Search this site
Embedded Files
Skip to main content
Skip to navigation
III BCA DS
Home
Syllabus
SYLLABUS
Unit 1 - Social Media Analytics & its need
SESSION 2 - These strategies affect a range of business activity
SESSION 3 - Getting Started with R and Social Media Analytics
SESSION 4 - Understanding Social Media
SESSION 5 - Advantages & Disadvantages of social media
SESSION 6 - Advantages and Significance of Social Media
SESSION 7 - Disadvantages and Pitfalls of Social Media
SESSION 8 - Social media analytics
SESSION 9 - Social media analytics
SESSION 10 - Opportunities and Challenges in Social Media Analytics
SESSION 11 - Getting Started with R & Environment Setup
SESSION 12 - Data Types & Data Structures in R: Vectors
SESSION 13 - Arrays, Matrices, and Lists in R
SESSION 14 - Data Frames in R
SESSION 15 -Functions in R - Built-in Functions
SESSION 16 - User-Defined Functions in R
SESSION 17 - Functions in R - Built-in Functions
SESSION 18 - Conditional Constructs in R
SESSION 19 - Advanced Operations in R
SESSION 20 - Understanding apply(), lapply(), sapply(), tapply() & mapply
unit 2 - Visualizing data
Trend analysis
Managing Packages in R
Managing packages
Sentiment analysis in R
Flickr Data Analysis
Understanding interestingness – similarities
Understanding interestingness – similarities
Follower Graph Analysis
Visualizing data
Data analytics - Analytics workflow
Machine learning techniques
Text Analytics
Registering an application
Connecting to Twitter using R
Extracting sample Tweets
Opinion summarization
Key concepts of sentiment analysis –Subjectivity, Sentiment polarity
Features
Accessing Flickr's data
Understanding Flickr data
Preparing the data -Building the classifier
Unit 3 - Overview of text mining:
From textual information to numerical vectors
Parts-of-speech tagging:
Sentence boundary determination
Vector generation for prediction:
Multiword features:
Lemmatization Inflectional stemming:
Is text different from numbers?
Structured or unstructured data:
What’s special about text mining?
Document classification:
Information retrieval
Text Clustering
K-Means Clustering
Information Extraction
Information Extraction Applications
Adding Semantics to the Information Extraction Process
Unit 4 - Using Text for Prediction
Recognizing that Documents Fit a Pattern
Learning to Predict from Text
similarity and Nearest-Neighbor Methods
Document Similarity
Decision Rules
Decision Trees
Scoring by Probabilities
Linear Scoring Methods
Evaluation of Performance -Estimating Current and Future Performance
Getting the Most from a Learning Method
Errors and Pitfalls in Big Data Evaluation
Information Retrieval and Text Mining
Is Information Retrieval a Form of Text Mining?
Key Word Search
Nearest-Neighbor Methods
Measuring Similarity -Shared Word Count
Word Count and Bonus, Cosine Similarity
Web-Based Document Search - Link Analysis
Document Matching
Inverted Lists
Evaluation of Performance
Schedule
Newsletter
III BCA DS
Home
Syllabus
SYLLABUS
Unit 1 - Social Media Analytics & its need
SESSION 2 - These strategies affect a range of business activity
SESSION 3 - Getting Started with R and Social Media Analytics
SESSION 4 - Understanding Social Media
SESSION 5 - Advantages & Disadvantages of social media
SESSION 6 - Advantages and Significance of Social Media
SESSION 7 - Disadvantages and Pitfalls of Social Media
SESSION 8 - Social media analytics
SESSION 9 - Social media analytics
SESSION 10 - Opportunities and Challenges in Social Media Analytics
SESSION 11 - Getting Started with R & Environment Setup
SESSION 12 - Data Types & Data Structures in R: Vectors
SESSION 13 - Arrays, Matrices, and Lists in R
SESSION 14 - Data Frames in R
SESSION 15 -Functions in R - Built-in Functions
SESSION 16 - User-Defined Functions in R
SESSION 17 - Functions in R - Built-in Functions
SESSION 18 - Conditional Constructs in R
SESSION 19 - Advanced Operations in R
SESSION 20 - Understanding apply(), lapply(), sapply(), tapply() & mapply
unit 2 - Visualizing data
Trend analysis
Managing Packages in R
Managing packages
Sentiment analysis in R
Flickr Data Analysis
Understanding interestingness – similarities
Understanding interestingness – similarities
Follower Graph Analysis
Visualizing data
Data analytics - Analytics workflow
Machine learning techniques
Text Analytics
Registering an application
Connecting to Twitter using R
Extracting sample Tweets
Opinion summarization
Key concepts of sentiment analysis –Subjectivity, Sentiment polarity
Features
Accessing Flickr's data
Understanding Flickr data
Preparing the data -Building the classifier
Unit 3 - Overview of text mining:
From textual information to numerical vectors
Parts-of-speech tagging:
Sentence boundary determination
Vector generation for prediction:
Multiword features:
Lemmatization Inflectional stemming:
Is text different from numbers?
Structured or unstructured data:
What’s special about text mining?
Document classification:
Information retrieval
Text Clustering
K-Means Clustering
Information Extraction
Information Extraction Applications
Adding Semantics to the Information Extraction Process
Unit 4 - Using Text for Prediction
Recognizing that Documents Fit a Pattern
Learning to Predict from Text
similarity and Nearest-Neighbor Methods
Document Similarity
Decision Rules
Decision Trees
Scoring by Probabilities
Linear Scoring Methods
Evaluation of Performance -Estimating Current and Future Performance
Getting the Most from a Learning Method
Errors and Pitfalls in Big Data Evaluation
Information Retrieval and Text Mining
Is Information Retrieval a Form of Text Mining?
Key Word Search
Nearest-Neighbor Methods
Measuring Similarity -Shared Word Count
Word Count and Bonus, Cosine Similarity
Web-Based Document Search - Link Analysis
Document Matching
Inverted Lists
Evaluation of Performance
Schedule
Newsletter
More
Home
Syllabus
SYLLABUS
Unit 1 - Social Media Analytics & its need
SESSION 2 - These strategies affect a range of business activity
SESSION 3 - Getting Started with R and Social Media Analytics
SESSION 4 - Understanding Social Media
SESSION 5 - Advantages & Disadvantages of social media
SESSION 6 - Advantages and Significance of Social Media
SESSION 7 - Disadvantages and Pitfalls of Social Media
SESSION 8 - Social media analytics
SESSION 9 - Social media analytics
SESSION 10 - Opportunities and Challenges in Social Media Analytics
SESSION 11 - Getting Started with R & Environment Setup
SESSION 12 - Data Types & Data Structures in R: Vectors
SESSION 13 - Arrays, Matrices, and Lists in R
SESSION 14 - Data Frames in R
SESSION 15 -Functions in R - Built-in Functions
SESSION 16 - User-Defined Functions in R
SESSION 17 - Functions in R - Built-in Functions
SESSION 18 - Conditional Constructs in R
SESSION 19 - Advanced Operations in R
SESSION 20 - Understanding apply(), lapply(), sapply(), tapply() & mapply
unit 2 - Visualizing data
Trend analysis
Managing Packages in R
Managing packages
Sentiment analysis in R
Flickr Data Analysis
Understanding interestingness – similarities
Understanding interestingness – similarities
Follower Graph Analysis
Visualizing data
Data analytics - Analytics workflow
Machine learning techniques
Text Analytics
Registering an application
Connecting to Twitter using R
Extracting sample Tweets
Opinion summarization
Key concepts of sentiment analysis –Subjectivity, Sentiment polarity
Features
Accessing Flickr's data
Understanding Flickr data
Preparing the data -Building the classifier
Unit 3 - Overview of text mining:
From textual information to numerical vectors
Parts-of-speech tagging:
Sentence boundary determination
Vector generation for prediction:
Multiword features:
Lemmatization Inflectional stemming:
Is text different from numbers?
Structured or unstructured data:
What’s special about text mining?
Document classification:
Information retrieval
Text Clustering
K-Means Clustering
Information Extraction
Information Extraction Applications
Adding Semantics to the Information Extraction Process
Unit 4 - Using Text for Prediction
Recognizing that Documents Fit a Pattern
Learning to Predict from Text
similarity and Nearest-Neighbor Methods
Document Similarity
Decision Rules
Decision Trees
Scoring by Probabilities
Linear Scoring Methods
Evaluation of Performance -Estimating Current and Future Performance
Getting the Most from a Learning Method
Errors and Pitfalls in Big Data Evaluation
Information Retrieval and Text Mining
Is Information Retrieval a Form of Text Mining?
Key Word Search
Nearest-Neighbor Methods
Measuring Similarity -Shared Word Count
Word Count and Bonus, Cosine Similarity
Web-Based Document Search - Link Analysis
Document Matching
Inverted Lists
Evaluation of Performance
Schedule
Newsletter
Syllabus
PAD21201J - DATA VIZUALIZATION.pdf
Report abuse
Page details
Page updated
Report abuse
