Share Description
In this course, you will :
- shows executives who aren't fluent in data analytics how to hire data scientists, manage data science teams, and transform their businesses with effectively deployed advanced analytics.
- how to build a well-rounded team, including identifying top-performing data scientists
- explains how to navigate the market's various analytics and machine learning software options, integrate data science into your organisational structure, and more.
Syllabus :
1. Getting Serious about Analytics
- Analytics is about making decisions
- Propensity scores and business problems
- The unintended consequences of proof of concept projects
- Why deployment, not insight, is the primary goal
- Analytics as a profit center
2. Hiring for Analytics
- Data science job requirements and problems they can create
- Growing a data science team organically
- Data scientists both with and without vertical industry experience
- The importance of subject matter expertise to modeling
- CRISP-DM: Established process of producing predictive models
- Traits of top performing data scientists
3. What to Consider before Buying Software
- Analytics and machine learning software options
- Specific data prep for each project
- Citizen data scientists and self service analytics
- AutoML and self-service analytics: Emerging technologies
- Explainable AI and interpretable machine learning
4. Organizational Structure
- Analytics project management
- The career path of the data scientist
- Who data scientists should report to
- The CAO: Organizational structure from a senior executive POV
