Certified Data Analytics Professional (CDAP)

Collect data from diverse sources to gain insight and guide decisions, using surveys, integration of systems, raw data, and sensors across healthcare, retail, and traffic monitoring.

Analyze data types by contrasting quantitative, numerical data with qualitative, descriptive data. Explore structured versus unstructured data and primary, secondary, and tertiary sources for collection and analysis.

Explore eight data collection methods, from participatory methods to Delphi techniques, and weigh their advantages, challenges, and planning considerations for robust analytics.

Plan and implement data collection, analyze and report findings, while ensuring data accuracy, reliability, and privacy through standardized methods, random sampling, and cross-checked validation.

Explore how data collection accuracy relies on relevance, credibility, validity, and reliability, and apply triangulation and cross verification to ensure credible, stable, and comparable data.

Learn data privacy, data collection, ethical considerations, and consent, including signed permissions, transparency about purpose, and techniques to protect personal information and secure data.

Discover how data storage keeps data safe, organized, and easily retrievable across devices and services. Explore data warehouse concepts, data quality, governance, and data challenges like security, privacy, and capacity.

Explore common data storage types, from file and block storage to object and cloud storage, and compare data storage with data warehouses for analytics and business intelligence.

Discover data warehouse fundamentals as a centralized repository that integrates data from multiple sources for BI, dashboards, ETL, OLAP, data mining, and advanced analytics.

Explore data backup fundamentals by comparing full, incremental, and differential backups, and learn how recovery, storage, and time tradeoffs shape backup strategies.

Master big data basics, including the four V's—volume, variety, velocity, and veracity—and how they enable data storage, informed decisions, predictive analysis, and innovation.

Master data cleaning and data cleansing to correct errors and ensure accuracy, consistency, integrity, timeliness, completeness, and validity across customer databases, surveys, and e-commerce data.

Ensure data quality by maintaining accuracy, completeness, reliability, and timeliness for informed decisions and regulatory compliance. Consolidate, clean, and standardize data to resolve entry errors, interoperability challenges, and inconsistencies.

Identify issues in data cleaning by recognizing missing values, duplicates, inconsistencies, errors, and outliers, then perform initial assessment and implement automated validation and governance.

Explore four data quality techniques—missing data imputation, deduplication, error fixing, and data enrichment—using methods like mean/median imputation, prediction, exact and fuzzy matching, and API-driven enrichment.

Learn how data standardization and normalization align formats, units, and categories, converting dates to a single format, unifying capitalization and country names, and scaling values for analysis.

Data analysis turns raw data into useful information by examining large data sets to uncover correlations and insights, answer key questions, and guide decisions and strategic improvements.

Explore four data analysis types: descriptive, diagnostic, predictive, and prescriptive, and see how dashboards and tools like regression and time series analysis guide future actions.

Explore data analysis tools from Excel and Google Sheets to Tableau and Power BI, and master pivot tables and mean, median, correlation, regression, hypothesis testing for data-driven insights.

Artificial intelligence spans NLP, computer vision, machine learning, and deep learning, enabling machines to understand language, recognize patterns, and automate tasks with personalized insights and smarter decisions.

Explore how machine learning, including supervised, unsupervised, and reinforcement learning, lets systems learn from data to make decisions and predictions, boosting automation, accuracy, and scalable data handling.

Explore data mining: identify useful patterns, correlations, and insights from data sets, using the data warehouse, pattern evaluation, and techniques such as classification, clustering, and association to inform business decisions.

Data visualization uses graphs, charts, and maps to make data accessible and actionable. It highlights trends, patterns, and outliers to support decision making across retail, finance, and health care.

Master data analysis tools and business intelligence to collect, analyze, and present data for decision making, using dashboards and tools like Tableau, Power BI, QlikView, and Excel.

Explore dashboards as a centralized, interactive visual interface that integrates data from multiple sources, displays KPIs, enables real-time insights, and supports drill-down analysis across departments.

Present insights with clean visuals and a storytelling flow from problem to outcome; use a 3–5 color palette, limit data points, ensure readability with 18pt font, and include clear labels.

Apply data assimilation before interpretation to extract meaning and spark curiosity for business insights. Analyze context, sources, trends, outliers, and visuals to align with goals and involve stakeholders.

Develop reports that clearly communicate messages using simple, focused visualizations, accurate KPIs, and consistent design; enhance interactivity and relevance while avoiding clutter and confusion.

Demonstrates Cupido Cloud's KPI module and performance analytics, using AI-generated KPIs, balanced scorecard and OKR configurations, and comprehensive drill-down reporting for sales teams and projects.

Explore the Camel Balanced Scorecard quarterly template, detailing planning and tracking of KPIs across financial, customer, internal processes, and people perspectives, plus an employee appraisal framework and performance analysis.

This course uses the Camel project template to track monthly initiatives with a protected, formula-driven one-page dashboard covering plan, milestones, budget, progress, and risk.

Explore Carmel strategy template for developing and cascading corporate strategy across three levels, aligning scorecards, programs, and initiatives with budget, HR, and quarterly performance reviews.

Learn how the Kaizen program template guides staff to submit ideas, plus how the planning and performance maturity assessment evaluates strategic planning, performance management, and governance with scoring and recommendations.

Review five data lifecycle stages—collection, storage, cleaning, analysis, and visualization—emphasizing accuracy, privacy, backups, big data handling, data quality, validation, and clear dashboards for insights.