This one-week course is for software engineers, data analysts, business analysts, technical program managers, architects, database administrators, and researchers with an interest in data science and big data engineering. The format of the course is 50% lectures and 50% labs with exercises. This course is a practical introduction to the interdisciplinary field of data science and machine learning, which is at the intersection of computer science, statistics, and business.
A significant portion of the course will be a hands-on approach to the fundamental modeling techniques and machine learning algorithms that enable you to build robust predictive models. You will learn to use the Python programming language, AWS and Azure Machine Learning tools, and technologies to help you apply machine learning techniques to practical real-world problems. The two in-class projects with Kaggle capstone and IoT streaming will crystallize the concepts learned in the course.
- Duration: 1-week (50 hours)
- Classes: January 23-27 , 2017
- Schedule: Monday-Friday; 9:00am-7:00pm
- Level: Intermediate.
WHAT YOU WILL LEARN
- DATA EXPLORATION AND VISUALIZATION
- INTRODUCTION TO PREDICTIVE ANALYTICS AND CLASSIFICATION
- EVALUATION OF PREDICTIVE MODELS
- ENSEMBLE METHODS
- DEPLOYING MACHINE LEARNING MODELS
- PARAMETER TUNING
- INTRODUCTION TO REGRESSION
- TEXT ANALYTICS
- FUNDAMENTALS OF BIG DATA ENGINEERING
- A/B TESTING & ONLINE EXPERIMENTATION
- KAGGLE CAPSTONE
- EVENT INGESTION AND STREAM PROCESSING
- IoT CASE STUDY
Receive a digital CERTIFICATE OF COMPLETION for display on your LinkedIn profiles with links back to the content and verification details to allow anyone to connect to your learning. Divergence Academy is Texas Workforce Commission approved career school.