#KB Sentiment Analysis — Rule-Based vs. ML ApproachesSentiment analysis turns text into insights, using tokenization, polarity scores, and models like VADER and Roberta for better accuracy.Nov 30Nov 30
#KB Amazon Redshift — Analytics at ScaleExplore Amazon Redshift’s ability to handle massive datasets using parallel processing, columnar storage, and flexible distribution styles.Nov 30Nov 30
#KB Alteryx — Case Study Insights from a DatathonBuild efficient workflows in Alteryx to clean and analyze data, as seen in the 2024 SoCal Datathon case study.Nov 30Nov 30
#KB AWS Fundamentals — ML with SageMakerLearn how to set up SageMaker, create a notebook, and train your first ML model using AWS tools and services.Nov 24Nov 24
#KB Probability Theory — Part 5- The Central Limit TheoremCentral Limit Theorem explained: From sample sizes to practical limits, see why it’s essential in statistical analysis and business.Nov 10Nov 10
#KB AWS Fundamentals — Compute Cloud with EC2Learn to deploy, monitor, and optimize Shiny applications on AWS EC2 with this comprehensive and practical setup guide.Nov 6Nov 6
#KB Probability Theory — Part 4- Common Continuous Probability DistributionsUse Uniform and Exponential distributions to solve real-world business problems with probability theory basics.Oct 31Oct 31
#KB App Development — UI and Dashboard Design with Shiny IIExplore UI principles for Shiny app development, from wireframing to implementing responsive dashboards, with a stock portfolio as example.Oct 31Oct 31
#KB Probability Theory — Part 3- Continuous Probability DistributionsUnderstanding continuous probability distributions: Learn the basics of the normal distribution, confidence intervals, and apply in ExcelOct 27Oct 27
#KB App Development — Getting Started with Shiny IShiny provides a flexible alternative to traditional BI tools, offering a way to develop responsive, fully customized data applications.Oct 27Oct 27
