Overview
Learn the basic concepts of data analytics, AI, business intelligence, big data, machine learning, and deep learning.
Syllabus
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- Introduction to Data Analytics and AI
-- Overview of Data Analytics
-- Essentials of Artificial Intelligence
-- Course structure and downloading exercise files
- Fundamentals of Data Analytics
-- Types of data: structured and unstructured
-- Data collection and data cleaning
-- Introduction to data visualization tools
- Basic Statistics for Data Analysis
-- Descriptive statistics: mean, median, mode
-- Inferential statistics: sampling and hypothesis testing
-- Correlation and causation
- Data-Driven Decision Making
-- Understanding business problems
-- Data-driven strategies
-- Analyzing and interpreting data outputs
- Introduction to AI Concepts
-- Historical evolution and AI's role today
-- Basic AI terminologies
-- Overview of popular AI applications
- Machine Learning Fundamentals
-- Supervised vs. unsupervised learning
-- Common algorithms: decision trees, k-means, and linear regression
-- Introduction to neural networks
- Tools and Platforms for Data Analytics and AI
-- Overview of tools: Python, R, Excel
-- Introduction to Jupyter notebooks
-- Download and setup instructions for software tools
- Practical Exercises and Case Studies
-- Hands-on exercises with provided datasets
-- Real-world case studies in various industries
-- Group discussions and project work
- Ethical Considerations in Data Science and AI
-- Data privacy and security
-- Bias in AI algorithms
-- Ethical AI practices
- Conclusion and Next Steps
-- Recap of key concepts learned
-- Resources for further learning
-- Career paths in data analytics and AI
Taught by
Tags
On-Demand
