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RAYALASEEMA UNIVERSITY (UG)
SEMESTER-VI
Paper-VIII:
Elective–A
Foundations
of Data Science
Syllabus
UNIT I
INTRODUCTION TO DATA SCIENCE :Data science process – roles, stages
in data science project – working with data from files – working with
relational databases – exploring data – managing data – cleaning and sampling
for modeling and validation – introduction to NoSQL.
UNIT II
MODELING METHODS : Choosing and evaluating models –
mapping problems to machine learning, evaluating clustering models, validating
models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization
Methods – Linear and logistic regression – unsupervised methods.
UNIT III
INTRODUCTION TO R Language: Reading and getting data into R –
ordered and unordered factors – arrays and matrices – lists and data frames –
reading data from files.
UNIT IV
PROBABILITY DISTRIBUTIONS: Statistical models in R - Binomial, Poisson, Normal distributions – Manipulating objects – data distribution.
UNIT V
DELIVERING RESULTS : Documentation and deployment –
producing effective presentations– Introduction to graphical analysis – plot()
function – displaying multivariate data – matrix plots – multiple plots in one
window - exporting graph - using graphics parameters. Case studies.
Download Links :
| Study Notes Download | : |
| PPTS Unit -I Download | : |
| PPTS Unit -II Download | : |
RAYALASEEMA UNIVERSITY (UG)
SEMESTER-VI
Paper-VIII:
Elective–A
Foundations
of Data Science
Syllabus
UNIT I
INTRODUCTION TO DATA SCIENCE :Data science process – roles, stages
in data science project – working with data from files – working with
relational databases – exploring data – managing data – cleaning and sampling
for modeling and validation – introduction to NoSQL.
UNIT II
MODELING METHODS : Choosing and evaluating models –
mapping problems to machine learning, evaluating clustering models, validating
models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization
Methods – Linear and logistic regression – unsupervised methods.
UNIT III
INTRODUCTION TO R Language: Reading and getting data into R –
ordered and unordered factors – arrays and matrices – lists and data frames –
reading data from files.
UNIT IV
PROBABILITY DISTRIBUTIONS: Statistical models in R - Binomial, Poisson, Normal distributions – Manipulating objects – data distribution.
UNIT V
DELIVERING RESULTS : Documentation and deployment –
producing effective presentations– Introduction to graphical analysis – plot()
function – displaying multivariate data – matrix plots – multiple plots in one
window - exporting graph - using graphics parameters. Case studies.
Download Links :
| Study Notes Download | : |
| PPTS Unit -I Download | : |
| PPTS Unit -II Download | : |
