Predictive Analytics Essential Training for Executives

Predictive Analytics Essential Training for Executives
Predictive Analytics Essential Training for Executives
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 20m | 669 MB

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.

Table of Contents

1 Speak the language of data scientists
2 Analytics is about making decisions
3 Propensity scores and business problems
4 The unintended consequences of proof of concept projects
5 Why deployment, not insight, is the primary goal
6 Analytics as a profit center
7 Data science job requirements and problems they can create
8 Growing a data science team organically
9 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
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
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
22 Next steps