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This certification offers hands-on projects, including an employee attrition prediction capstone, focusing on data-driven decision-making and storytelling. With mentorship and resources, it prepares participants for careers in AI and data science.

Prerequisites

  • Foundational Knowledge: Basic understanding of computer science and statistics is beneficial but not mandatory.
  • IInterest in Data Analysis: A keen interest in analyzing data trends and solving real-world problems.
  • Programming Willingness: Willingness to learn programming languages such as Python and R.

 
Recertification Requirements
    To maintain the validity of your certification, The Whiteboard, in affiliation with AI CERTs, requires annual recertification. A notification will be sent three months before the due date, guiding candidates through the process outlined in the candidate handbook. Need Assistance? For any questions or support regarding recertification, please contact The Whiteboard's team at info@thewhiteboard.co.in.

Course Modules and Curriculum

Course Introduction Watch Demo
  • 1.1 Introduction to Data Science
  • 1.2 Data Science Life Cycle
  • 1.3 Applications of Data Science
  • 2.1 Basic Concepts of Statistics
  • 2.2 Probability Theory
  • 2.3 Statistical Inference
  • 3.1 Types of Data
  • 3.2 Data Sources
  • 3.3 Data Storage Technologies
  • 4.1 Introduction to Python for Data Science
  • 4.2 Introduction to R for Data Science
  • 5.1 Data Imputation Techniques
  • 5.2 Handling Outliers and Data Transformation
  • 6.1 Introduction to EDA
  • 6.2 Data Visualization
  • 7.1 Introduction to Generative AI Tools
  • 7.2 Applications of Generative AI
  • 8.1 Introduction to Supervised Learning Algorithms
  • 8.2 Introduction to Unsupervised Learning
  • 8.3 Different Algorithms for Clustering
  • 8.4 Association Rule Learning with Implementation
  • 9.1 Ensemble Learning Techniques
  • 9.2 Dimensionality Reduction
  • 9.3 Advanced Optimization Techniques
  • 10.1 Introduction to Data-Driven Decision Making
  • 10.2 Open Source Tools for Data-Driven Decision Making
  • 10.3 Deriving Data-Driven Insights from Sales Dataset
  • 11.1 Understanding the Power of Data Storytelling
  • 11.2 Identifying Use Cases and Business Relevance
  • 11.3 Crafting Compelling Narratives
  • 11.4 Visualizing Data for Impact
  • 12.1 Project Introduction and Problem Statement
  • 12.2 Data Collection and Preparation
  • 12.3 Data Analysis and Modeling
  • 12.4 Data Storytelling and Presentation

Tools

Google Colab.png

Google Colab.png

MLflow

MLflow

Alteryx

Alteryx

KNIME

KNIME

Who Should Enroll?

This certification is ideal for:

  • Data Scientists & Analysts
    - Gain expertise in AI-powered data modeling and visualization.
  • AI Engineers & Machine Learning Specialists
    - Learn how AI optimizes data pipelines and automation.
  • Business Intelligence & IT Professionals
    - Use AI to improve decision-making and business forecasting.
  • Big Data & Cloud Computing Experts
    - Master AI-driven data processing in cloud environments.
Key Learning Outcomes
  • Learn how AI transforms data analytics, business intelligence, and automation.
  • Gain expertise in machine learning models for data-driven decision-making.
  • Understand big data management, AI-driven analytics, and predictive modeling.
  • Develop skills in data processing, visualization, and AI-powered forecasting.

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Exam Objectives

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Advanced Data Analysis Techniques

Develop skills in data management, preprocessing, and analysis using statistical methods and exploratory techniques to uncover insights and patterns.

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Programming & Machine Learning Expertise

Gain proficiency in programming for data science, mastering both basic and advanced machine learning techniques to build predictive models.

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Generative AI & Machine Learning Applications

Learn to apply generative AI tools and machine learning algorithms to extract deeper insights from data and enhance analytical capabilities.

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Data-Driven Decision-Making & Storytelling

Acquire the ability to interpret data for informed decision-making and effectively communicate findings through compelling data storytelling.

Career Opportunities Post-Certification

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Median Salaries

$86,015

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With AI Skills

$145,407

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% Difference

69

Product Manager

AI Data Scientist

Extracts insights from complex data, develops predictive models, applies statistical techniques, and communicates findings to support data-driven decision-making.

Data Analyst

AI Machine Learning Engineer

Designs and develops machine learning systems, implements algorithms, optimizes data pipelines, and integrates models into scalable, production-ready applications.

AI Architect

AI Engineer

Develops AI solutions, programs neural networks, optimizes AI algorithms, ensures ethical deployment, and troubleshoots AI systems.

ML Engineer

AI Data Analyst

Interprets data insights, generates reports, identifies trends, supports business decisions, and utilizes data visualization tools for effective presentation.

Join AI Course & Become Future-Ready!

Stay ahead in the AI-driven world. Enroll now to gain in-demand skills, hands-on experience, and industry-recognized certification for a future-ready career.

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Frequently Asked Questions

The certification covers foundational topics such as data science principles, statistical analysis, and programming, progressing to advanced areas like generative AI and machine learning.

Participants engage in immersive projects, including a capstone on employee attrition prediction, and have access to cutting-edge tools and personalized mentorship to tackle complex data challenges.

Graduates can pursue roles such as Data Scientist, Machine Learning Engineer, AI Specialist, Data Analyst, and other related positions in the field of AI and data science.

You will acquire skills in data-driven decision-making, data storytelling, statistical analysis, programming in Python and R, and proficiency in machine learning and generative AI applications.

While a basic understanding of computer science and statistics is beneficial, it is not mandatory. A keen interest in data analysis and a willingness to learn programming languages like Python and R are recommended.