AI+ Data™

  • 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
Enroll Now
AI+ Data™
Self-Paced: $495
Instructor-Led: $595

At a Glance: Course + Exam Overview

Category AI Data & Robotics, AI System Engineer, AI Technical, All Courses, Available Now, Business Intelligence Analyst, Data Engineer, Data Science & AI Specialist, Data Scientist, English, Financial Analyst, Language, Machine Learning Scientist, Statistician
Program Name AI+ Data™
Duration Instructor-Led:5 days (live or virtual) | Self-Paced:40 hours (5 Days)
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.
Exam Format 50 questions, 70% passing, 90 minutes, online proctored exam

What You'll Learn

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.

Certification Modules

Course Overview

  • Course Introduction Preview

Module 1: Foundations of Data Science

  • 1.1 Introduction to Data Science
  • 1.2 Data Science Life Cycle
  • 1.3 Applications of Data Science

Module 2: Foundations of Statistics

  • 2.1 Basic Concepts of Statistics
  • 2.2 Probability Theory
  • 2.3 Statistical Inference

Module 3: Data Sources and Types

  • 3.1 Types of Data
  • 3.2 Data Sources
  • 3.3 Data Storage Technologies

Module 4: Programming Skills for Data Science

  • 4.1 Introduction to Python for Data Science
  • 4.2 Introduction to R for Data Science

Module 5: Data Wrangling and Preprocessing

  • 5.1 Data Imputation Techniques
  • 5.2 Handling Outliers and Data Transformation

Module 6: Exploratory Data Analysis (EDA)

  • 6.1 Introduction to EDA
  • 6.2 Data Visualization

Module 7: Generative AI Tools for Deriving Insights

  • 7.1 Introduction to Generative AI Tools
  • 7.2 Applications of Generative AI

Module 8: Machine Learning

  • 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

Module 9: Advance Machine Learning

  • 9.1 Ensemble Learning Techniques
  • 9.2 Dimensionality Reduction
  • 9.3 Advanced Optimization Techniques

Module 10: Data-Driven Decision-Making

  • 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

Module 11: Data Storytelling

  • 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

Module 12: Capstone Project - Employee Attrition Prediction

  • 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

Optional Module: AI Agents for Data Analysis

  • 1. Understanding AI Agents
  • 2. Case Studies
  • 3. Hands-On Practice with AI Agents

Finish the course and get certified

Course Certificate

Industry Opportunities

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.

Frequently Asked Questions

Prerequisites

Exam Details

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

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%
Self-Paced Online

Self-Paced Online: 40 hours (5 Days)

Price: $495

Instructor-Led Online

Instructor-Led Online: 5 days (live or virtual)

Price: $595

Core AI Tools Covered