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CHAPTER 1: What is Data Analytics
1. R tools
and their uses in Business Analytics
2. Objectives
3. Analytics
4. Where is
analytics applied?
5. Responsibilities
of a data scientist
6. Problem
definition
7. Summarizing
data
8. Data
collection
CHAPTER 2: About R
1. Difference
between R and other analytical languages
2. Different
data types in R
3. Built in
functions of R: seq(), cbind (), rbind(), merge()
4. Subsetting
methods
5. Use of
functions like str(), class(), length(), nrow(), ncol(),head(), tail()
CHAPTER 3: Data manipulation in R
1. Steps
involved in data cleaning
2. Problems
and solutions for Data cleaning
3. Data
inspection
4. Use of
functions grepl(), grep(), sub()
5. Use of
apply() function
6. Coerce
the data
CHAPTER 4: Data Import techniques
1. How R
handles data in a variety of formats
2. Importing
data from csv files, spreadsheets and text files
3. Import
data from other statistical formats like sas7bdat and sps
4. Packages
installation used for database import
5. Connect
to RDBMS from R using ODBC and basic SQL queries in R
6. Basics of
Web Scraping
CHAPTER 5: Exploratory Data analysis
1. Understanding
the Exploratory Data Analysis(EDA)
2. Implementation
of EDA on various datasets
3. Boxplots
4. Understanding
the cor() in R
5. EDA
functions like summarize()
6. llist()
7. Multiple
packages in R for data analysis
8. Segment
plot HC plot in R
CHAPTER 6: Data Visualization in R
1. Understanding
on Data Visualization
2. Graphical
functions present in R
3. Plot
various graphs like tableplot
4. Histogram
5. Box Plot
6. Customizing
Graphical Parameters to improvise the plots
7. Understanding
GUIs like Deducer and R Commander
8. Introduction
to Spatial Analysis
CHAPTER 7: Data Mining: Clustering Techniques
1. Introduction
to Data Mining
2. Understanding
Machine Learning
3. Supervised
and Unsupervised Machine Learning Algorithms
4. K-means
Clustering
CHAPTER 8: Data Mining: Association Rule Mining and
Sentiment Analysis
1. Association
Rule Mining
2. Sentiment
Analysis
CHAPTER 9: Linear and Logistic Regression
1. Linear
Regression
2. Logistic
Regression
3. CHAPTER
10: Anova
4. Anova
5. CHAPTER
11: Predictive Analysis
6. Predictive
Analysis
CHAPTER 12: More on Data Mining
1. Decision
Trees
2. Algorithm
for creating Decision Trees
3. Greedy
Approach: Entropy and Information Gain
4. Creating
a Perfect Decision Tree
5. Classification
Rules for Decision Trees
6. Concepts
of Random Forest
7. Working
of Random Forest
8. Features
of Random Forest
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