AIDASD

AI-Driven Data Analytics for Strategic Decision-Making

This day focuses on discovering insights throughanalytical techniques.​

Topics include:​

Exploratory Data Analysis (EDA) concepts andtechniques​

Data visualization methods for discovering trendsand patterns​

Using tools such as Matplotlib, Seaborn, andggplot2​

Introduction to generative AI tools for derivinginsights​

Using generative models for data augmentationand anomaly detection​

Participants develop skills in exploring data andidentifying meaningful insights.

This session introduces machine learningalgorithms and predictive modeling techniques.​

Topics include:​

Overview of machine learning principles andworkflows​

Supervised learning algorithms includingregression and classification​

Unsupervised learning methods such asclustering and pattern detection​

Model evaluation techniques and performanceoptimization​

Advanced machine learning approaches forcomplex datasets​

Participants gain hands-on experience indeveloping predictive models.​

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The final day integrates learning through practicalapplication.​

Key topics include:​

Principles of data-driven decision-making inorganizations​

Tools for data visualization and interactivedashboards​

Data storytelling techniques for communicatinginsights​

Case study analysis using real-world business data​

Capstone project: predicting employee attritionusing machine learning models​

Participants apply their skills to solve a practicalanalytics problem and present insights.