Development & Engineering · Self-paced
AI+ Robotics™
Master AI-Powered Robotics: Perception, Control, and Autonomous Systems.
Executive summary
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.
Built for these roles
Before you start
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
One-time price
$280
40 hours, self-paced. Lifetime access, certificate included.
Certification exam included (limited attempts).
Secure checkout via Stripe. Instant access after payment.
Curriculum
What you'll cover.
40 hours of self-paced content. Work through it in order, on your schedule.
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 Refresher
8.1 Introduction to Supervised Learning Algorithms
8.2 Introduction to Unsupervised Learning
8.3 Different Algorithms for Clustering
8.4 Association Rule Learning
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
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