Full-stack Data Science Engineering is the integration of data science and data engineering.
A full-stack data scientist has a broad understanding of data science product life-cycle shown as blow:
For Chinese introduction, Please see here:
Career Goal:
Software architect: a software expert who makes high-level design choices and dictates technology standards.
Full-Stack data scientist: data science architect who can design application system in big data context, work on complicated data integration, and feature engineering and modeling.
The course is recommended to:
– Young professionals who want to leverage their career
– Students who want to enter the data science job market within 8 to 12 months
Content:
-
Data Analytics and Modeling:
Data processing with Pandas and Numpy
Statistics modeling with R
Machine learning with Scikit-Learn
Distributed machine learning
Deep learning
Full-cycle data analytics
-
Data Science Application with big data platform:
Hadoop platform
Spark platform
Hive (SQL) distributed database
Pig big data processing
No-SQL database
Real-time data analytics
Lambda architecture
Data science application design
-
Broad domain knowledge:
Computation algorithms such as searching and sorting
Recommendation system
Finance big data analytics
Supply chain management
GIS (Geographic Information System) big data application
Natural language processing
Healthcare big data analytics
