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    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 and R. Enroll & Get Certified now!

    • ✔ Course Duration : 32 hrs
    • ✔ Training Options : Live Online / Self-Paced / Classroom
    • ✔ Certification Pass : Guaranteed

    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 and R.

    What you will Learn

    • 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.

    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.

    CURRICULUM

    Learning Objectives:
    • 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?
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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)
    Learning Objectives:
    • 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)
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    Learning Objectives:
    • Need of Data preparation
    • Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
    • Variable Reduction Techniques – Factor & PCA Analysis
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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 )
    Learning Objectives:
    • 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)
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • 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
    Learning Objectives:
    • Concept of Conditional Probability
    • Bayes Theorem and Its Applications
    • Naïve Bayes for classification
    • Applications of Naïve Bayes in Classifications
    Learning Objectives:
    • 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.
    Learning Objectives:

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

    FAQs

    You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
    1. Online ,By deposit the mildain bank account
    2. Pay by cash team training center location

    Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

    Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

    You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

    We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

    Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

    Just give us a CALL at +91 8447121833 OR email at info@mildaintrainings.com

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  • Real-life Case Studies
    Live project based on any of the selected use cases, involving implementation of the various Course concepts.
  • Assignments
    Each class will be followed by practical assignments.
  • Lifetime Access
    You get lifetime access to presentations, quizzes, installation guide & class recordings.
  • 24 x 7 Expert Support
    We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.
  • Certification
    Sucessfully complete your final course project and Mildaintrainings will give you Course completion certificate.
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