Affiliated to National Board of Vocational Training Education, Government of India.
Affiliated to National Board of Vocational Training Education, Government of India.
52%

Data Science with R Programming Course

33,900.00

Data Science with R Programing Course lets you master data manipulation with R Programming, data visualization, advanced analytics topics like regression and data mining using RStudio.

  • IBM® & IABAC® Certification
  • 6-Month | 400 Learning Hours
  • 120-Hour Live Online Training
  • 20 Capstone & 1 Client Project
  • 365 Days Access + Cloud Lab
  • Internship + Job Assistance
Category:

Data Science with R Programing Course lets you master data manipulation with R Programming, data visualization, advanced analytics topics like regression and data mining using RStudio.

Data Science with R Programming Course Module

The following topics are covered here

Module 1 – Introduction to Data Science with Python

  • Installing Python Anaconda distribution
  • Python native Data Types
  • Basic programming concepts
  • Python data science packages overview

Module 2 – Python Basics: Basic Syntax, Data Structures

  • Python Objects
  • Math & Comparision Operators
  • Conditional Statement
  • Loops
  • Lists, Tuples, Strings, Dictionaries, Sets
  • Functions
  • Exception Handling

Module 3 – Numppy Package

  • Importing Numpy
  • Numpy overview
  • Numpy Array creation and basic operations
  • Numpy Universal functions
  • Selecting and retrieving Data
  • Data Slicing
  • Iterating Numpy Data
  • Shape Manipulation
  • Stacking and Splitting Arrays
  • Copies and Views : no copy, shallow copy , deep copy
  • Indexing : Arrays of Indices, Boolean Arrays

Module 4 – Pandas Package

  • Importing Pandas
  • Pandas overview
  • Object Creation : Series Object , DataFrame Object
  • View Data
  • Selecting data by Label and Position
  • Data Slicing
  • Boolean Indexing
  • Setting Data

Module 5 – Python Advanced: Data Mugging with Pandas

  • Applying functions to data
  • Histogramming
  • String Methods
  • Merge Data : Concat, Join and Append
  • Grouping & Aggregation
  • Reshaping
  • Analysing Data for missing values
  • Filling missing values: fill with constant, forward filling, mean
  • Removing Duplicates
  • Transforming Data

Module 6 – Python Advanced: Visualization with MatPlotLib

  • Importing MatPlotLib & Seaborn Libraries
  • Creating a basic chart: Line Chart, Bar Charts and Pie Charts
  • Plotting from Pandas object
  • Saving a plot
  • Object-Oriented Plotting: Setting axes limits and ticks
  • Multiple Plots
  • Plot Formatting: Custom Lines, Markers, Labels, Annotations, Colors
  • Statistical Plots with Seaborn

Module 7 – Exploratory Data Analysis: Case Study

The following topics are covered here

Module 1: Introduction to Statistics

  • Two areas of Statistics in Data Science
  • Applied statistics in business
  • Descriptive Statistics
  • Inferential Statistics
  • Statistics Terms and definitions
  • Type of Data
  • Quantitative vs Qualitative Data
  • Data Measurement Scales

Module 2: Harnessing Data

  • Sampling Data, with and without replacement
  • Sampling Methods, Random vs Non-Random
  • Measurement on Samples
  • Random Sampling methods
  • Simple random, Stratified, Cluster, Systematic sampling.
  • Biased vs unbiased sampling
  • Sampling Error
  • Data Collection methods

Module 3: Exploratory Analysis

  • Measures of Central Tendencies
  • Mean, Median and Mode
  • Data Variability : Range, Quartiles, Standard Deviation
  • Calculating Standard Deviation
  • Z-Score/Standard Score
  • Empirical Rule
  • Calculating Percentiles
  • Outliers

Module 4: Distributions

  • Distribtuions Introduction
  • Normal Distribution
  • Central Limit Theorem
  • Histogram – Normalization
  • Other Distributions: Poisson, Binomial et.,
  • Normality Testing
  • Skewness
  • Kurtosis
  • Measure of Distance
  • Euclidean , Manhattan and Minkowski Distance

Module 5: Hypothesis & computational Techniques

  • Hypothesis Testing
  • Null Hypothesis, P-Value
  • Need for Hypothesis Testing in Business
  • Two-tailed, Left tailed & Right tailed test
  • Hypothesis Testing Outcomes: Type I & II errors
  • Parametric vs Non-Parametric Testing
  • Parametric Tests, T-Tests: One sample, two-sample, Paired
  • One Way ANOVA
  • Importance of Parametric Tests
  • Non-Parametric Tests: Chi-Square, Mann-Whitney, Kruskal-Wallis, etc.,
  • Which Test to Choose?
  • Asserting accuracy of Data

Module 6: Correlation & Regression

  • Introduction to Regression
  • Type of Regression
  • Hands-on of Regression with R and Python.
  • Correlation
  • Weak and Strong Correlation
  • Finding Correlation with R and Python

The following topics are covered here

Module 1: Machine Learning Introduction

  • What is Machine Learning
  • Applications of Machine Learning
  • Machine Learning vs Artificial Intelligence
  • Machine Learning Languages and platforms
  • Machine Learning vs Statistical Modelling

Module 2: Machine Learning Algorithms

  • Popular Machine Learning Algorithms
  • Clustering, Classification and Regression
  • Supervised vs Unsupervised Learning
  • Application of Supervised Learning Algorithms
  • Application of Unsupervised Learning Algorithms
  • Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
  • How to choose Machine Learning Algorithm?

Module 3: Supervised Learning

  • Simple Linear Regression: Theory, Implementing in Python (and R), Working on the use case.
  • Multiple Linear Regression: Theory, Implementing in Python (and R), Working on the use case.
  • K-Nearest Neighbors: Theory, Implementing in Python (and R), KNN advantages, Working on the use case.
  • Decision Trees: Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on the use case.

Module 4: Unsupervised Learning

  • K-Means Clustering: Theory, Euclidean Distance method.
  • K-Means hands-on with Python (and R)
  • K-Means Advantages & Disadvantages

The following topics are covered here

Module 1: Advanced Machine Learning Concepts

  • Tuning with Hyperparameters.
  • Popular ML algorithms,
  • Clustering, classification, and regression,
  • Supervised vs unsupervised.
  • Choice of ML algorithm
  • Grid Search vs Random search cross-validation

Module 2: Principle Component Analysis (PCA)

  • Key concepts of dimensionality reduction
  • PCA theory
  • Hands-on coding.
  • case study on PCA

Module 3: Random Forest – Ensemble

  • Key concepts of Randon Forest
  • Hands-on coding.
  • Pros and cons.
  • case study on Random Forest

Module 4: Support Vector Machine (SVM)

  • Key concepts of Support Vector Machine.
  • Hands-on coding.
  • Pros and Cons.
  • case study on SVM

Module 5: Natural Language Processing (NLP)

  • Key concepts of NLP.
  • Hands-on coding.
  • Pros and Cons.
  • Text Processing with Vectorization
  • Sentiment analysis with TextBlob
  • Twitter sentiment analysis

Module 6: Naïve Bayes Classifier

  • Key concepts of Naive Bayes.
  • Hands-on coding.
  • Pros and Cons
  • Naïve Bayes for text classification
  • New articles tagging

Module 7: Artificial Neural Network (ANN)

  • Basic ANN network for Regression and Classification
  • Hands-on coding.
  • Pros and Cons
  • Case study on ANN, MLP

Module 8: Tensorflow overview and Deep Learning Intro

  • Tensorflow workflow demo
  • Introduction to deep learning.

Module 1: Tableau Introduction

  • Tableau Interface
  • Dimensions and measures
  • Filter shelf
  • Distributing and publishing

Module 2: Connecting to Data Source

  • Connecting to sources, Excel, Databases, API, Pdf
  • Extracting and interpreting data.

Module 3: Visual Analytics

  • Charts and plots with Super Store data

Module 4: Forecasting

  • Forecasting time series data

Module 1: Understanding Business Case

  • Components of Business Case.
  • ROI calculation techniques.
  • Scoping

Module 2: Writing Data Science Business Case

  • Defining Business opportunity.
  • Translating to Data Science problem.
  • Creating a project plan

Module 3: Benefits Analysis

  • Demonstrating break-even and benefits analysis with Data Science Solutions.
  • IRR benefits analysis
  • Discounted Cash Flow

Module 4: Starting project, Setting up Team, and closing

  • Initiating Project
  • Setting up the Team
  • Controlling project delivery
  • Closing project.

Introduction to Data Science

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is Artificial Intelligence?
  • Data Analytics and its types

Introduction to R

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R Studio Review

R Packages

  • Data Types
  • Variable Vectors
  • Lists
  • Environment Setup
  • Array
  • Matrix
  • Data Frames
  • Factors
  • Loops
  • Functions
  • Packages
  • In-Built Datasets
  • R Basics
  • Importing data
  • Manipulating data
  • Statistics Basics
  • Error metrics
  • Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Machine Learning using R

Introduction to Deep Learning

  • What is a neural network?
  • Supervised Learning with Neural Networks – Python
  • How Deep Learning is different from Machine Learning

Overview of Machine Learning Concepts

  • What is Machine Learning?
  • Supervised Machine Learning algorithms
  • K-Nearest Neighbors (KNN) concept and application
  • Naive Bayes concept and application
  • Logistic Regression concept and application
  • Classification Trees concept and application
  • Unsupervised Machine Learning algorithms
  • Clustering with K-means concept and application
  • Hierarchical Clustering concept and application

TensorFlow Essentials

  • Representing tensors
  • Creating operators and executing with sessions
  • Introduction Jupyter notebook for TensorFlow coding
  • TensorFlow variables
  • Visualizing data using TensorBoard

ML Algorithm – Linear Regression in TensorFlow

  • Regression problems
  • Linear regression applications
  • Regularization
  • Available datasets
  • Coding Linear Regression with TensorFlow – Case study

Deep Neural Networks in TensorFlow

  • Basic Neural Nets
  • Single Hidden Layer Model
  • Multiple Hidden Layer Model

Convolutional Neural Networks

  • Introduction to Convolutional Neural Networks
  • Input Pipeline
  • Introduction to RNN, LSTM, GRU

Reinforcement Learning in Tensorflow

  • Concept of Reinforcement Learning
  • Simple model applying Reinforcement Learning in TensorFlow

Hands-on Deep Learning Application with TensorFlow

  • Example Application – Case study
  • Hands-on building the Deep Learning application with TensorFlow

Introduction to TensorFlow

  • Installing TensorFlow using Docker
  • Installing Matplotlib
  • Hello World application with TensorFlow

Basic Statistics

  • Basic Statistics and Exploratory Analysis
  • Descriptive summary statistics with Numpy
  • Summarize continuous and categorical data
  • Outlier analysis

Machine Learning Introduction

  • Machine learning essentials
  • Data representation and features
  • Distance metrics
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Theano, Caffe, Torch, CGT, and TensorFlow

TensorFlow Essentials

  • Representing tensors
  • Creating operators and executing with sessions
  • Introduction Jupyter notebook for TensorFlow coding
  • TensorFlow variables
  • Visualizing data using TensorBoard

ML Algorithm – Linear Regression in TensorFlow

  • Regression problems
  • Linear regression applications
  • Regularization
  • Available datasets
  • Coding Linear Regression with TensorFlow – Case study

ML Algorithm – Classification in TensorFlow

  • Classification problems
  • Using linear regression for classification
  • Using logistic regression (including multi-dimensional input)
  • Multiclass classifiers (such as softmax regression)
  • Hands-on Classification with TensorFlow

ML Algorithm – Clustering in TensorFlow

  • Traversing files in TensorFlow
  • K-means clustering
  • Clustering using a self-organizing map

Simple Neural Networks in TensorFlow

  • Introduction to Neural Networks
  • Batch training
  • Variational, denoising, and stacked autoencoders

Reinforcement learning

  • Concept of Reinforcement Learning
  • Simple model applying Reinforcement Learning in TensorFlow

Convolutional and Recurrent Neural Networks

  • Advantages and disadvantages of neural networks
  • Convolutional neural networks
  • The idea of contextual information
  • Recurrent neural networks
  • Real-world predictive model – example

Case study – Stock Market Analysis with TensorFlow

  • Case study – Stock Market Analysis
  • Hands-on Coding in TensorFlow

Why Data Science with R?

  • R language is one of the top languages that most of the companies are demanding.
  • Learning Data Science with R opens new career opportunities for both beginners and professionals.
  • It helps you achieve top programming jobs with the help of lab sessions, assignments and project work.
  • Also, this training program helps you prepare for the R Certification exam.

Data Science with R - Course Objectives

After successful completion of the course “Data Science with R”, you should have,

  • Gained a better knowledge of the workflow of data science with R language
  • Understood the key concepts of data science
  • Gained hands-on knowledge of R language
  • In-depth knowledge of data manipulation, data visualization, advanced analytics and data mining.

Advantages of Data Science with R course

  • High demand for data scientists as a very less number of eligible candidates are available to be hired
  • High salaries as per the demand for data scientists as supply is low

Who should choose Data Science with R course?

  • Software Engineers and Data Analysts
  • SAS developers who aspire to learn the open-source technology
  • Business Intelligence professionals
  • Candidates who want to start a career in Data Science

Reviews

There are no reviews yet.

Be the first to review “Data Science with R Programming Course”

Your email address will not be published. Required fields are marked *

Open chat