Certified Data Analyst Certification (CDA – BA1200)

  • CDA is an intermediate to expert level certification that can be pursued by both working professionals and aspiring beginners.
  • One of the most respected data analyst certificates based on the EU EDISON framework.
  • The certification programs are aligned with Industry requirements.
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Data analyst certification is a valuable credential that showcases proficiency in data analysis techniques and tools. Obtaining this certification demonstrates expertise in data manipulation, visualization, and statistical analysis. It enhances job prospects and validates skills in data-driven decision-making. It is a stepping stone towards a successful career in the ever-growing field of data analytic


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Certified Data Analyst

Test Perparation Study Guide

The Benefits

International Credential

IABAC® is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.


IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC® CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.


Introduction to Data Analysis

  • Overview of data analysis
  • Role and responsibilities of a data analyst
  • Understanding data types and formats
  • Data cleaning and preprocessing techniques

Exploratory Data Analysis

  • Descriptive statistics and data visualization
  • Handling missing data and outliers
  • Data summarization and aggregation
  • Exploring relationships between variables

Data Wrangling and Transformation

  • Data manipulation and transformation techniques
  • Handling categorical and numerical data
  • Feature engineering and dimensionality reduction
  • Data integration and merging

Statistical Analysis

  • Probability theory and distributions
  • Hypothesis testing and confidence intervals
  • Correlation and regression analysis
  • ANOVA and experimental design

Data Mining and Machine Learning

  • Introduction to data mining and machine learning
  • Supervised and unsupervised learning algorithms
  • Model evaluation and performance metrics
  • Feature selection and model tuning

Data Visualization and Communication

  • Principles of effective data visualization
  • Choosing appropriate visualization techniques
  • Designing and creating visualizations
  • Communicating data insights effectively

Data Ethics and Privacy

  • Ethical considerations in data analysis
  • Data privacy and security best practices
  • Legal and regulatory compliance
  • Handling sensitive and confidential data

Tools and Technologies

  • Introduction to data analysis tools (e.g., R, Python, SQL)
  • Data manipulation and analysis libraries (e.g., pandas, NumPy)
  • Data visualization tools (e.g., Tableau, Power BI)
  • Machine learning frameworks (e.g., scikit-learn, TensorFlow)

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