Extract Useful Knowledge from Small to Big Data
Gain Experience from Real-World Case Studies
- Location: Online via Zoom
- Duration: 10 Weeks
- Time: 9am -12pm Sat & Sun
- Cost: $4,398 Covering ALL
- Next Start Date: September 18, 2021
INFORMATION SESSION - MAY 27, 2021
Working professionals with experience in the field of data analysis as well as young professionals seeking a career in data science
Background Knowledge Requirement:
- an undergraduate degree in engineering/science or at least 3 years of professional experience in a field such as data analysis, business analytics or intelligence, software engineering or programming
- knowledge of at least one programming language preferably python
- demonstrated programming experience via projects or coursework (e.g., completion of TCSS 501 and TCSS 503 from UWT Graduate Certificate in SDE or equivalent)
- college-level statistics, probability theory, linear algebra, calculus
- A computer with a recent OS and a current web browser with at least 4GB and preferably 8GB of RAM
- High-speed internet access
- A Google account with Colab access
- Headset and webcam (recommended)
According to Tim Berners Lee, the inventor of the World Wide Web: “Data is a precious thing and will last longer than the systems themselves”. It is clear that data proliferation will never end and because of that, the use of data related technologies like Data Science and Big Data is increasing day by day. To jumpstart a career in data science, it is not to name the certificate program you have taken or to count the number of certificates you have obtained, but to build comprehensive skills required by data science that is the goal of this program.
In this program with 3 modules, participants will examine concepts, elements, strategies, and skills covering the general discipline of data science. As part of the coursework, you will be introduced to the data science life cycle as a shared mental model, learning how to formulate a problem, design the data based on the problem, visualize and analyze data, and thereafter conduct prediction/inference. In the phase of prediction and inference, you will learn state-of-the-art machine learning technologies to turn data into actionable insights. Considering the current challenges from big data, the coursework will also cover distributed data management and computing technologies. All knowledge will be delivered using practical examples of data science applications in a variety of areas within the industry as well as in academic research.
What You’ll Learn:
Module 1: Data Science Life Cycle
A data science life cycle defines the phases (or steps) in a data science project. Using a well-defined data science life cycle is useful in that it provides a common vocabulary (and shared mental model) of the work to be done. This module goes over the data science life cycle with real-world case studies.
Module 2: Machine Learning
Machine learning is in high demand, as companies and organisations are looking for ways to turn their data into actionable insights. This module covers state-of-the-art machine learning models focusing on classification and regression problems. Participants will learn how to train, evaluate, and deploy the machine learning models.
Module 3: Big Data
Big data has become a challenge for many traditional data analytics techniques. This module introduces big data infrastructure, distributed computational paradigm, and distributed data analytics algorithms for common real-world applications.
What You’ll Do:
Build theoretical foundations through lectures, exercises, discussions, and quizzes
Gain hands-on experience through workshops/tutorials with related independent assignments
Demonstrate comprehensive data science skills through milestone projects
Juhua Hu is an Assistant Professor of Computer Science and Systems in the School of Engineering and Technology and the director of Center for Data Science at the University of Washington Tacoma. Before joining UW Tacoma, Juhua worked as a Machine Learning Engineer for a silicon valley startup EverAI (now Paravision). Juhua's primary research interest is in the areas of machine learning, data mining, and data science. She is especially interested in deep representation learning, deep model interpretation and compression, time series analysis, and explainable and fair ML, where the applications span over Computer Vision, Networking, Cybersecurity, Healthcare, Human Computer Interaction, and Smart City.
For questions or additional information please email Dr. Juhua Hu