A

1. Affinity analysis

2. Aggregate() function

3. Akaike information criterion (AIC) value

4. Amazon

5. Apache Hadoop ecosystem

6. Apache Hadoop YARN

7. Apache HBase

8. Apache Hive

9. Apache Mahout

10. Apache Oozie

11. Apache Pig

12. Apache Spark

13. Apache Storm

14. Apply() function

15. Arrays, R

16. Artificial intelligence

17. Association-rule analysis

    1. association rules

    2. if-then

    3. interpreting results

    4. market-basket analysis

    5. rules

    6. support

18. Association rules/affinity analysis

B

1. Bar plot

2. Bayes theorem

3. Bias-variance erros

4. Big data

   1. analysis

   2. analytics, future trends

      1. addressing security and compliance

      2. artificial intelligence

      3. autonomous services for machine learning

      4. business users

      5. cloud

      6. data lakes

      7. growth of social media

      8. healthcare

      9. in-database analytics

      10. in-memory analytics

      11. Internet of Things

      12. migration of solutions

      13. prescriptive analytics

      14. real-time analytics

      15. vertical and horizontal applications

      16. visualization at business users

      17. whole data processing

   3. characteristics

   4. ecosystem

   5. use of

5. Big data analytics

6. Binomial distribution

7. Bivariate data analysis

8. Bootstrap aggregating/bagging

9. Boxplots

10. Business analytics

    1. applications of

       1. customer service and support areas

       2. human resources

       3. marketing and sales

       4. product design

       5. service design

    2. computer packages and applications

    3. consolidate data from various sources

    4. drivers for

    5. framework for

    6. infinite storage and computing capability

    7. life cycle of project

    8. programming tools and platforms

    9. required skills for business analyst

       1. data analysis techniques and algorithms

       2. data structures and storage/warehousing techniques

       3. programming knowledge

       4. statistical and mathematical concepts

11. Business Analytics and Statistical Tools

12. Business analytics process

    1. data collection and integration

       1. data warehouse

       2. HR and finance functions

       3. IT database

       4. manufacturing and production process

       5. metadata

       6. NoSQL databases

       7. operational database

       8. primary source

       9. sampling technique

       10. secondary source

       11. variable selection

    2. definition

    3. deployment

    4. functions

       1. collection and integration

       2. deployment

       3. evaluation

       4. exploration and visualization

       5. management and review report

       6. modeling techniques and algorithms

       7. preprocessing

       8. problem, objectives, and requirements

    5. historical data

    6. identifying and understanding problem

    7. life cycle

    8. management report and review

       1. data cleaning carried out

       2. data set use

       3. deployment and usage

       4. issues handling

       5. model creation

       6. prerequisites

       7. problem description

    9. model evaluation

       1. confusion matrix

       2. gain/lift charts

       3. holdout partition

       4. k-fold cross-validation

       5. ROC chart

       6. test data

       7. validation

    10. model evaluationt

        1. training

    11. preprocessing

1. real-time data

2. regression model

3. root-mean-square error

4. sequence of phases

5. techniques and algorithms

   1. data types

   2. descriptive analytics

   3. machine learning

   4. predictive analytics

C

1. Classification techniques

   1. decision tree

1. disadvantage

2. k-nearest neighbor (K-NN)

3. probabilistic models

   1. advantages and limitations

   2. bank credit-card approval process

   3. Naïve Bays

4. R

   1. cross-validation error

   2. CSV format

   3. functions

   4. misclassification error

   5. plotting deviance vs. size

   6. school data set

   7. testing model

   8. training set and test set

   9. tree() package

5. random forests

6. step process

7. types

1. Cloud

2. Cloudera

3. Clustering analysis

   1. average linkage (average distance)

   2. categorical variable

   3. centroid distance

   4. complete linkage (maximum distance)

   5. Euclidean distance

   6. finance

   7. hierarchical clustering

      1. algorithm

      2. dendrograms

      3. limitations

   8. hierarchical method

   9. HR department

   10. Manhattan distance

   11. market segmentation

   12. measures distance (between clusters)

   13. mixed data types

   14. n records

   15. nonhierarchical clustering

1. nonhierarchical method

2. overview

3. pearson product correlation

4. purpose of

5. single linkage (minimum distance)

1. Coefficient of determination

2. Comma-Separated Values (CSV)

3. Computations on data frames

   1. analyses

   2. EmpData data

   3. in R

   4. scatter plots

4. Continuous data

5. Control structures in R

   1. for loops

   2. if-else

   3. looping functions

      1. apply() function

      2. cut() function

      3. lapply() function

      4. sapply() function

      5. split() function

      6. tapply() function

   4. while loops

   5. writing functions

6. Correlation

7. Correlation coefficient

8. Correlation graph

9. Cross-Industry Standard Process for Data Mining (CRISP-DM)

10. Cut() function

11. Cutree() function

D

1. Data

2. Data aggregation

3. Data analysis, R

   1. reading and writing data

      1. from Microsoft Excel file

      2. from text file

      3. from web

4. Data analysis tools

5. Data analytics

6. Data exploration and visualization

   1. descriptive statistics

   2. goal of

   3. graphs

      1. box/whisker plot

      2. correlation

      3. density function

      4. histograms

      5. notched plots

      6. registered users vs. casual users

      7. scatter plot matrices

      8. scatter plots

      9. trellis plot

      10. types of

      11. univariate analysis

   4. normalization techniques

   5. phase

   6. tables

   7. transformation

   8. View() command

7. Data frames, R

8. Data lakes

9. Data Mining Group (DMG)

10. Data science

11. Data structures

    1. in R

       1. arrays

       2. data frames

       3. factors

       4. lists

       5. matrices

12. Decision tree structure

    1. bias and variance

    2. classification rules

    3. data tuples

    4. entropy/expected information

    5. generalization errors

    6. gini index

    7. impurity

    8. induction

    9. information gain

    10. overfitting and underfitting

    11. overfitting errors

        1. CART method

        2. pruning process

        3. regression trees

        4. tree growth

    12. recursive divide-and-conquer approach

    13. root node

13. Deep learning

14. Dendrograms

15. Density function

16. Descriptive analytics

    1. computations on dataframes

1. graphical

1. Maximum depth of river

2. mean depth of the river

3. median of the depth of river

4. notice, sign board

5. percentile

6. population and sample

7. probability

8. quartile 3

9. statistical parameters

1. Discrete data types

2. Durbin-Watson test

E

1. Economic globalization

2. Ecosystem, big data

3. Euclidean distance

4. Extensible Markup Language (XML)

F

1. Factors, R

2. for loops

G

1. Graphical description of data

   1. bar plot

   2. boxplot

   3. histogram

   4. plots in R

      1. code

      2. creation, simple plot

      3. plot()

      4. variants

2. Gross domestic product (GDP)

H

1. Hadoop Distributed File System (HDFS)

2. Hadoop ecosystem

   1. advantages

3. Hadoop framework

4. Healthcare, big data

5. Hierarchical clustering

   1. algorithm

   2. closeness

   3. dendrograms

   4. limitations

6. Histograms

7. Huge computing power

8. Huge storage power

9. Hybrid Transactional/Analytical Processing (HTAP)

10. Hypothesis testing

I

1. If-else structure

2. In-database analytics

3. In-memory analytics

4. Integrated development environment (IDE)

5. Internet of Things

6. Interquartile Range (IQR) method

7. Interval data types

J

1. JavaScript Object Notation (JSON) files

2. JobTracker

K

1. k-fold cross-validation

2. K-means algorithm

   1. case study

      1. outliers verification

      2. relevant variables

      3. scores() function

      4. standardized values

      5. test data set

   2. data points (observations)

      1. aggregate() function

      2. cutree() function

      3. data observations

      4. dendrogram

      5. dist() function

      6. hclust() function

      7. hierarchical partitioning approach

      8. library(NbClust) command

      9. NbClust() command

      10. observations

      11. plot() function

      12. rect.hclust() function

      13. rent and distances

      14. selected approaches

   3. goal

   4. k-means algorithm

   5. limitations

   6. objective of

   7. partition clustering methods

3. kmeansruns() function

4. k-nearest neighbor (K-NN)

L

1. lapply() function

2. Lasso Regression method

3. Linear regression

   1. assumptions

   2. correlation

      1. attrition

      2. cause-and-effect relationship

      3. coefficient

      4. customer satisfaction

      5. employee satisfaction index

      6. sales quantum

      7. strong/weak association

   3. data frame creation

   4. degrees of freedom

   5. equal variance, variable

   6. equation

   7. F-statistic

   8. function

   9. independent and dependent variable

   10. innovativeness

   11. intercept

   12. least squares method

   13. linear relationship

   14. marketing efforts

   15. multiple R-squared

   16. predict() function

   17. profitability

   18. properties

   19. p-value

   20. quality-related statistics

   21. R command

   22. residuals

   23. residual standard error

   24. sales personnel

   25. standard error

   26. testing

       1. independence errors

       2. linearity

       3. normality

   27. validation

       1. crPlots(model name) function

       2. gvlma() function

       3. scale-location plot

   28. value of significance

   29. work environment

4. Lists, R

5. Logistic regression

   1. binomial distribution

   2. data creation

   3. glm() function

   4. lm() function

   5. logistic regression model

   6. model creation

      1. comparison

      2. conclusion

      3. deviance

      4. dispersion

      5. glm() function

      6. model fit verification

      7. multicollinearity

      8. residual deviance

      9. summary of

      10. variables

      11. warning message

   7. multinomial logistic regression

   8. read.csv() command

   9. regularization

1. training and testing

   1. prediction() function

   2. response variable

   3. validation

1. Looping functions

   1. apply() function

   2. cut() function

   3. lapply() function

   4. sapply() function

   5. split() function

   6. tapply() function

M

1. Machine learning

2. Manhattan distance

3. MapReduce

4. Market-basket analysis (MBA)

1. Matrices, R

2. Measurable data

1. Microsoft Azure

2. Microsoft Business Intelligence and Tableau

3. Microsoft Excel file, reading data

4. Microsoft SQL Server database

5. Minkowski distance

6. Min-max normalization

7. Mtcars Data Set

8. Multicollinearity

9. Multinomial logistic regression

10. Multiple linear regression

    1. assumptions

    2. components

    3. correlation

    4. data

    5. data-frame format

    6. discrete variables

    7. equation

    8. lm() function

    9. multicollinearity

    10. predictors

    11. response variable

    12. R function glm()

    13. stepwise

    14. subsets approach

    15. training and testing model

    16. validation

        1. crPlots

        2. Durbin-Watson test

        3. ncvTest(model name)

        4. normal Q-Q plot

        5. qqPlot

        6. residuals vs. fitted

        7. residuals vs. leverage plot

        8. scale-location plot

        9. Shapiro-Wilk normality test

11. multiple linear regression equation

1. Multiple regression

2. myFun() function

N

1. Naïve Bays

2. Natural language processing (NLP)

3. NbClust() function

4. Nominal data types

5. Nonhierarchical clustering

1. Non-linear regression

2. Normal distribution

3. Normalization techniques

4. NoSQL

5. Null hypothesis

O

1. Online analytical processing (OLAP)

2. Open Database Connectivity (ODBC)

3. Ordinal data types

4. Overdispersion

P

1. Packages and libraries, R

2. Partition clustering methods

3. Poisson distribution

4. Prediction

5. Predictive analytics

   1. classification

   2. regression

6. Predictive Model Markup Language (PMML)

7. Preprocessing data

   1. preparation

      1. duplicate, junk, and null characters

      2. empty values

      3. handling missing values

   2. R

      1. as.numeric() function

      2. complete.cases() function

      3. data types

      4. factor levels

      5. factor() type

      6. head() command

      7. methods

      8. missing values

      9. names() and c() function

      10. table() function

      11. vector operations

   3. types

8. Probabilistic classification

   1. advantages and limitations

   2. bank credit-card approval process

   3. Naïve Bays

9. Probability

   1. concepts

   2. distributions

1. events

2. mutually exclusive events

3. mutually independent events

4. mutually non-exclusive events

1. Probability distributions

   1. binomial

   2. normal

   3. poisson

2. Probability sampling

3. Property graphs (PG)

Q

1. Qualitative data

2. Quantitative data

R

1. R

   1. advantages

   2. console

   3. control structures

      1. for loops

      2. if-else

      3. looping functions

      4. while loops

      5. writing functions

   4. data analysis

      1. reading and writing data

   5. data analysis tools

   6. data structures

      1. arrays

      2. data frames

      3. factors

      4. lists

      5. matrices

   7. glm() function

   8. installation

      1. RStudio interface

   9. interfaces

   10. library(NbClust) command

   11. lm() function

   12. Naïve Bays

   13. objects types

   14. packages and libraries

   15. pairs() command

   16. programming, basics

       1. assigning values

       2. creating vector

   17. View() command

2. Random forests

3. Random sampling

4. Ratio data types

5. read.csv() function

6. read.table() function

7. Receiver operating characteristic (ROC)

8. rect.hclust() function

9. Regularization

   1. cv.fit() model

   2. cv.glmnet() function

   3. generic format

   4. glmnet() function

   5. glmnet_fit command

   6. methods

   7. plot() function

   8. plot(cv.fit)

   9. predict() function

   10. print() function

   11. shrinkage methods

   12. variable

10. Ridge Regression method

11. RODBC package

12. Root-mean-square error (RSME)

13. RStudio

    1. installation error

    2. installing

    3. interface

    4. output

    5. window

S

1. sapply() function

2. Scatter plot matrices

3. Scatter plots

   1. analysis of data

   2. changes, relationship

   3. Coding

   4. created in R

   5. EmpData1

4. seq_along() function

5. Shrinkage methods

6. Simple regression

7. split() function

8. Standard deviations

9. Statistical parameters

   1. mean

      1. data set

      2. downside of

      3. in R

      4. limitations

      5. profit and effective

      6. single parameter

      7. usage of

   2. median

   3. mode

   4. quantiles

   5. range

   6. standard deviation

   7. summary(dataset)

   8. variance

10. Storm

11. Stratified sampling

12. Supervised machine learning

13. Systematic sampling

T

1. tapply() function

2. Text file, reading data

3. Transformation

4. Trellis graphics

U

1. Univariate analysis

2. Unsupervised machine learning

   1. association-rule analysis

      1. association rules

      2. if-then

      3. interpreting results

      4. market-basket analysis

      5. rules

      6. support

   2. clustering

V

1. Variance errors

2. Variance inflation factor (VIF)

3. Variety

4. Velocity

5. Visualization

   1. Workflow

6. Visualization

W, X, Y

1. Web, reading data

2. while loops

3. Whole data processing

Z

1. Z-score normalization