Predictive Analytics Essential Training for Executives (2020)

Course details

Organizations in nearly every industry are seeking and hiring data scientists, but many of these professionals don’t remain at their posts for long. 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. Keith details how to hire a well-rounded team, including how to identify top-performing data scientists. Plus, he shares how to navigate the different analytics and machine learning software options on the market, fit data science into your organizational structure, and more.

Topics include:
- Define propensity scoring and describe its use.
- Characterize typical analytics projects.
- Describe methods for finding and hiring talented data scientists.
- List characteristics, traits, and roles of top-performing data scientists.
- Explain why data preparation is such a large part of any analytics project.
- Describe general features of analytics software.
- Describe how analytics projects, programs, and portfolios should be managed.

Concepts

0. Introduction

01 - Speak the language of data scientists

1. Getting Serious about Analytics

02 - Analytics is about making decisions
03 - Propensity scores and business problems
04 - The unintended consequences of proof of concept projects
05 - Why deployment, not insight, is the primary goal
06 - Analytics as a profit center

2. Hiring for Analytics

07 - Data science job requirements and problems they can create
08 - Growing a data science team organically
09 - Data scientists both with and without vertical industry experience
10 - The importance of subject matter expertise to modeling
11 - CRISP-DM - Established process of producing predictive models
12 - Traits of top performing data scientists

3. What to Consider before Buying Software

13 - Analytics and machine learning software options
14 - Specific data prep for each project
15 - Citizen data scientists and self service analytics
16 - AutoML and self-service analytics - Emerging technologies
17 - Explainable AI and interpretable machine learning

4. Organizational Structure

18 - Analytics project management
19 - The career path of the data scientist
20 - Who data scientists should report to
21 - The CAO - Organizational structure from a senior executive POV

Conclusion

22 - Next steps