Topic outline

  • General

    Daffodil International University

    Department of Computer Science and Engineering (CSE)

    Course Code:

    CSE506

    Course Title:

    Advance Data Analytics

    Program:

    M.Sc. in CSE

    Faculty:

    Faculty of Science and Information Technology (FSIT)

    Semester:

    Spring 2024

    Year:

    2024

    Credit:

    3

    Contact Hour:

    3hr/week

    Course Level:

    L1T1

    Prerequisite:




    Instructor Name:

     Dr. Fizar Ahmed

    Designation:

    Associate Professor

    Email:

    fizar.cse@diu.edu.bd

    Office Address:

    301, AB-4, Daffodil Smart City, Ashulia.

    Course Description (from syllabus)/Rational:

    As digitization is touching our lives in almost all spheres computer is omnipresent due to this. Computers are present everywhere from schools to colleges to banks to post offices to professional world. There is a lot of uses of computers in the universities. The biggest example for the same is that Google Classroom is helping to teach this course online and recipients will be accessing it and using it online. Many of our daily tasks are done on the computer too. CSE112: Computer Fundamentals is one prominent core courses that is concerned of the basics of using a computer. This course has been designed for the newly admitted students with little to no computer experience.


  • Week-1

    Les. 1

    Introduction of the Course Teacher to the students and vice versa; Sharing the course information, e.g. Course Teacher’s information, different information of BLC course, course contents, and course delivery plan and so on.


    Python Data Types, Python Data Structure, Control-Flow Statement, Loop Statement, Python Functions.





    • Week-2

      Discussion on data: types of data, quality of data, data preprocessing, and similarity and dissimilarity measures. (Ref.: TB-I [Chapter 2])

      Continuation the lecture on Lesson-5, i.e. data preprocessing. (Ref.: TB-I [Chapter 2])


      Discussion on data exploration: exploratory data analysis (EDA), summary statistics, data visualization, and OLAP and multidimensional data analysis with pandas, numpy, seaborn, matplotlib.

    • Week-3

      Discussion on data: types of data, quality of data, data preprocessing, and similarity and dissimilarity measures. (Ref.: TB-I [Chapter 2])

      Continuation the lecture on Lesson-5, i.e. data preprocessing. (Ref.: TB-I [Chapter 2])


      Discussion on data exploration: exploratory data analysis (EDA), summary statistics, data visualization, and OLAP and multidimensional data analysis with pandas, numpy, seaborn, matplotlib.

      • Week-4

        Discussion on classification: a linear classifier, decision trees, model overfitting, evaluating the performance of a classifier etc. (Ref: TB-I [Chapter 4])


        Hands on classification: a linear classifier, decision trees, model overfitting, evaluating the performance of a classifier with Python

        • Week-5

          Discussion on nearest - neighbor classifiers and Bayesian classifiers. (Ref: TB-I [Chapter 4])


          Hands on nearest - neighbor classifiers and Bayesian classifiers with Python

          • Week-6

            Discussion on Decision Tree classifiers. Classification and Prediction, Classification: tree-based approaches


            Hands on Decision Tree classifiers. Classification and Prediction, Classification: tree-based approaches with Python


          • Week-7

            Midterm Examination

            • Week-8

              Continuation of Lesson13, i.e. cluster analysis. (Ref: TB-I [Chapter 8])


              Discussion on association rule mining: basics, frequent itemset generation, rule generation, etc. (Ref: TB-I [Chapter 6])

            • Week-9

              Discussion on cluster analysis: basics, K-means, agglomerative hierarchical clustering, DBSCAN. (Ref: TB-I [Chapter 8])


              Hands on cluster analysis: basics, K-means, agglomerative hierarchical clustering with Python


            • Week-10

              Discussion on artificial neural networks: perceptron, multilayer artificial neural network, etc. (Ref.: TB-I [Chapter 5], TB-II [Chapter 4])


              Hands on artificial neural networks: multilayer artificial neural network, etc. with Tensorflow.

              • Week-11

                Project Discussion & Review of all week.



                • Week-12

                  Project Demonstration

                  • Week-13

                    Final Examination