Section outline

  • General

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    Daffodil International University

    Department of Computer Science and Engineering (CSE)

    		  

    Course Code:

    CSE506

    Course Title:

    Advance Data Analytics

    Program:

    M.Sc. in CSE and MIS

    Faculty:

    Faculty of Science and Information Technology (FSIT)

    Semester:

    Summer 2025

    Year:

    2025

    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

    Module 1: Introduction to Advanced Data Analytics

    • 1.1 Overview of Data Analytics
      • Evolution of data analytics
      • Types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
      • Applications of advanced analytics in various industries
    • 1.2 Data Analytics Lifecycle
      • Problem definition
      • Data collection and preparation
      • Model building and validation
      • Deployment and monitoring
    • 1.3 Tools and Technologies
      • Overview of tools: Python, R, SQL, Tableau, Power BI, Spark, etc.
      • Introduction to cloud platforms: AWS, Google Cloud, Azure

  • Week-2

    Module 2: Data Preprocessing and Cleaning

    • 2.1 Data Collection and Integration
      • Data sources: Structured, semi-structured, and unstructured data
      • APIs, web scraping, and database integration
    • 2.2 Data Cleaning
      • Handling missing data
      • Outlier detection and treatment
      • Data normalization and transformation

  • Week-3

    • Data Preprocessing

    • 2.3 Feature Engineering
      • Feature selection and extraction
      • Dimensionality reduction techniques (PCA, t-SNE)
      • Encoding categorical variables

  • Week-4

    Module 3: Statistical Modelling for Decision Making

    • Hypothesis testing (t-tests, ANOVA, Chi-square)

    • Regression analysis (linear & logistic regression)

  • Week-5

    Module 3: Statistical Modelling for Decision Making

    • Time-series forecasting (moving averages, trend analysis)

    • Hands-on: Predicting business trends using Excel/Tableau

  • Week-6

    Module 4: Machine Learning for Advanced Analytics

    • 4.1 Supervised Learning
      • Regression models (Linear, Polynomial, Ridge, Lasso)
      • Classification models (Decision Trees, Random Forest, SVM, k-NN)
      • Model evaluation metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC)

  • Week-7

    Midterm Examination

  • Week-8

    Module 4: Machine Learning for Advanced Analytics

    • 4.2 Unsupervised Learning

  • Week-9

    Module 4: Machine Learning for Advanced Analytics

    • 4.2 Unsupervised Learning

  • Week-10

    Module 4: Machine Learning for Advanced Analytics

    • 4.3 Advanced Machine Learning Techniques
      • Ensemble methods (Bagging, Boosting, Stacking)
      • Neural networks and deep learning basics
      • Time series analysis and forecasting (ARIMA, LSTM)

  • Week-11

    Module 6: Big Data & AI in Modern Analytics

    ·       Introduction to Big Data (Hadoop, Spark – conceptual)

    ·       AI & automation in analytics (ChatGPT for data insights)

    ·       Limitations and challenges in real-world data analysis

     

     

  • Week-12

    Module 6: Data Visualization and Storytelling

    • 6.1 Principles of Data Visualization
      • Choosing the right chart type
      • Design principles for effective visualizations
    • 6.2 Advanced Visualization Tools
      • Tableau, Power BI, and Python libraries (Matplotlib, Seaborn, Plotly)
      • Interactive dashboards and reports
    • 6.3 Data Storytelling
      • Communicating insights effectively
      • Creating narratives with data

  • Week 13

    Module 9: Capstone Project

    • 9.1 Project Planning
      • Defining the problem statement
      • Data collection and preparation
    • 9.2 Implementation
      • Applying advanced analytics techniques
      • Building and validating models
    • 9.3 Presentation
      • Visualizing results
      • Delivering insights and recommendations

  • Week-14

    Final Examination