DATA SCIENCE WITH R

Data Science with R Introduction to R Programming

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Learn Data Science Course in Mumbai at Wings Academy – Rated as No 1 Data Science Training institute in Mumbai by leading Data Scientists from the industry. The Data Science Course at Wings Academy is the right track for any aspirant to become an expert in the field of Data Science. Designed by our faculty data scientists with immense experience in the industry, the course combines sound theory coupled with real-time projects in the subject.

  • What is R?
  • History and Features of R
  • Introduction to R Studio
  • Installing R and Environment Setup
  • Command Prompt 
  • Understanding R programming Syntax
  • Understanding R Script Files

R Programming Basics

  • Data types in R
  • Creating and Managing Variables
  • Understanding Operators
    • Assignment Operators
    • Arithmetic Operators
    • Relational and Logical Operators
    • Other Operators
  • Understanding and using Decision Making Statements
    • The IF Statement
    • The IF…ELSE statement
    • Switch Statement
  • Understanding Loops and Loop Control 
    • Repeat Loop
    • While Loop 
    • For Loop
    • Controlling Loops with Break and Next Statements

More on Data Types

  • Understanding the Vector Data type
    • Introduction to Vector Data type
    • Types of Vectors
    • Creating Vectors and Vectors with Multiple Elements
    • Accessing Vector Elements
  • Understanding Arrays in R
    • Introduction to Arrays in R
    • Creating Arrays
    • Naming the Array Rows and Columns
    • Accessing and manipulating Array Elements
  • Understanding the Matrices in R
    • Introduction to Matrices in R
    • Creating Matrices
    • Accessing Elements of Matrices
    • Performing various computations using Matrices
  • Understanding the List in R
    • Understanding and Creating List 
    • Naming the Elements of a List
    • Accessing the List Elements
    • Merging different Lists
    • Manipulating the List Elements
    • Converting Lists to Vectors
  • Understanding and Working with Factors
    • Creating Factors
    • Data frame and Factors
    • Generating Factor Levels
    • Changing the Order of Levels
  • Understanding Data Frames
    • Creating Data Frames
    • Matrix Vs Data Frames
    • Sub setting data from a Data Frame
    • Manipulating Data from a Data Frame
    • Joining Columns and Rows in a Data Frame
    • Merging Data Frames
  • Converting Data Types using Various Functions
  • Checking the Data Type using Various Functions

Functions in R

  • Understanding Functions in R
  • Definition of a Function and its Components
  • Understanding Built in Functions
    • Character/String Functions
    • Numerical and Statistical Functions
    • Date and Time Functions
  • Understanding User Defined Functions (UDF)
    • Creating a User Defined Function
    • Calling a Function
    • Understanding Lazy Evaluation of Functions

Working with External Data

  • Understanding External Data
  • Understanding R Data Interfaces
  • Working with Text Files
  • Working with CSV Files
  • Understanding Verify and Load for Excel Files
  • Using WriteBin() and ReadBin() to manipulate Binary Files 
  • Understanding the RMySQL Package to Connect and Manage MySQL Databases

Data Visualization with R

  • What is Data Visualization
  • Understanding R Libraries for Charts and Graphs 
  • Using Charts and Graphs for Data Visualizations
  • Exploring Various Chart and Graph Types
    • Pie Charts and Bar Charts
    • Box Plots and Scatter Plots
    • Histograms and Line Graphs

Exploring Statistical Computations using R

  • Understanding the Basics of Statistical Analysis
  • Uses and Advantages of Statistical Analysis
  • Understanding and using Mean, Median and Mode
  • Understanding and using Linear, Multiple and Logical Regressions
  • Generating Normal and Binomial Distributions
  • Understanding Inferential Statistics
  • Understanding Descriptive Statistics and Measure of Central Tendency

Packages in R

  • Understanding Packages
  • Installing and Loading Packages
  • Managing Packages

Understanding Machine Learning Models

  • Understand what is a Machine Learning Model
  • Various Machine Learning Models
  • Choosing the Right Model
  • Training and Evaluating the Model
  • Improving the Performance of the Model

More on Models

  • Understanding Predictive Model
  • Working with Linear Regression
  • Working with Polynomial Regression
  • Understanding Multi Level Models
  • Selecting the Right Model or Model Selection
  • Need for selecting the Right Model
  • Understanding Algorithm Boosting
  • Various Types of Algorithm Boosting
  • Understanding Adaptive Boosting

Understanding Machine Learning Algorithms

  • Understanding the Machine Learning Algorithms
  • Importance of Algorithms in Machine Learning
  • Exploring different types of Machine Learning Algorithms
    • Supervised Learning 
    • Unsupervised Learning
    • Reinforcement Learning

Exploring Supervised Learning Algorithms

  • Understanding the Supervised Learning Algorithm
  • Understanding Classifications
  • Working with different types of Classifications
  • Learning and Implementing Classifications
    • Logistic Regression
    • Naïve Bayes Classifier
    • Nearest Neighbor
    • Support Vector Machines (SVM)
    • Decision Trees
    • Boosted Trees
    • Random Forest
  • Time Series Analysis (TSA)
    • Understanding Time Series Analysis
    • Advantages of using TSA
    • Understanding various components of TSA
    • AR and MA Models
    • Understanding Stationarity
    • Implementing Forecasting using TSA

Exploring Un-Supervised Learning Algorithms

  • Understanding Unsupervised Learning
  • Understanding Clustering and its uses
  • Exploring K-means 
    • What is K-means Clustering
    • How K-means Clustering Algorithm Works
    • Implementing K-means Clustering
  • Exploring Hierarchical Clustering
    • Understanding Hierarchical Clustering
    • Implementing Hierarchical Clustering
  • Understanding Dimensionality Reduction
    • Importance of Dimensions
    • Purpose and advantages of Dimensionality Reduction
    • Understanding Principal Component Analysis (PCA)
    • Understanding Linear Discriminant Analysis (LDA)

Understanding Hypothesis Testing

  • What is Hypothesis Testing in Machine Learning
  • Advantages of using Hypothesis Testing 
  • Basics of Hypothesis
    • Normalization
    • Standard Normalization
  • Parameters of Hypothesis Testing
    • Null Hypothesis
    • Alternative Hypothesis
  • The P-Value
  • Types of Tests
    • T Test
    • Z Test
    • ANOVA Test
    • Chi-Square Test

Overview Reinforcement Learning Algorithm

  • Understanding Reinforcement Learning Algorithm
  • Advantages of Reinforcement Learning Algorithm
  • Components of Reinforcement Learning Algorithm
  • Exploration Vs Exploitation tradeoff

Trainer Profile

  • Wings Academy trainers are the experts who have 8+ years of experience in the Data Science field.
  • Trainers are experienced on various real time projects.
  • They are working professionals in the MNC companies.
  • We have certified professionals with strong practical and theoretical knowledge.
  • Trainers provide hands-on training and make the students work on real-time projects to get industry exposure.
  • Trainers train the students with the recent algorithms and tools that are used in data science.
  • Trainers provide necessary individual attention and helps the students according to their academic needs.
  • In Wings Academy, trainers guide the students with necessary interview tips & supports in resume building
  • Tutors guide the students to enhance their technical skills in Data Science.