Intelligent business solutions are a result of smart data processing. Thanks to new technologies like machine learning that are helping analyze large data sets and ease the work of data scientists by automating processes. According to Glassdoor, data science and machine learning are gaining momentum and recognition and are the top profession in technology.
In this exclusive session, Reza Nickmanesh, Lead Data Analytics, Providence Healthcare, talks about the skills every data professional should have and how the Data Science and Machine Learning: Making Data-Driven Decisions Program helps you be industry-ready.
Agenda for the session
- Opportunities, scope, and future of data science and machine learning
- Being industry-ready with a practical, hands-on learning experience
- Data Science in the healthcare industry
- Q&A session
About Speakers
Reza Nickmanesh
Lead Data Analytics, Providence Health Care, Canada
Reza Nickmanesh is an expert data scientist and comes with a rich experience in data research, analytics, and modeling. He is currently working as the Lead Data Scientist at Providence Health care, a provider of comprehensive healthcare facilities in British Columbia, Canada. Reza is a biomedical graduate and continues to contribute toward creating workable insights with data in the healthcare industry.
Data Science and Machine Learning: Making Data-Driven Decisions Program
The Data Science and Machine Learning: Making Data-Driven Decisions Program has a curriculum carefully crafted by MIT faculty to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions.
This data science program has been designed for the needs of data professionals looking to grow their careers and enhance their data science skills to solve complex business problems. In a relatively short period of time, the program aims to build your understanding of most industry-relevant technologies today such as machine learning to deep learning, to network analytics, to recommendation systems, graph neural networks, and time series. Hence, the program is best suited for learners with prior exposure of having worked with data using some tools, and applying basic algorithms and methods.
