Modelling 1
Fact. Set 1
Product
Modelling 3
Fact. Set 2
Category
Fact. Set 3
Rest
y
e1
e2
Depend.

12. DS & Retail
A4Retail – Results
www.a4everyone.com

13. Opportunities
Opportunities
Education
Business
Science

14. DATA SCIENCE, RETAIL
& OTHER OPPORTUNITIES
Alexander Efremov
THANK YOU