Data Science with R Certification Training in San Jose | Data Science Online Course

Data Science using R Training in San Jose CA US

Data Science with R is the significant methodology used for complex data visualization, statistical study with large datasets, and graphics competencies.
Data Science is a scientific methodology that assists in learning various data patterns provided by users. The scientific methodology uses technologies such as R and Python for comprehending the data. Enroll & Get Certified now!

  • 32 Hours Instructor­ led Online Training
  • Authorized Digital Learning Materials
  • Lifetime Free Content Access
  • Flexible Schedule Learn Anytime, Anywhere.
  • Training Completion Certificate
  • 24x7 After Course Support

Program Calendar

  • Available Dates
    Live Virtual Training
    • cal.png29 June, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png06 July, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png13 July, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
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Course Overview

You can go ahead with data-driven decisions using R.

Majorly, data science is an architecture that applies scientific process, algorithm, framework, and extract the insight knowledge base. The data scientists read the data provided in structured or unstructured format. After analyzing the data, the scientists provide incredible solutions to the enterprises so that business could take précised data-driven decisions.

Learning Objectives

  • 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.

Supplementary with the raw values of abundant packages, these packages are presenting advanced graphical competencies. The technology R is applied in customizing graphs in the programming language.

Why is R with Data Science preferred?

Students enrolling in data science with R training in San Jose CA US will acknowledge that R is a chronological, swift, and open-source programming language. This is not tricky to use. One of the chief motivating parts of applying code in data analysis is that it supports collaborations (collabs). Participants in course training learn to reproduce it. It includes submissions, counts, or identifies mistakes in scrutiny.

How data science with R certification is marking its way.

Completing the course in data-science enables participants to find quick jobs, a better data community, and a high salary. R offers faster performance, has enhanced features, comprising better key features compared to other big data solutions, unit testing and web frameworks.

The industry is looking for candidates who can analyze data interacting with R tools. This includes store data.

How R is assisting industries.

Data science being an existing field in analyzing data, R in data science allows data scientists to interact with R tools to analyze and store data.

Features of R in Data Science:

  • Manipulation
  • Visualization
  • Exploration
  • Modeling
  • Collection

Data Manipulation

The concept behind any data analysis is developed considering data manipulation. This involves more advanced data analysis. One of R's values is that it holds inclusive code repositories and libraries such as CRAN (Comprehensive R Archive Network). Aspirants going through the data science course in San Jose CA US find a huge repository with R functions, codes, and data. Apart from it, data science with R also has libraries for data redesigning and analyzing results with more accuracy.

Data Visualization

The idea of R is to do arithmetical analysis and give learning results. It presents higher graphical competencies. Data Science with R is a huge option of a data visualization package for the arithmetic programming language. Along with R, participants can also opt for data science with python certification. It assists scientists applied to customize and displaying graphs. This provides a comfort level to data scientists to plot graphs and create advanced complex scatter plots. This comes with regression and basic chart matrices.

Data Exploration

R was built to do statistical analysis and arithmetical analysis of large data. This delivers a higher form of a prospect distribution, enabling a range of statistical tests to data. It also allows the use of standard machine knowledge, and data mining methods. The data exploration owns the basic random number generation, analytics optimization, statistical processing, signal processing, and machine learning as prior functionality. Mildain Solutions involves thorough learning in contrast to relying on heavy libraries.

Data Modeling

Precise data modeling analysis relies greatly on other packages past R's core functionality. The packages comprise chance laws allocation for efficient modeling.

R is an open-source stage that one can employ without any hassle. One language that everybody talks about, but when coming to operational with data, it is one of the best languages to labor with; hence, it is used worldwide.

Data Collection

R with data science supports importing data from Excel, CSV, SPSS, and text files. As mentioned earlier, data science with python is another open-source and free programming language going along with R. However, python is considered the most efficient language for collecting data. Python provides the best framework for websites.

Enrolling in data science with R certification in San Jose CA US will enable you to learn all aspects of data mining comprehensively. Enroll today!


  • Inspire and encourage the right decision making
  • Increases the decision making capabilities of an organization
  • Reduces the chances of errors that could damage the business
  • Assist focusing on the areas to improve relationship with customers
  • Data Science is the future, which has already became a requirement


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.

Course Curriculum

  • Topic Covered:

    • 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?
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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)
  • Topic Covered:

    • 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)
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • Need of Data preparation
    • Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
    • Variable Reduction Techniques – Factor & PCA Analysis
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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 )
  • Topic Covered:

    • 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)
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • 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
  • Topic Covered:

    • Concept of Conditional Probability
    • Bayes Theorem and Its Applications
    • Naïve Bayes for classification
    • Applications of Naïve Bayes in Classifications
  • Topic Covered:

    • 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.
  • Topic Covered:

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

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