In episode 6 of Data Conversations Over Coffee we talk to Maciej Kaliszka about building a fully accredited Data University!
Many organisations have reached a fairly mature point in their data transformation in that they have a structured data office that provides data analytics services to the rest of the business. Naturally the people that work in the data office are data literate. But, to achieve a true data centric state across the enterprise data literacy needs to be improved across all business units.
Enter Maciej (Maj) Kaliszka and Niren Mungar Ram. Not satisfied with just evangelising data analytics across absa CIB they decided to develop a Data University for bank employees.
The curriculum is made up of 4 levels and employees progress through each stage to a point where they can work on their own data sets and develop models that can be put into production.
Watch the video for all the detail on how the Data University was set up.
DATACON AFRICA: LIVE
DataCon Africa: Live is a 100% virtual conference and will connect Africa's most progressive data analytics leaders with the world's most forward-thinking solution providers, set against a backdrop of cutting-edge content that you cannot find anywhere else. At home, in the office or on the road.
Data Quality Training Online
About the Course
In an age where data analytics is used to drive decision making across every facet of a business ensuring the quality of your data is paramount to your organisation's success. The adage "garbage in, garbage out" may be cliched but it describes, perfectly, the critical importance of high quality data being fed into sophisticated models.
Corinium has partnered with InfoBluePrint to bring you the most comprehensive data quality management course delivered through a state-of-the-art webinar platform.
The course structure is 8 modules spread over 4 days by our leading trainers - Diana Joseph and Joe Newbert. The 8 modules are:
- Data Quality Awareness
- Data Quality in Context
- Data Assessment
- Business & User Requirements
- Data Measurement
- Data Correction
- Problem Prevention
- Context of Data Management