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
Kaggle: The Home of Data Science,
Data Science 101 | Learning To Be A Data Scientist,


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


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