For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform—by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words.
Interested in attending? Have a suggestion about running this event near you?
Register your interest now
Description
- 1 - Addressing Business Issues with Data Science
- Topic A:
- Initiate a Data Science Project
- Topic B:
- Formulate a Data Science Problem
- 2 - Extracting, Transforming, and Loading Data
- Topic A:
- Extract Data
- Topic B:
- Transform Data
- Topic C:
- Load Data
- 3 - Analyzing Data
- Topic A:
- Examine Data
- Topic B:
- Explore the Underlying Distribution of Data
- Topic C:
- Use Visualizations to Analyze Data
- Topic D:
- Preprocess Data
- 4 - Designing a Machine Learning Approach
- Topic A:
- Identify Machine Learning Concepts
- Topic B:
- Test a Hypothesis
- 5 - Developing Classification Models
- Topic A:
- Train and Tune Classification Models
- Topic B:
- Evaluate Classification Model
- 6 - Developing Regression Models
- Topic A:
- Train and Tune Regression Models
- Topic B:
- Evaluate Regression Models
- 7 - Developing Clustering Models
- Topic A:
- Train and Tune Clustering Models
- Topic B:
- Evaluate Clustering Models
- 8 - Finalizing a Data Science Project
- Topic A:
- Communicate Results to Stakeholders
- Topic B:
- Demonstrate Models in a Web App
- Topic C:
- Implement and Test Production Pipelines