Data & Analytics
Transform Data Into Insight, Decisions and Business Value
Organizations increasingly rely on data to improve operations, understand customers, measure performance, manage risk, identify opportunities, and support decision-making.
IBACTP® Data & Analytics certifications are organized around two complementary disciplines:
Data Analytics and Data Science
Data Analytics focuses primarily on interpreting and communicating data to support decisions.
Data Science extends into modeling, machine learning, predictive methods, experimentation, and advanced analytical techniques.
DATA ANALYTICS
CDAP® — Certified Data Analytics Professional
Designed for practitioners who work with data preparation, analysis, visualization, business intelligence, and decision support.
Competency areas may include:
- data collection;
- data preparation;
- data quality;
- descriptive statistics;
- visualization;
- dashboards;
- business intelligence;
- reporting;
- analytical interpretation;
decision support.
Potential Roles
- data analyst;
- business analyst;
- BI analyst;
- reporting analyst;
- analytics consultant;
- operations analyst;
decision-support analyst.
CDAM® — Certified Data Analytics Manager
Designed for professionals responsible for:
- analytics strategy;
- analytics teams;
- data quality;
- governance;
- analytics portfolios;
- business intelligence;
- executive reporting;
- performance measurement;
organizational analytics adoption.
- Progression
- CDAP®
- Analytics Experience
- CDAM®
DATA SCIENCE
CDSP® — Certified Data Science Professional
Designed for professionals developing applied competency in:
- data preparation;
- exploratory analysis;
- statistics;
- machine learning;
- predictive modeling;
- model evaluation;
- feature development;
- data interpretation;
- visualization;
responsible data science.
Potential Roles
- data scientist;
- machine-learning practitioner;
- analytics specialist;
- research analyst;
- quantitative analyst;
AI/data professional.
CDSM® — Certified Data Science Manager
Designed for professionals responsible for:
- data science teams;
- enterprise data science strategy;
- analytics projects;
- AI and model governance;
- project prioritization;
- budgets;
- workforce capability;
- stakeholder communication;
- business value;
model risk.
- Progression
- CDSP®
- Data Science Experience
- CDSM®
Analytics vs. Data Science
- Professionals uncertain between these paths should consider:
Choose Data Analytics if you primarily:
- analyze existing data;
- build dashboards;
- create reports;
- support decisions;
- communicate insights;
work with business intelligence.
Choose Data Science if you primarily:
- build predictive models;
- work with machine learning;
- perform advanced statistical analysis;
- develop algorithms;
- experiment with data;
create analytical models.
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