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Overview
The Chartered Data Analytics Manager (CDAM) is a prestigious 12-week professional designation designed for leaders who bridge the gap between technical data science and strategic business management. The program focuses on empowering managers to oversee data lifecycles, lead analytics teams, and translate complex data outputs into actionable business growth strategies.
Participants will develop the ability to align data analytics initiatives with organizational goals and master the end-to-end process of data collection, cleaning, and preparation. The course ensures that data practices comply with global privacy standards and ethical frameworks while utilizing advanced statistical tools to uncover hidden patterns.
Key Features
Duration: 12 weeks.
Target Audience: Mid-to-Senior Level Managers, Business Analysts & Data Analysts, Operations & Finance Leaders, and Consultants & Entrepreneurs.
Assessment: Practical and competency-based, including individual assignments, group projects, class participation, and a final leadership action plan or presentation.
Certification: Chartered Data Analytics Manager (CDAM) Designation Issued by the Chartered Institute of Management Specialists (CIMS).
PROGRAM MODULES
1. Data Collection and Preparation
Architecting a strategic ETL lifecycle through advanced Power
Query orchestration, this phase institutionalizes statistical hygiene
and normalization to ensure the structural integrity and governance
of data assets for complex inferential modeling.
2. Exploratory Data Analysis
This curriculum examines how organizations manage data
growth drivers and navigate structured versus unstructured
formats using tools like SQL and Power Bl, underpinned by
quality frameworks to ensure reliable insights.
3. Statistical Analysis and Hypothesis Testing
This module covers the ETL (Extract, Transform, Load)
process, focusing on data cleaning techniques such as
handling missing values, outliers, and normalization using
Excel Power Query to ensure data is prepared for analysis.
4. Predictive Modelling and Machine Learning
This section focuses on Exploratory Data Analysis (EDA),
specifically uncovering patterns, trends, and anomalies
through univariate and multivariate analysis to profile
data and validate business hypotheses.
5. Data Visualization and Storytelling
This module covers statistical hypothesis testing, focusing on
establishing null and alternative hypotheses and utilizing p-values,
T-tests, and ANOVA to drive business decisions. It also applies
these principles to A/B testing for optimizing product and
marketing strategies.
6. Business Intelligence and Reporting
This section introduces supervised and unsupervised
learning, focusing on linear and logistic regression for
business forecasting while utilizing accuracy metrics
and "goodness of fit" to evaluate model performance.
7. Ethical & Responsible Data Use
This module explores data ethics and governance,
specifically focusing on compliance with privacy laws
like GDPR/NDPR, the identification and mitigation of
algorithmic bias, and the implementation of governance
frameworks to ensure Al security and fairness.