# Data Science and Predictive Analytics on R

4 out of 5
4
6 reviews R is the most popular programming language among statisticians. In fact, it was initially built by statisticians for statisticians. It has a rich package repository with more than 9100 packages with every statistical function you can imagine.R’s expressive syntax allows researchers – even those from non computer science backgrounds to quickly import, clean and analyze data from various data sources.

Programming languages like R give a data scientist superpowers that allow them to collect data in real time, perform statistical and predictive analysis, create visualizations and communicate actionable results to stakeholders.

R has found a lot of use in predictive analytics and machine learning. It has various package for common ML tasks like linear and non-linear regression, decision trees, linear and non-linear classification and many more.Everyone from machine learning enthusiasts to researchers use R to implement machine learning algorithms in fields like finance, genetics research, retail, marketing and health care.

### Main Features

• Understand the fundamentals of Predictive Analytics in R.
• Enhance skills by getting accustomed to the techniques that are currently in demand, such as Hypothesis Testing, Correlations, Chi-Square Test, ANOVA, Linear Regression, Logistic Regression and Time Series.
• Use Predictive Modelling Techniques on data from various fields like sports, marketing, media etc.
• Use Backward Elimination and Forward Selection methods to create statistical models
• Perform Data Manipulation and Preparation before the actual analysis
• Develop constructive approach to solve business queries with R.
• Analyze real time data and perform learnt skills

### Introduction to Analytics

1
Introduction to Excel
2
Conditional Formatting
3
Descriptive Statistics- Measures of Central tendencies
4
Measures of Dispersion, Range, Mean Deviation, Standard Deviation
5
Skewness and Kurtosis

### Understanding Probability and Probability Distribution

1
Introduction to Probability theory
2
Types of probability distribution – Discrete Distribution and Continuous distribution
3
Understanding Probability Mass Function and Probability Density Function
4
Normal Distribution and Standard Normal Distribution
5
Understanding Binomial Distribution and Poisson Distribution
6
Application on Binomial Distribution
7
Application on Normal Distribution

### Introduction to Sampling Theory and Estimation

1
Concept of Population and Sample
2
Introduction to Some important terminologies
3
Parameter and Statistic
4
Properties of a good estimator
5
Standard Deviation and Standard Error
6
Point and Interval Estimation
7
Confidence level and level of Significance
8
Constructing Confidence Intervals
9
Formulation of Null and Alternative hypothesis and performing simple test of Hypothesis

### Introduction to Segmentation Techniques: Factor Analysis

1
Introduction to Factor Analysis and various techniques
2
Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA)
3
KMO MSA test, Bartlett’s Test Sphericity
4
The Mineigen Criterion, Scree plot
5
6
Application of the technique on a case study
7
Interpretation of the result

### Introduction to Segmentation Techniques: Cluster Analysis

1
Introduction to Cluster Analysis and various techniques
2
Hierarchical and Non – Hierarchical Clustering techniques
3
Using Hierarchical Clustering in R
4
Performing K – means Clustering in R
5
Divisive Clustering, Agglomerative Clustering
6
Application of Cluster Analysis in Analytics with Examples with profiling of the clusters and interpretation of the clusters
7
Application of the techniques on a case study
8
Interpretation of the result

### Correlation and Linear Regression

1
Introduction to Pearson’s Correlation coefficient
2
Correlation and Causation- Fitting a simple linear regression model
3
Introduction to CLRM
4
Assumptions of CLRM
5
Understanding the MLRM technique
6
Understanding the related statistic to linear regression
7
Goodness of fit test for linear regression
8
Importing dataset in R to apply linear regression
9
Splitting of dataset – Training and testing
10
Conducting several tests to understand the results obtained
11
Checking for the accuracy of the linear regression model
12
Assessing Collinearity, Heteroskedasticity and Auto – Correlation

### Introduction to categorical data analysis and Logistic Regression

1
Comparison between Liner Regression and Logistic Regression
2
Performing Goodness of fit test of the model
3
Introduction to Percent Concordant, AIC, SC, and Hosmer – Lemeshow
4
Receiver Operating Characteristics (ROC) Curve and Area under Curve (AUC)
5
Interpretation of the model: overall fit of the model and finding out the influential variables using Odds ratio criteria
6
Understanding the ROC testing
7
Checking for the accuracy of the model
8
Application and interpretation using case study

### Introduction to Time Series Analysis

1
What is Time series Analysis, Objectives and Assumptions of Time Series
2
Identifying pattern in Time series data: Decomposition of the time series data
3
Introduction to Various Smoothing techniques: Simple Moving Average, Weighted Moving Average
4
Exponential Smoothing, Holt’s Linear Exponential Smoothing Examples of Seasonality and detecting Seasonality in Time series data
5
Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology
6
Introduction to Autoregressive Moving Average (ARMA) model and Autoregressive Integrated Moving Average (ARIMA) model
7
Building an ARIMA Model
8
Detection of Stationarity, Seasonality in ARIMA Model
9
Detecting the order of AR and MA of ARIMA model
10
Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)
11
Detecting the order by using AIC and BIC criterion
12
Estimation and forecast using R

### Text Mining

1
Introduction to text mining
2
Importance of applying this technique
3
Package required in R to do text mining
4
Understanding WordCloud methodology
5
Performing text mining analysis using a data
6
Understanding the Sentiment Analysis
7
Application of the technique on a dataset
8
Interpretation of the result

1
2
Understanding the Association Rule
3
Understanding Support, Lift and Confidence
4
Application of the technique on a given data
5
Interpretation of the result

### Statistical Significance T Test Chi Square Tests and Analysis of Variance

1
Performing test of one sample mean
2
Difference between two group means (independent sample)
3
Difference between two group means (Paired sample)
4
Performing Chi square tests: Test of Independence
5
Descriptive statistics and inferential statistics
6
T-tests and it’s application on case studies
7
ANOVA testing and its application on case studies
8
Interpretation of the test results
9
Chi-square test of independence
10
Test for correlation and partial-correlation test
11
Performing post-hoc multiple comparisons tests in R using Tukey HSD
12
Performing two-way ANOVA with and without interactions
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