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Introduction to Finance Data Analytics Pt. 1
Pricing: Member: $195 | Non-member: $295

Credits: FP&A 4.8 | CTP 4.8 | CPE 4.8 | CPE Field of Study: Finance 
Dates: February 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 one 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 first series is an introduction to finance data analytics and descriptive analytics. 

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 data analytics and business data

2. Learn how to assess data and it's role in business

3. Apply data analytics techniques and derive insights

 

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: Introduction to Finance Data Analytics
  • Introduction to Finance Data Analytics
  • Competitive Advantage with Data Analytics
  • Drivers for Finance Data Analytics
  • 3 types of Analytics and Data Science Techniques Taxonomy
  • 4 phases of the Data Analytics Lifecycle
  • MAD Framework and 2 Types of Business Insights
  • 4x4 Types of Business Data and Data Types
  • 3 types of IT Systems
  • Data Quality and its 12 key dimension
Session 2: Descriptive Analytics 
  • Measuring Business Performance with Exploratory Data Analytics
  • Measures of Central Tendency and Variation
  • Introduction to Associative Data Analytics
  • Correlation – Pearson and Spearman
  • Apriori Techniques
  • Hypothesis Testing