Subject Details
Dept     : CSE
Sem      : 5
Regul    : 2019
Faculty : swathi G
phone  : NIL
E-mail  : swathi.g.cse@snsct.org
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Syllabus

UNIT
1
DATA SCIENCE LIFE CYCLE

Generalizing from Data-Rectangular Data-Relational Databases and SQL- Indexes, Slicing, Sorting- Applying & Plotting Data science Process.

UNIT
2
UNDERSTANDING THE DATA

Data Representation-Data Quality-Exploratory Data Analysis-Data Visualization-Text Mining -Text Analytics.

UNIT
3
DATA SOURCES

Working with Text-Regular Expressions-Web Technologies-REST-Xpath-Handling large Data on a Single Computer -Applications for Machine Learning in Data Science-Introducing Naive Bayes Classifiers-The Rise of graph databases

UNIT
4
CLASSIFICATION

Regression on Probabilities-The Logistic Model-A Loss Function for the Logistic Model-Fitting the Logistic Model-Evaluating the Logistic Models- Multiclass Classification-Data visualization to the End Users

UNIT
5
REPLICABILITY

P-hacking-Dimensionality Reduction-PCA-PCA using Singular value Decomposition-Decision tree-Random Forest.

Reference Book:

1 Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (India) Private Limited, 2013. 2 Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning: with Applications in R”, Springer; First Edition 2013. 3 P. Flach, ―Machine Learning: The art and science of algorithms that make sense of data, Cambridge University Press, 2012.

Text Book:

1.AlpaydinEthem, “Introduction to Machine Learning”, MIT Press, Second Edition, 2010. 2.Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer; Second Edition, 2009.

 

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