The bootcamp will be oriented towards causal relationship models and recommendation systems, explaining the mathematical theory explanation of the model, parameter selection, and programming skills, and completely leading students to complete two complex data science projects from data collection and cleaning to modeling output. . Students can define their own questions of interest, select data sets under the guidance of instructors, and present their complete models and explanations at the end of the class.

It is mainly aimed at Master students or PhD students with a strong mathematical background and students seeking jobs in data science and artificial intelligence in North America. This bootcamp looks forward to helping students who want to apply for data science jobs stand out and lock in North American technical job offers in advance.

The total duration of the bootcamp is four weeks &12 class hours;. In the first week, the mentor communicates with the students online, assigns tasks related to software learning, and guides the students to use SQL, Python, Tableau and other data analysis and visualization tools; in the second week, the mentor will conduct training based on the students' situation. Checking and filling gaps in knowledge points and improving skills; each week from the third to fourth weeks will provide students with a unique data analysis case and provide data. The utilization methods include: descriptive and predictive data analysis, A/B Testing, and logistic regression. , machine learning, etc., and finally submit three data analysis project research reports.

It is mainly aimed at students who apply for business analysis, data analysis, and other relevant fields' position. By completing three popular big data projects in the industry, students can understand the most cutting-edge big data transformation trends and become proficient in the techniques, methods, and ways of thinking required for big data analysis.

The total duration of the bootcamp is four weeks. In the first week, the instructor communicates with the students online, introduces basic concepts, and learns Spark data processing; in the second week, they learn Spark text processing and machine learning related operations, and complete a simple sentiment analysis model. In the last two weeks, you will apply the knowledge you have learned and complete a practical project using spark. This project is based on Yelp data and uses both user data and review text to build a fake review detection system

Mainly aimed at students with a science and engineering background who hope to engage in data science-related work in the future. By completing typical spark projects in the industry, students can master the skills and thinking methods required for the next data science project in the big data era, greatly improving their competitiveness in applications and job hunting.