Data Science With R Training in Pune

• What is Data Analytics?
• Types of Data Sets and Data Models
• Understanding of Business Analytics
• Need of Business Analytics
• Types of Business Analytics
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
• Supply Chain Analytics
• Health Care Analytics
• Marketing Analytics
• Human Resource Analytics
• Data Management and Business Analytics
• Web Analytics and Business Intelligence
• Data Science as a Strategic Asset
• Data Warehousing and OLAP
• Data Visualization using R and Excel
• Data Visualization using Tableau

• Understanding R
• Which Companies Use R?
• Understanding Comprehensive R Archive Network (CRAN)
• How to Install R on Operating Systems?
• How to Install R on Windows from CRAN Website?
• IDEs for R
• R Packages: Installation and Practice
• Understanding R Programming
• Studying Operators in R
• Operators: Arithmetic, Relational, Logical, Assignments
• Statements in R Programming
• Conditional Statements in R
• Break and Next Statement
• If else () Function
• Switch Function
• Scan () Function
• Loops in R
• How to Run an R Script and Batch Script?
• R Functions: Commonly Used and String Functions
• Defining Data Structures in R
• Types of Data Structures
• Vectors and Scalars
• Colon Operator
• Matrices
• Elements: Vector, Matrix, Array
• Understanding Data Frames
• Factors and Lists
• How to Import Files in R?
• How to Import an Excel File?
• How to Import Minitab File?
• Importing Table and CSV Files
• Importing Data from SQL Databases
• How to Export Files from R?

•How to Export Files from R?
• Types of Apply Functions
• Apply () Function: Lapply, Sapply, Tapply
• Vapply () Function, Mapply () Function
• Understanding Dplyr Package
• R Data Structures – Vectors, Factors, Lists, Data Frames, Matrixes and Arrays
• Managing Data with R
• Saving and Loading R Data Structures
• Importing and Saving Data from CSV Files
• Importing Data from SQL Databases
• Exploring the Structure of Data
• Exploring and Understanding Data
• Exploring Numeric Variables
• Understanding Types of Data
• Qualitative and Quantitative Analysis
• Studying Descriptive Statistics
• Exploring Numeric Variables
• Measuring the Central Tendency – The Model
• Measuring Spread – Variance and Standard Deviation
• Visualizing Numeric Variables – Boxplots and Histograms
• Understanding Numeric Data – Uniform and Normal Distributions
• Measuring the Central Tendency – The Mode
• Exploring Relationships between Variables
• Visualizing Relationships – Scatterplots
• Nominal and Ordinal Measurement
• Interval and Ratio Measurement
• Statistical Investigation
• Inferential Statistics
• Probability and Central Limit Theorem
• Exploratory Data Analysis
• Normal Distribution
• Distance Measures
• Euclidean & Manhattan Distance

• Minkowski & Mahalanobis
• Cosine
• Correlation
• PPMC (Pearson Product Moment Coorelation)
Machine Learning Techniques using R
Hypothesis Testing
• Implementing Machine Learning Algorithms on larger Data Sets
with Apache Mahout
• How do Machines Learn?
• Abstraction and Knowledge Representation
• Generalization
• Assessing the Success of Learning
• Steps to apply Machine Learning to your Data
• Choosing a Machine Learning Algorithm
• Thinking about the Input Data
• Thinking about Types of Machine Learning Algorithms
• Matching your Data to an Appropriate Algorithm
• Importance of Hypothesis Testing in Business
• Null and Alternate Hypothesis
• Understanding Types of Errors
• Contingency Table and Decision Making
• Confidence Coefficient
• Upper Tail Test and Test Statistics
• Understanding Parametric Tests
• Z-Test and Z-Test in R
• Chi-Square Test
• Degree of Freedom
• One-Way ANOVA Test
• F-Distribution, F-Ration Test

• Data Preparation for Modelling
• How do Machines Learn?
• Choosing a Machine Learning Algorithm

• Supervised Learning Techniques and Algorithms
• Understanding Process Flow of Supervised Learning Techniques
• K-NN, Naïve Bayes, Support Vector Machines
• Defining Classification
• Understanding Classification and Prediction
• Decision Tree Classifier
• How to Build Decision Trees?
• Basic Algorithm for a Decision Tree
• Decision Trees and Data Mining
• Random Forest Classifier
• Features of Random Forests
• Out of Box Error Estimate and Variable Importance
• Naïve Bayes Classifier Model
• Bayesian Theorem
• Advantages and Disadvantages of Naïve Bayes Classifier Model
• Understanding Support Vector Machines
• What is Geometric Margin SVMs?
• Understanding Linear SVMs
• Machine Learning Techniques Using R
• Machine Learning: Tasks, Features, Models, and Design
• Machine Learning Common Use Case
• Supervised and Unsupervised Learning Techniques
• Clustering
• Similarity Metrics
• Distance Measure Types: Euclidean
• Cosine Measures
• Creating predictive Models
• Classification using Nearest Neighbors
• Studying Clustering
• Clustering and Classification
• Understanding K-means Clustering
• K-means and Pseudo Code
• K-means Clustering using R

• TF-IDF and Cosine Similarity
• Application to Vector Space Model
• What is Hierarchical Clustering?
• Hierarchical Clustering Algorithm
• Understanding Agglomerative Clustering Process
• DBSCAN Clustering
• What is Association Rule Mining?
• Association Rule Strength Measures
• Checking Apriori Algorithms
• Ordering Items
• Understanding Candidate Generation
• Performing Visualization on Associated Rules

• Understanding Neural Networks
• From Biological to Artificial Neurons
• Activation Functions

• What is Regression?
• Model Selection
• Generalized Regression
• Simple Linear Regression
• Multiple Linear Regression
• Correlations
• Correlation between X and Y
• Ridge and Regularized Regression
• LASSO
• Time Series
• Prediction: Time Dependent/Variant Data
• Ordinary Least Square Regression Model
• Dummy Variable Regression Model
• Interaction Regression Model
• Non-Linear Regression Model
• Perform Regression Analysis with Multiple Variables