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    【瀏覽課程大綱Syllabus】【列印Print】

  • 一般

  • 1. 09月 14日 - 09月 20日

    Introduction to Big Data
    * Describe the Big Data landscape including
    examples of real world big data problems
    including the three key sources of Big Data:
    people, organizations, and sensors.
    * Explain the V’s of Big Data (volume, velocity,
    variety, veracity, valence, and value) and why each
    impacts data collection, monitoring, storage,
    analysis and reporting.
    * Get value out of Big Data by using a 5-step
    process to structure your analysis.

    • 2. 09月 21日 - 09月 27日

      Introduction to Big Data
      * Describe the Big Data landscape including
      examples of real world big data problems
      including the three key sources of Big Data:
      people, organizations, and sensors.
      * Explain the V’s of Big Data (volume, velocity,
      variety, veracity, valence, and value) and why each
      impacts data collection, monitoring, storage,
      analysis and reporting.
      * Get value out of Big Data by using a 5-step
      process to structure your analysis.

      • 3. 09月 28日 - 10月 4日

        Introduce basic about Statistica Software
        Introduce about Statistica v13 interface, data
        import, descriptive statistics and correlation…
        How to get open data, and import to Statistica

        • 4. 10月 5日 - 10月 11日

           Introduction real case application of big data
          analytics in different fields
          Big Data Analytics Application in agriculture,
          manufacturing, marketing, online retailing, health
          care and banking

          • 5. 10月 12日 - 10月 18日

            Introduction real case application of big data
            analytics in different fields
            Big Data Analytics Application in agriculture,
            manufacturing, marketing, online retailing, health
            care and banking

            • 6. 10月 19日 - 10月 25日

              Data Cleansing & Preparation; Data
              Summarization & Visualization)
              Find out outlier, missing data, combing or
              separate data

              • 7. 10月 26日 - 11月 1日

                Association Rules (Baskets Analysis)
                Detecting relationships or associations between specific values of categorical values in large data
                sets. This is a common task in many data mining
                projects applied to databases containing records of
                customer transactions (e.g. Items purchased by
                each customer). Allow analysts and researchers to
                uncover hidden pattern in large data sets. 

                • 8. 11月 2日 - 11月 8日

                  Association Rules (Baskets Analysis)
                  Detecting relationships or associations between
                  specific values of categorical values in large data
                  sets. This is a common task in many data mining
                  projects applied to databases containing records of
                  customer transactions (e.g. Items purchased by
                  each customer). Allow analysts and researchers to
                  uncover hidden pattern in large data sets.

                  • 9. 11月 9日 - 11月 15日

                    Midterm

                    • 10. 11月 16日 - 11月 22日

                      Classification and Decision Tree
                      classification systems based on multiple covariates
                      or for developing prediction algorithms for a
                      target variable. This method classifies a
                      population into branch-like segments that
                      construct an inverted tree with a root node,
                      internal nodes, and leaf nodes. It commonly used
                      in operations research, specifically in decision
                      analysis, to help identify a strategy most likely to
                      reach a goal, but are also a popular tool in
                      machine learning

                      • 11. 11月 23日 - 11月 29日

                        Classification and Decision Tree
                        classification systems based on multiple covariates
                        or for developing prediction algorithms for a
                        target variable. This method classifies a
                        population into branch-like segments that
                        construct an inverted tree with a root node,
                        internal nodes, and leaf nodes. It commonly used
                        in operations research, specifically in decision
                        analysis, to help identify a strategy most likely to
                        reach a goal, but are also a popular tool in
                        machine learning.

                        • 12. 11月 30日 - 12月 6日

                          Cluster Analysis
                          Handling large data sets and enabling clustering of
                          continuous and/or categorical variables, and
                          providing the functionality for complete
                          unsupervised learning (clustering) for pattern
                          recognition, with all deployment options for
                          predictive clustering.

                          • 13. 12月 7日 - 12月 13日

                            Cluster Analysis Handling large data sets and enabling clustering of
                            continuous and/or categorical variables, and
                            providing the functionality for complete
                            unsupervised learning (clustering) for pattern
                            recognition, with all deployment options for
                            predictive clustering. 

                            • 14. 12月 14日 - 12月 20日

                              Logistics Regression
                              To predict outcome of a categorical dependent
                              variable on the basic of predictor variables.
                              Logistic regression is used in various fields,
                              including machine learning, most medical fields,
                              and social sciences

                              • 15. 12月 21日 - 12月 27日

                                Logistics Regression
                                To predict outcome of a categorical dependent
                                variable on the basic of predictor variables.
                                Logistic regression is used in various fields,
                                including machine learning, most medical fields,
                                and social sciences.

                                • 16. 12月 28日 - 01月 3日

                                  Discriminant Analysis
                                  to develop discriminant functions that are nothing
                                  but the linear combination of independent
                                  variables that will discriminate between the
                                  categories of the dependent variable in a perfect
                                  manner.

                                  • 17. 01月 4日 - 01月 10日

                                    Discriminant Analysis
                                    to develop discriminant functions that are nothing
                                    but the linear combination of independent
                                    variables that will discriminate between the
                                    categories of the dependent variable in a perfect
                                    manner.

                                    • 18. 01月 11日 - 01月 17日

                                      Final Exam