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Introduction to Finance Data Analytics Pt. 2
Pricing: Member: $195 | Non-member: $295

Credits: FP&A 4.8 | CTP 4.8 | CPE 4.8 | CPE Field of Study: Finance 
Dates: March 21 and 23, 2023 from 10:00 AM - 12:00 PM ET
System Requirements: Excel with the Analysis ToolPak and Solver Add-Ins

Course Description: 

This course is part two of a two part series on Finance Data Analytics. The two seminars can be attended in full or individually based on your learning needs. If you wish to attend both seminars you can purchase both at the bundle rate below. This second series focuses on predictive analytics, data story telling and decision science. 

This training equips participants with key data analytics concepts and skills across Finance Data analytics domains. Though the discipline of data and analytics has existed for many decades, the analytical models, data management, and software applications underpinning these analytics have evolved significantly. The way data is collected and used in Finance mean that enterprises are looking for professionals who can understand, analyze, and use the data for improved business performance. In this backdrop, this training is designed for experienced Finance and Business Professionals and builds on one’s technical and managerial competencies. Participants will learn real life examples on how data analytics can be applied to various areas of finance and business. This training has a strong focus on the application of data and insights for business performance.

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Learning Objectives

1. Understand predictive analytics and business systems 

2. Make good and quick decision using the right decision science techniques

3. Learn to derive and communicate insights to business stakeholders

 

Pre-Requisites:
  • Working knowledge of finance and business
  • Basic knowledge of MS Excel
  • High school level mathematics
Speaker Information Register Now

Sneak Peek

Session 1: Predictive Analytics 
  • Fundamentals of Predictive Analytics
  • Regression Models – Simple Linear Regression and Multiple Linear Regression
  • Evaluating Predictive Analytics Models
  • Fundamentals of ML (Machine Learning) and key characteristics of ML Models
  • Supervised & Unsupervised ML Algorithms
Session 2: Data Story Telling and Decision Science  
  • Data Storytelling including Data Visualization
  • Introduction to Decision Making
  • Decision Making Models
  • Biases in Decision Making
  • Managing your careers in Data Analytics
  • Summary and Wrap-up