Course description
The course teaching ideas and goals
The data-driven decision is becoming more and more important in Business, and Data Science is considered as the sexiest career of twenty-first century. The objective of this course is to introduce the basic knowledge and skills of being as business data analyst. The Goals of this course include:
1) Basic Knowledge: general process of data analysis, such as finding the right data source, adopting appropriate methods, and demonstrating the results effectively.
2) Basic Techniques: mass data management (including SQL and NoSQL data solutions), mass data mining algorithms, and basic statistical modeling techniques.
3) Data analysis team management: attracting, building and nurturing the data science team, managing data analysis projects and etc.
Teaching methods and means
1)Lectures 2)Tutorials 3)Projects
Methods Learning Goal 1 Learning Goal 2 Learning Goal 3
Lectures √ √
Tutorials √ √ √
Projects √ √
The assessment method
Evaluation method Ratio
Exam 70%
Group Projects 20%
Classroom Discussion 10%
The teaching material
1) Galit Shmueli, Nitin R. Patel, Peter C. Bruce (2010) Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. Wiley; 2nd Edition
2) Frank J. Ohlhorst . Big Data Analytics: Turning Big Data into Big Money. Wiley
3) Viktor Mayer-Schönberger, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Eamon Dolan/Mariner Books
Network resource
本团队拥有高性能服务器一台，学院教室配备PC机，可以支持本课程。
网络资源包括:
Kaggle: The Home of Data Science, http://www.kaggle.com/
Data Science 101 | Learning To Be A Data Scientist, http://101.datascience.community/
The teaching effect
商业数据科学 2013-2014学年第二学期 4.91
管理科学 2013-2014学年第二学期 4.86
网络营销与CRM 2013-2014学年第二学期 4.83
网络营销与CRM 2012-2013学年第二学期 4.92
Course plan
Week No Topic Contents
Week 1 Introduction Background,Syllabus,General process
Week 2 Data Preprocess Data Srapping,Data Munging,Data Cleaning
Week 3 Visualization Statistical graphs
Week 4 Regression Linear regression
Week 5 Regression Logistic regression
Week 6 Classification Association Rules
Week 7 Classification k-NN
Week 8 Classification Decision Tree
Week 9 Classification Naive Bayesian
Week 10 Classification Feature Selection
Week 11 Clustering k-Means
Week 12 Clustering Hierarchical Clustering
Week 13 Recommender Systems Collaborative filtering
Week 14 Recommender Systems Singular value decomposition
Week 15 Social Network Analysis Link Analysis
Week 16 Social Network Analysis Community detection
Week 17 Big Data Management MapReduce, Hadoop, NoSQL DB
Week 18 Review Session Summary of the course
