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AI+ Data™

AT-120

Mastering AI, Maximizing Data: Your Path to Innovation

The AI+ Data™ certification equips professionals with vital skills for data science. It covers key concepts like Data Science Foundations, Statistics, Programming, and Data Wrangling. Participants delve into advanced topics such as Generative AI and Machine Learning, preparing them for complex data challenges. The program includes a hands-on capstone project focusing on Employee Attrition Prediction. Emphasis is placed on Data-Driven Decision-Making and Data Storytelling for actionable insights. Personalized mentorship, immersive projects, and cutting-edge resources ensure a transformative learning journey, preparing individuals for success in AI and data science.

Certification Duration: 40 hours (5 Days)

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Prerequisites

  • 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

Modules

12

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  1. Course Introduction Preview
  1. 1.1 Introduction to Data Science
  2. 1.2 Data Science Life Cycle
  3. 1.3 Applications of Data Science
  1. 2.1 Basic Concepts of Statistics
  2. 2.2 Probability Theory
  3. 2.3 Statistical Inference
  1. 3.1 Types of Data
  2. 3.2 Data Sources
  3. 3.3 Data Storage Technologies
  1. 4.1 Introduction to Python for Data Science
  2. 4.2 Introduction to R for Data Science
  1. 5.1 Data Imputation Techniques
  2. 5.2 Handling Outliers and Data Transformation
  1. 6.1 Introduction to EDA
  2. 6.2 Data Visualization
  1. 7.1 Introduction to Generative AI Tools
  2. 7.2 Applications of Generative AI
  1. 8.1 Introduction to Supervised Learning Algorithms
  2. 8.2 Introduction to Unsupervised Learning
  3. 8.3 Different Algorithms for Clustering
  4. 8.4 Association Rule Learning with Implementation
  1. 9.1 Ensemble Learning Techniques
  2. 9.2 Dimensionality Reduction
  3. 9.3 Advanced Optimization Techniques
  1. 10.1 Introduction to Data-Driven Decision Making
  2. 10.2 Open Source Tools for Data-Driven Decision Making
  3. 10.3 Deriving Data-Driven Insights from Sales Dataset
  1. 11.1 Understanding the Power of Data Storytelling
  2. 11.2 Identifying Use Cases and Business Relevance
  3. 11.3 Crafting Compelling Narratives
  4. 11.4 Visualizing Data for Impact
  1. 12.1 Project Introduction and Problem Statement
  2. 12.2 Data Collection and Preparation
  3. 12.3 Data Analysis and Modeling
  4. 12.4 Data Storytelling and Presentation

Certification Modules

  1. Course Introduction Preview
  1. 1.1 Introduction to Data Science
  2. 1.2 Data Science Life Cycle
  3. 1.3 Applications of Data Science
  1. 2.1 Basic Concepts of Statistics
  2. 2.2 Probability Theory
  3. 2.3 Statistical Inference
  1. 3.1 Types of Data
  2. 3.2 Data Sources
  3. 3.3 Data Storage Technologies
  1. 4.1 Introduction to Python for Data Science
  2. 4.2 Introduction to R for Data Science
  1. 5.1 Data Imputation Techniques
  2. 5.2 Handling Outliers and Data Transformation
  1. 6.1 Introduction to EDA
  2. 6.2 Data Visualization
  1. 7.1 Introduction to Generative AI Tools
  2. 7.2 Applications of Generative AI
  1. 8.1 Introduction to Supervised Learning Algorithms
  2. 8.2 Introduction to Unsupervised Learning
  3. 8.3 Different Algorithms for Clustering
  4. 8.4 Association Rule Learning with Implementation
  1. 9.1 Ensemble Learning Techniques
  2. 9.2 Dimensionality Reduction
  3. 9.3 Advanced Optimization Techniques
  1. 10.1 Introduction to Data-Driven Decision Making
  2. 10.2 Open Source Tools for Data-Driven Decision Making
  3. 10.3 Deriving Data-Driven Insights from Sales Dataset
  1. 11.1 Understanding the Power of Data Storytelling
  2. 11.2 Identifying Use Cases and Business Relevance
  3. 11.3 Crafting Compelling Narratives
  4. 11.4 Visualizing Data for Impact
  1. 12.1 Project Introduction and Problem Statement
  2. 12.2 Data Collection and Preparation
  3. 12.3 Data Analysis and Modeling
  4. 12.4 Data Storytelling and Presentation

Tools

Google Colab

MLflow

Alteryx

KNIME

Exam Objectives

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Advanced Data Analysis Techniques

Learners will acquire skills in managing, preprocessing, and analyzing data using statistical methods and exploratory techniques to uncover insights and patterns.

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Programming and Machine Learning Proficiency

Students will develop strong programming skills necessary for data science, along with foundational and advanced machine learning techniques to build predictive models.

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Application of Generative AI and Machine Learning

Learners will learn to employ generative AI tools and machine learning algorithms to derive deeper insights from data, enhancing their analytical capabilities.

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Data-Driven Decision Making and Storytelling

Students who goes through this course will get the ability to make informed decisions based on data analysis 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

$1,45,407
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% Difference

69
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AI Data Scientist

Analyzes complex data to extract insights, builds predictive models, employs statistical methods, and communicates findings to influence decision-making.

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AI Machine Learning Engineer

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

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AI Engineer

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

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AI Data Analyst

Interprets data, generates reports, identifies trends, supports business decisions with actionable insights, and utilizes visualization tools to present data.

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Frequently Asked Questions

The certification covers Data Science Foundations, Statistics, Programming, and Data Wrangling, along with advanced subjects such as Generative AI and Machine Learning.

Participants are prepared for complex data challenges through immersive projects, e-learning resources, and access to cutting-edge tools.

Graduates of this program can pursue various 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 gain skills in Data-Driven Decision-Making, Data Storytelling for actionable insights, and proficiency in key areas of data science and AI.

Yes, the course is designed to be flexible and can be pursued alongside full-time work. The online format allows participants to learn at their own pace and manage their study schedules according to their availability.

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