- AI Certifications
AI+ Data™
This globally recognized AI+ Data™ Certification Course by AI CERTs® equips professionals with essential knowledge to work with data for artificial intelligence, enabling them to confidently begin their journey into data-driven AI modeling, analytics, and intelligent decision-making.
AI+ Data™ Certification
Synergogy, the Authorized Training Partner (ATP) of AI Certs, brings you the AI Foundation Course designed to help learners understand the fundamentals of Artificial Intelligence. This globally recognized AI+ Data™ Certification Course by AI CERTs® equips professionals with essential knowledge to work with data for artificial intelligence, enabling them to confidently begin their journey into data-driven AI modeling, analytics, and intelligent decision-making.
- Format: Self paced online course.
- Level: Dive into Generative AI, Machine Learning, and Predictive Analytics
- Certificate: AI+ Data™ Certification by AI Certs®.
- Partner: Delivered by Synergogy as an ATP for AI Certs.
AI+ Data™ Certification
This course includes
- Online Self-Paced Learning
- Online Proctored Exam with 1 Free Retake
- Digital Badge
- High-Quality Videos, E-books, and Podcasts
- Access for Tablet & Phone
- Capstone Projects
- Case Studies
- Quizzes & Assessments
- AI Mentor for Personalized Guidance
Why This Certification Matters
- Rising Demand for AI-Data Expertise
Organizations increasingly rely on professionals who can convert complex datasets into meaningful insights while maintaining strong standards for data quality, privacy, and governance. - Reducing Risk in Data-Driven AI Systems
Improper data handling and misapplied AI models can result in inaccurate outcomes and business risk. This certification equips professionals to manage data responsibly and minimize AI-related errors. - Building Effective AI-Powered Data Strategies
Certified professionals contribute to the development of AI-driven data strategies that improve performance, support decision-making, and comply with regulatory and ethical requirements. - Accelerating Career Growth in Data and AI
As AI-enabled data solutions become central to modern business, this certification provides a competitive advantage, helping professionals advance into high-impact data and AI roles.
Data Analysts and Data Scientists: Strengthen analytical capabilities by applying AI techniques to predictive modeling, pattern discovery, and data-driven decision-making.
Business Intelligence Professionals: Use artificial intelligence to extract insights, identify trends, and uncover opportunities within complex and large-scale datasets.
IT Specialists and System Integrators: Deploy AI-powered solutions to improve data management, system performance, and infrastructure efficiency.
Data Engineers: Build and maintain scalable, AI-enabled data pipelines and architectures that support advanced analytics and intelligent applications.
Students and Early-Career Professionals: Develop in-demand AI and data skills to succeed in a rapidly expanding, data-centric professional landscape.
- Google Colab
- MLflow
- Alteryx
- KNIME
- Basic knowledge of computer science and statistics (beneficial but not mandatory).
- Keen interest in data analysis.
- Willingness to learn programming languages such as Python and R.
- Foundations of Data Science – 5%
- Foundations of Statistics – 5%
- Data Sources and Types – 6%
- Programming Skills for Data Science – 10%
- Data Wrangling and Preprocessing – 10%
- Exploratory Data Analysis – 12%
- Generative AI Tools for Deriving Insights – 6%
- Machine Learning – 10%
- Advance Machine Learning – 10%
- Data-Driven Decision-Making – 10%
- Data Storytelling – 6%
- Capstone Project – Employee Attrition Prediction – 10%
What You'll Learn
1: Foundations of Data Science
Core concepts of data science and analytics
Data science lifecycle and real-world applications
Role of AI in modern data-driven organizations
Introduction to data-driven problem solving
2: Statistics and Probability for Data Analysis
Descriptive and inferential statistics
Probability theory and distributions
Hypothesis testing and confidence intervals
Statistical thinking for reliable AI models
3: Data Sources, Types, and Data Management
Structured, semi-structured, and unstructured data
Data sources: databases, APIs, and web data
Relational vs NoSQL databases
SQL fundamentals and data retrieval techniques
4: Programming for Data Science (Python & R)
Python and R fundamentals for data science
Data manipulation using Pandas, NumPy, and dplyr
Data visualization with Matplotlib, Seaborn, and ggplot2
Writing efficient, scalable data analysis code
5: Data Wrangling and Exploratory Data Analysis (EDA)
Data cleaning, transformation, and preprocessing
Handling missing values and outliers
Exploratory data analysis techniques
Visualization-driven insight discovery
6: Generative AI and Machine Learning Fundamentals
Generative AI tools for data insights
Autoencoders, GANs, and VAEs
Supervised and unsupervised machine learning refresher
Feature engineering and model evaluation basics
7: Advanced Machine Learning and Data-Driven Decision Making
Advanced ML algorithms and ensemble techniques
Dimensionality reduction and model optimization
Applying ML to business and operational decisions
Tools for dashboards, analytics, and reporting
8: Data Storytelling and Capstone Project
Transforming insights into compelling data stories
Data visualization best practices for stakeholders
Capstone project: real-world data problem (e.g., employee attrition prediction)
End-to-end application of AI-powered data science
Bring our
AI+ Foundation™ Course in-house
Interested in getting your team started with the AI Foundation Course, fully customised for your team?
FAQs - AI+ Data™
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What is the AI+ Data™ Certification and what key data skills does it cover?
The AI+ Data™ Certification is a globally recognized program that equips professionals with essential skills in data science and artificial intelligence. It covers foundations of data science, statistics, data management, programming (Python & R), machine learning, generative AI tools, and data-driven analytics techniques.
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How does AI certification in data improve practical AI and analytics job readiness?
AI certification in data strengthens practical readiness by teaching hands-on skills like data wrangling, exploratory analysis, ML model building, and advanced analytics that align with real industry workflows. This helps learners confidently apply AI techniques to data problems in the workplace.
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What foundational concepts are essential for an AI data certification?
Foundational concepts include data science lifecycle, probability and statistics, data sourcing, data preprocessing, and introduction to machine learning. These fundamentals ensure learners understand both theory and practical application of data in AI systems.
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Do you need programming skills to start an AI certification in data?
While prior programming experience can help, many AI certification programs begin with essentials like Python and R for data manipulation, visualization, and model implementation, making them accessible for beginners.
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How does AI certification support data-driven decision-making skills?
AI certification strengthens data-driven decision-making skills by teaching advanced ML algorithms, model evaluation, and analytics techniques that help professionals translate complex datasets into actionable business insights.
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