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Overview Prerequisites Skills You’ll Gain Self Study Materials Included Tools What You’ll Learn Course Modules FAQs

What’s Included?

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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.

Skills You’ll Gain

  • Data Visualization Techniques
  • Data Quality and Bias Mitigation
  • Deep Learning for Data Processing
  • Statistical Modeling
  • Big Data Technologies

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You’ll Master

Google Colab

Google Colab

MLflow

MLflow

Alteryx

Alteryx

KNIME

KNIME

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.

Course Modules

Course Overview
  1. Course Introduction Preview
Module 1: Foundations of Data Science
  1. 1.1 Introduction to Data Science
  2. 1.2 Data Science Life Cycle
  3. 1.3 Applications of Data Science
Module 2: Foundations of Statistics
  1. 2.1 Basic Concepts of Statistics
  2. 2.2 Probability Theory
  3. 2.3 Statistical Inference
Module 3: Data Sources and Types
  1. 3.1 Types of Data
  2. 3.2 Data Sources
  3. 3.3 Data Storage Technologies
Module 4: Programming Skills for Data Science
  1. 4.1 Introduction to Python for Data Science
  2. 4.2 Introduction to R for Data Science
Module 5: Data Wrangling and Preprocessing
  1. 5.1 Data Imputation Techniques
  2. 5.2 Handling Outliers and Data Transformation
Module 6: Exploratory Data Analysis (EDA)
  1. 6.1 Introduction to EDA
  2. 6.2 Data Visualization
Module 7: Generative AI Tools for Deriving Insights
  1. 7.1 Introduction to Generative AI Tools
  2. 7.2 Applications of Generative AI
Module 8: Machine Learning
  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
Module 9: Advance Machine Learning
  1. 9.1 Ensemble Learning Techniques
  2. 9.2 Dimensionality Reduction
  3. 9.3 Advanced Optimization Techniques
Module 10: Data-Driven Decision-Making
  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
Module 11: Data Storytelling
  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
Module 12: Capstone Project - Employee Attrition Prediction
  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
Optional Module: AI Agents for Data Analysis
  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

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.

The certification provides participants with the necessary tools and skills to handle complex data challenges, such as cleaning, transforming, and analyzing data.

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.

Participants will gain skills in data analysis, machine learning, data visualization, data wrangling, and predictive analytics, along with proficiency in Python and R.

Yes, the AI+ Data™ certification is designed to be flexible and can be pursued while working full-time. The course materials are available online.