Syllabus
|
Course Code: MT-CSE-20-23 Course Name: Elective – III - (i) Data Preparation and Analysis |
||
| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
|---|---|---|
| 1 | Data Exploration as a Process, Data Mining, Motivation behind data mining, Data Preparation: Inputs, Outputs, Data Anomalies: Missing Value, Noise, Inconsistency, Incomplete, Modelling Tools and data preparation, Stages of Data Preparation, Data Discovery, Data Characterization, Data Set Assembly. | |
| 2 | Data Cleaning: Knowledge Discovery Process, Consistency Checking, Heterogeneous and Missing Data, Missing Values Replacement Policies, Types of Missing Data, Techniques of Dealing with Missing Data, Data Transformation, Data Transformation Process , Types of Data Transformation, Benefits and Limitations, Data Segmentation. | |
| 3 | Exploratory Analysis: Descriptive and Comparative Statistics, Clustering and Association, Visualization: Designing Visualizations, Time Series, Geolocated Data, Correlations and Connections, Hierarchies and Networks, Interactivity. | |
| 4 | R: Advantages of R over other Programming Languages, Working with Directories and Data Types in R, Control Statements, Loops, Data Manipulation and integration in R, Exploring Data in R: Data Frames, R Functions for Data in Data Frame, Loading Data Frames, Decision Tree packages in R, Issues in Decision Tree Learning, Hierarchical and K-means Clustering functions in R, Mining Algorithm interfaces in R. | |
