AI+ Data™
AI+ Data™
Mastering AI, Maximizing Data: Your Path to Innovation
- Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
- Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics
- Capstone Application: Solve real-world problems like employee attrition with AI
- Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship
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.
- Course Introduction Preview
- 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
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Google Colab
MLflow
Alteryx
KNIME
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
Exam Blueprint
- 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%
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.
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.
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.
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.
AI Data Scientist
Analyzes complex data to extract insights, builds predictive models, employs statistical methods, and communicates findings to influence decision-making.
AI Machine Learning Engineer
Designs and develops machine learning systems, implements algorithms, optimizes data pipelines, and integrates models into scalable, production-ready applications.
AI Engineer
Develops artificial intelligence solutions, programs neural networks, optimizes AI algorithms, ensures ethical AI deployment, and troubleshoots AI systems.
AI Data Analyst
Interprets data, generates reports, identifies trends, supports business decisions with actionable insights, and utilizes visualization tools to present data.
FAQ
What are the key components of the AI+ Data™ certification?
The certification covers Data Science Foundations, Statistics, Programming, and Data Wrangling, along with advanced subjects such as Generative AI and Machine Learning.
How does this certification prepare participants for data challenges?
The certification provides participants with the necessary tools and skills to handle complex data challenges, such as cleaning, transforming, and analyzing data.
What are the career opportunities after completing this certification?
Graduates of the AI+ Data™ certification program can pursue roles such as Data Scientist, Machine Learning Engineer, Data Analyst, AI Consultant, and other data-driven positions.
What skills will I gain from this certification?
Participants will gain skills in data analysis, machine learning, data visualization, data wrangling, and predictive analytics, along with proficiency in Python and R.
Can I pursue this course while working full-time?
Yes, the AI+ Data™ certification is designed to be flexible and can be pursued while working full-time. The course materials are available online.
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