Predictive Analytics Essential Training for Executives

Course details

Organizations in nearly every industry are seeking and hiring data scientists, but even though data analytics skills are highly valued, individuals with this skill set can't make an impact unless middle and senior management know how to leverage analytics for the long-term benefit of their organization. The challenge is that most of the people overseeing advanced analytics don’t have backgrounds in data science themselves.

In this course, Keith McCormick shows executives who aren't fluent in data analytics how to hire data science professionals, manage data science teams, and transform their business with effectively deployed advanced analytics. Learn how to actively participate in a discussion about which type of analytics may address your business problem, have a better appreciation of problem-solving from a data scientist’s point of view, think strategically about hiring and technology for advanced analytics, and consider various options for organizational structure and the enterprise-wide management of analytics.

Concepts

0. Introduction

01 - Give yourself the executive analytics edge
02 - Our course goals

1. Identifying the Optimal Type of Analytics for Your Challenge

03 - Predictive analytics vs. forecasting
04 - AI compared to predictive analytics
05 - What is traditional or classic machine learning
06 - Predictive analytics compared to statistics and data science
07 - Can Gen AI and LLMs be used in predictive models

2. Getting Serious about Analytics

08 - Analytics is about making decisions
09 - Propensity scores and business problems
10 - The unintended consequences of proof of concept projects
11 - Why deployment, not insight, is the primary goal
12 - Analytics as a profit center

3. Hiring and Staffing

13 - Who should you hire first for your new data science team
14 - Data scientist, data engineers, and MLOps
15 - Data science job requirements and the problems they can create
16 - Growing a team organically
17 - Data scientists with and without vertical industry experience
18 - The importance of SMEs to modeling
19 - Do you need the latest new techniques
20 - How to spot potential

4. Software and Technology

21 - Cloud and enterprise analytics
22 - Why data prep has to be customized for predictive models
23 - The analytics software ecosystem
24 - Citizens and self-service
25 - Responsible AI

5. Organizational Structure

26 - Analytics project management
27 - Career path of the data scientist
28 - Who should data science report to
29 - The role of the CAO
30 - CAOs, CDOs, and CAIOs

6. Continuing Your Predictive Analytics Learning Journey

31 - Next steps and additional resources