|Introduction to data analytics.
Impact of visualization,
Effective user interaction with system.
|Need for analytics in BFSI sector.
Lot of moving parts,
pressure to perform,
cost of new customer acquistion,
abundance of data,
local effects on business.
|Data volume and growth
Real time branch data,
online banking data,
feeds from other systems.
|Banking data warehouse DB model.
Batch process, Mobile, Online,
3rd parties like PayTM, Debit cards,
Data relationships, common connectors across data.
|FinAL architecture & data flow.
use of each technology product,
data source and target,
How it is deployed
|Data extraction from OLTP source.
Source OLTP db,
frequency of data extraction,
|Adaptors to ingest data to FinAL.
field mappings from source to target,
run frequency of adaptors,
duplications and rerun effects,
initial and incremental extracts
|Simple analytics samples.
Show a set of visuals,
Demo the effect of visuals for various user roles
Different chart types,
Configuring views to map to base data,
Admin and user roles of visualization engine
|Security/user control in visualization
Define visualization privileges,
Map views and data fields for users,
Verify the information hiding features
|Advanced analytics - dashboards
Configure multiple charts,
Access to dashboards to users,
Change in one chart and effect on the whole dashboard
|Advanced analytics - drill downs
level 1 detail drill down,
level n detail drill down,
restrictions in drill downs, grid details
|Basics of ML/AI
What cannot be done using regular analytics,
Need for ML and AI,
Benefits of ML,
Tools for ML and AI
|Data samples for ML
Time series data samples,
Geo based data samples,
Categorized/tagged data samples
|Running predictive regression algorithms
|Running neural packages