首先是algorithms for data science这门课，算法课的内容主要就是算法和数据结果，教学内容还是很丰富的，...
DA / 课程设置 基础课程6学分:QBUS5001 Foundation in Data Analytics for Business 专业必修核心课程6学分:BUSS6002 Data Science in Business 专业选修课程18学分,选3门课:QBUS6810 Statistical Learning an…
Data Science 学生必须从以下3个范围里面选择5门课。 其中每个范围至少一门课。 其中两个范围里必须有2门课。 Databases & Data Mining: COMP9313 Big Data Management COMP9315 Database Systems Implementation COMP9318 Data Warehousing & Data Mining (Data Science和AI双方向的选修课) COMP9319 Web Data Compre...
Data science is a dynamic and growing career field that demands knowledge and skills-based in SQL to be successful. This course is designed to provide you with a solid foundation in applying SQL skills to analyze data and solve real business problems. Whether you have successfully completed the...
Data Science Coursera Plus Course Auditing Coursera University of Colorado System Michael G. Kahn Laura K. Wiley (ISC)² Education & Training Statistics & Data Analysis Data Science USA Intermediate 4 Weeks 1-4 Hours/Week Yes, Paid Exam and/or Final Project ...
I also demonstrate Tableau in my corporate training work and various presentations on data visualization, data mining, and data science. I often tell people that Microsoft Excel will do everything it can to make your chart terrible and break rules by default while Tableau will do what it can ...
To this effect we compare the grades of the students in the first semester to their performance in the final exams. The outcome is quite interesting. The members of the science club exhibited a remarkable resilience in keeping their grade level, compared not only to the rest of the class ...
Causal Estimation methods for Machine learning and Data Science Part III – Instrument Variable Analysis 1.0 Introduction In the past two blogs of this series we’ve been discussing causal estimation, a very important subject in data science, we delved into causal estimation using regression method ...
The same intuition is applied to other materials science use cases with features that are long in one or two dimensions; for example, delamination in carbon fiber composites, pore space in gas-bearing shale, thin films in power structures, layer-wise metrology of semiconduc...
1. Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts ...