TE #08: How to become a Full-stack Data Scientist

Mr. Tam Nguyen Thanh
Online via Zoom
#08
Important
Saturday, Apr 16 2022 | 04:00 PM
Online via Zoom
Introduction

Who is the Full-stack data scientist?





They are general scientists with the job of an engineer and work on each phase of the data science lifecycle.





A full-stack data scientist’s scope of work includes every component of a data science business initiative, from problem identification, training, to deploying machine learning models that benefit stakeholders.





So:





  • What does a particular Full stack Data scientist do in a data lifecycle?
  • And to become a full-stack scientist, what skills will you need to equip yourself?
  • Is the path to becoming a data scientist really interesting, exciting, and particularly right for you?




Join Gambaru to answer the above questions at the 8th Technical Event sharing session, which will take place on the afternoon of Saturday, April 16.





The event will have the participation of Mr. Tam Pham, lecturer of Griffith University.






The topic covers

  1. Skillsets in Data Science: an overview of Data Science's skills are available and what it is used for.

  2. Career path in Data Science: Understand the progression and job requirements in Data Science. There are positions that ask for a bit of the knowledge and experience of other job positions.

  3. Resources and experiences in Data Science: How to create 21st century self-study skills and the learnings of their predecessors.

  4. Problems and applications in Data Science: The holistic view and effect to pursue new knowledge and opportunities in Data Science.


Presented by
Mr. Tam Nguyen Thanh
Lecturer @Griffith University | Linkedin

Mr. Tâm is an expert in databases, data science, artificial intelligence, blockchain, and machine learning.


He has a Ph.D. in Computer Science and Data Science from EPFL, Switzerland where his research focused on the area of databases with a special focus on data filtering techniques for both structured and unstructured datasets; trust management for crowdsourcing platforms; deep learning for data lakes, data networks, and streams; as well as human-in-the-loop systems design to facilitate trust-building mechanisms between humans (workers) and machines (algorithms).


His interest span ranges from designing novel algorithms to implementable systems that can be used by individuals or organizations to better understand their world through the lens of big data.


Now he is the advisor of 7 startups in EduTech, FinTech, Mental Health, Nutrition, Business Intelligence, etc.