Subject Details
Dept     : CSE
Sem      : 4
Regul    : R2019
Faculty : Poornima devi L
phone  : NIL
E-mail  : poornima.l.cse@snsct.org
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  • Assignment

    Assignment topic is KDD AND CRISP PROCESS and due date is .

  • Assignment

    Assignment topic is KDD AND CRISP PROCESS and due date is .

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:
    A Little History on Methodologies for Data Analytics,
    ; Data Analytics- Types, Tools and Applications,
    KDD Process, CRISP-DM Methodology

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:
    Descriptive Statistics,
    Scale Types,,
    Descriptive Univariate Analysis, Descriptive Bivariate Analysis; Descriptive Multivariate Analysis,
    Multivariate Frequencies, Multivariate Data Visualization, Multivariate Statistics,
    Infographics and Word Clouds,
    Data Quality - Missing Values, Redundant Data, Inconsistent Data, Noisy Data, Outliers,
    Distance Measures ,
    Distance Measures for Non-conventional Attributes,
    Clustering Validation,
    Clustering Techniques - K-means, Centroids and Distance Measures, DBSCAN

  • Question Bank

    Dear Students the Question Bank has been uploaded for the following topics:
    Short Answers,
    Short Answers

  • Youtube Video

    Dear Students the Youtube Video has been uploaded for the following topics:
    KDD Process
    Scale Type

  • Puzzles

    Dear Students the Puzzles has been uploaded for the following topics:
    Puzzle

  • Resource Link

    Dear Students the Resource Link has been uploaded for the following topics:
    Introduction to Data Analytics
    K-Means

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:
    Big Data and Data Science, A Project on Data Analytics

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:
    Binary Classification ,
    Predictive Performance Measures for Classification,
    Distance-based Learning Algorithms,
    K-nearest Neighbor Algorithms, Case-based Reasoning; Probabilistic Classification Algorithms -,
    Binary Classification ,
    Logistic Regression Algorithm, Naive Bayes Algorithm.,
    Regression and its types,
    DA Applications for Text, Web and Social Media,
    Working with Texts, Recommender Systems, Social Network Analysis

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