Data Analysis – Use in AI

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Course Overview

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Mentor Based

Program Syllabus

Module 1

About This Course

This course offers a comprehensive exploration into data analysis with a focus on its pivotal role in artificial intelligence. It covers statistical methods, data management, and predictive analytics, enhancing skills in handling, analyzing, and interpreting data to drive AI innovations.

Module 2

Aim

To provide a deep understanding of data analysis techniques and their application in artificial intelligence to optimize decision-making processes.

Module 3

Program Objectives

Master advanced data analysis techniques and their applications in AI.
Develop skills to manage, process, and analyze large datasets to uncover actionable insights.
Enhance proficiency in predictive analytics and machine learning model development.

Module 4

Program Structure

Foundations of Data Analysis for AI: Introduction to data analysis, statistical concepts, and the essential tools for data analysis.
Data Management for AI: Techniques for data collection, cleaning, preparation, and ethical data handling.
Exploratory Data Analysis (EDA): Deep dive into EDA techniques and implementing EDA using Python.
Predictive Analytics and Machine Learning: Basics of machine learning, building predictive models, and exploring advanced AI techniques like neural networks.
Case Studies and Applications: Application of data analysis in various industries such as healthcare and retail, and real-world project implementations.

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.

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