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DATA SCIENCE USING R TRAINING

Become an expert in Data Analytics / Data Science using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R Cloud Lab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.

4 Days / 32 Hrs

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Data Science Using R Training

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Data Science Using R and R Certification

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Delivery Options: Attend remote-live or on-demand online classes.

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DESCRIPTION

DESCRIPTION

Data Science using R training

With this comprehensive R training learn hand-on skills on Data Science with R – the rediscovered language for Data Science! Over past several years R has garnered immense popularity among Data Science practitioners and it is no surprise that R language is often as referred as lingua franca of Data Science! This Data Science R course effectively covers basic data analytics, statistical predictive modelling and machine learning through various practical examples and projects.

Best R training, for candidates who do not have programming background but want to acquire job oriented practical skills on a prominent open source Data Science platform. This Data Science R course is also available through live online and self-paced video based mode as well. 

You may also check for amazing value combo course Data Science Specialization to learn Data Science using Python & R.

Enroll Nowand know applications of using Data Science using R and solving real-life challenges related to: • Computer Vision • Speech • Natural Language Processing

CURRICULUM

CURRICULUM

Introduction to data science with R
  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • How leading companies are harnessing the power of analytics?
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem solving framework
  • List of steps in Analytics projects
  • Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project
  • Why R for data science?
Introduction- Data Importing/Exporting
  • Introduction R/R-Studio – GUI
  • Concept of Packages – Useful Packages (Base & Other packages)
  • Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
  • Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
  • Database Input (Connecting to database)
  • Exporting Data to various formats)
  • Viewing Data (Viewing partial data and full data)
  • Variable & Value Labels –  Date Values
Data Manpiulation
  • Data Manipulation steps
  • Creating New Variables (calculations & Binning)
  • Dummy variable creation
  • Applying transformations
  • Handling duplicates
  • Handling missings
  • Sorting and Filtering
  • Subsetting (Rows/Columns)
  • Appending (Row appending/column appending)
  • Merging/Joining (Left, right, inner, full, outer etc)
  • Data type conversions
  • Renaming
  • Formatting
  • Reshaping data
  • Sampling
  • Data manipulation tools
  • Operators
  • Functions
  • Packages
  • Control Structures (if, if else)
  • Loops (Conditional, iterative loops, apply functions)
  • Arrays
  • R Built-in Functions (Text, Numeric, Date, utility)
  • Numerical Functions
  • Text Functions
  • Date Functions
  • Utilities Functions
  • R User Defined Functions
  • R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)
Data Analysis Visualization
  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
  • R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
  • R Packages for Graphical Analysis (base, ggplot, lattice,etc)
Introduction to Statistics
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
  • Inferential Statistics -Sampling – Concept of Hypothesis Testing
  • Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
Introduction to Predictive Modeling
  • Concept of model in analytics and how it is used?
  • Common terminology used in analytics & modeling process
  • Popular modeling algorithms
  • Types of Business problems – Mapping of Techniques
  • Different Phases of Predictive Modeling
Data Exploration for Modeling
Data Prepration
  • Need of Data preparation
  • Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
  • Variable Reduction Techniques – Factor & PCA Analysis
Segmentation: Problems on Segmentation
  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling – Identify cluster characteristics
  • Interpretation of results – Implementation on new data
Linear Regression: Problems on Regression
  • Introduction – Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Assess the overall effectiveness of the model
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results – Business Validation – Implementation on new data
  • Introduction – Applications
Logistic Regression: Classification Problems
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model (Binary Logistic Model)
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  • Interpretation of Results – Business Validation – Implementation on new data
Time Series Forecasting
  • Introduction – Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based – Pattern less)
  • Basic Techniques – Averages, Smoothening, etc
  • Advanced Techniques – AR Models, ARIMA, etc
  • Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
Machine Learning & Predictive Modeling Basics
  • Introduction to Machine Learning & Predictive Modeling
  • Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
  • Overfitting (Bias-Variance Trade off) & Performance Metrics
  • Feature engineering & dimension reduction
  • Concept of optimization & cost function
  • Overview of gradient descent algorithm
  • Overview of Cross validation(Bootstrapping, K-Fold validation etc)
  • Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
Unsupervised Learning: Segmentation
  • What is segmentation & Role of ML in Segmentation?
  • Concept of Distance and related math background
  • K-Means Clustering
  • Expectation Maximization
  • Hierarchical Clustering
  • Spectral Clustering (DBSCAN)
  • Principle component Analysis (PCA)
Supervised Learning: Decision Tree
  • Decision Trees – Introduction – Applications
  • Types of Decision Tree Algorithms
  • Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
  • Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
  • Decision Trees – Validation
  • Overfitting – Best Practices to avoid
Supervised Learning: Ensemble Learning
  • Concept of Ensembling
  • Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling (Stacking, Mixture of Experts)
  • Bagging (Logic, Practical Applications)
  • Random forest (Logic, Practical Applications)
  • Boosting (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost
Supervised Learning: Artificial Neural Network
  • Motivation for Neural Networks and Its Applications
  • Perceptron and Single Layer Neural Network, and Hand Calculations
  • Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
  • Neural Networks for Regression
  • Neural Networks for Classification
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating ANN models
Supervised Learning: Support Vector Machines
  • Motivation for Support Vector Machine & Applications
  • Support Vector Regression
  • Support vector classifier (Linear & Non-Linear)
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating SVM models
Supervised Learning: KNN
  • What is KNN & Applications?
  • KNN for missing treatment
  • KNN For solving regression problems
  • KNN for solving classification problems
  • Validating KNN model
  • Model fine tuning with hyper parameters
Supervised Learning: Naïve Bayes
  • Concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naïve Bayes for classification
  • Applications of Naïve Bayes in Classifications
Text Mining and Analysis
  • Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
  • Finding patterns in text: text mining, text as a graph
  • Natural Language processing (NLP)
  • Text Analytics – Sentiment Analysis using R
  • Text Analytics – Word cloud analysis using R
  • Text Analytics – Segmentation using K-Means/Hierarchical Clustering
  • Text Analytics – Classification (Spam/Not spam)
  • Applications of Social Media Analytics
  • Metrics(Measures Actions) in social media analytics
  • Examples & Actionable Insights using Social Media Analytics
  • Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
  • Fine tuning the models using Hyper parameters, grid search, piping etc.
Wrap up

Applying different algorithms to solve the business problems and benchmark the results

FAQ | Data Science Using R

Why should I take Data Science Using R Training from Mildaintrainings?

You should go for Data Science Using R from Mildaintrainings as our trainers have 12 plus years of industry practical experience and we also provide Practical training with the live project so that you could understand each and everything better, it will help you in your job. At Mildaintraining we will provide you six months Technical support as well.

Do I get the Data Science using R and R training certificate?

Yes, at Mildaintrainings we will provide you participation certificate after the completion of Data Science using R course from Mildaintrainings.

When will the classes be held for Data Science Using R?

Classes will be held on weekends as well as weekdays as per schedule or your convenience.

What if I miss the Data Science Using R class?

If you miss the class in that case backup class can be adjusted in next live session.

What is Data Science Using R course duration?

This course duration will be of 32 – 40 hours or 4 days and it will be Instructor lead training at Mildaintrainings with Practical training with live project. The timing will be according to your convenience it can be on weekend and weekdays.

What are the course objectives?

The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.

  • Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
  • Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
  • As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure you get practical, hands-on experience with your new skills. Four additional projects are also available for further practice.
What skills will you learn?

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:

  • IT professionals looking for a career switch into data science and analytics
  • Software developers looking for a career switch into data science and analytics
  • Professionals working in data and business analytics
  • Graduates looking to build a career in analytics and data science
  • Anyone with a genuine interest in the data science field
  • Experienced professionals who would like to harness data science in their fields
  • Prerequisites: There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.

Reviews

Rekha Amminbhavi

Testing Engineer at CSC

Guidewire The program was really knowledgeable
and the modules were just perfectly made and managed
and were taught with ease.
I am so happy I choose mildain

Jyotish Phukon

Senior EDI Analyst at SIQES

IBM Sterling Integrator Good training. Explaining the things with practical examples. Well experienced and confident enough to answer every query. Trainers are working more as a friend rather than working like for money. Worth of paying for the course.

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