Data Science R

Course Information

  • Weekdays Course (Monday-Thursday) Duration: 4 Days
  • Weekend Course
    (
    Sat & Sun)
    Duration: 2 Weeks
  • Study Modes: (Classroom/Virtual/OnSite)
  • What is included:
    instructor-led hands training,
    passing guarantee or free retraining, Lab access during the course, Course Material
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From £1300

Interest Free Available

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Course Insight

Data Science is an arena of reconstructing unstructured data to a structured model and confer it into knowledge. There are several tools and programming languages that are used in this process, and R is one such effective and required programming language for this purpose. Data Science R Training is one such training program that will help you to learn R Programming for Data Science from basic to advance level.

This training program will let you learn how to use variables, matrix, functions and other models of R programming required for Data Science. At advance level, you learn to use R programming language in collaboration of Machine learning algorithm for Data Science.

Prerequisites
  • This course has no specific prerequisites.
  • Fundamental knowledge in any high-level programming language (preferably R) is ideal but not required.
  • Basic knowledge of statistics would be considered as an added advantage.
  • Basic knowledge of computer hardware and software is ideal but not required.
What will you gain after this course
  • Through this course, you can stay on top of others in the talent race.
  • You will be recognized as a professional Data Scientist.
  • You will be recognized as an R Programmer, as well.
  •  With the help of this course, you will be recognized as a Business intelligence (BI) expert.
JOBS YOU CAN GET WITH A DATA SCIENCE R CERTIFICATION
  • Senior System Specialist
  • Manager BI (Business intelligence)
  • Principal Data Scientist
  • Senior Software Engineer
  • Statistician
Corporate Group Training
  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

Dates & prices

This is an On-Demand course. Please call us on 02085347556 to arrange the training as per your requirement.

Course Contents

  • Introduction to Data Science
  • Importance of Data Science in today’s data-driven world
  • Applications of Data Science, Data Science lifecycle
  • What is Machine Learning, Big Data Hadoop, and Deep Learning
  • Introduction to R programming and RStudio
  • Data exploration introduction
  • Data to/from external sources import and export
  • What are data exploratory analysis and data importing?
  • DataFrames, individual elements, vectors, factors, operators, in-built functions
  • Conditional and looping statements, user-defined functions, and data types
  • Data manipulation
  • dplyr package introduction
  • select(), filter(), mutate() functions, sampling, and counting
  • SQL-like operations with sqldf with the pipe operator and implementing
  • Introduction to visualization
  • Types of graphs, the ggplot2 package
  • geom_bar(), geom_hist(), geom_freqpoly(), geom_pont()
  • geom_boxplot multivariate analysis
  • barplot, a histogram and a density plot, and multivariate analysis
  • bar plots for categorical variables with geom_bar(), and theme() plotly visualization, geom_freqpoly() frequency plots, multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
  • Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
  • Geographic visualization with ggmap() and building web applications with shinyR
  • Introduction to Data Science
  • Importance of Data Science in today’s data-driven world
  • Applications of Data Science, Data Science lifecycle
  • What is Machine Learning, Big Data Hadoop, and Deep Learning
  • Introduction to R programming and RStudio
  • Data exploration introduction
  • Data to/from external sources import and export
  • What are data exploratory analysis and data importing?
  • DataFrames, individual elements, vectors, factors, operators, in-built functions
  • Conditional and looping statements, user-defined functions, and data types
  • Data manipulation
  • dplyr package introduction
  • select(), filter(), mutate() functions, sampling, and counting
  • SQL-like operations with sqldf with the pipe operator and implementing
  • Introduction to visualization
  • Types of graphs, the ggplot2 package
  • geom_bar(), geom_hist(), geom_freqpoly(), geom_pont()
  • geom_boxplot multivariate analysis
  • barplot, a histogram and a density plot, and multivariate analysis
  • bar plots for categorical variables with geom_bar(), and theme() plotly visualization, geom_freqpoly() frequency plots, multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
  • Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
  • Geographic visualization with ggmap() and building web applications with shinyR
  • Why do we need statistics?
  • Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
  • Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution
  • Introduction to Machine Learning
  • Introduction to linear regression, predictive modelling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
  • Predicting results and finding the p-value and an introduction to logistic regression
  • Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
  • Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
  • Understanding the summary results with a null hypothesis, F-statistic, and building linear models with multiple independent variables
  • Introduction to logistic regression
  • Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
  • Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
  • Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false-positive rate, and threshold evaluation with ROCR
  • Finding out the right threshold by building the ROC plot, cross-validation, multivariate logistic regression, and building logistic models with multiple independent variables
  • Real-life applications of logistic regression
  • What is the classification? Different classification techniques
  • Introduction to decision trees
  • Algorithm for decision tree induction and building a decision tree in R
  • Confusion matrix and regression trees vs classification trees
  • Introduction to bagging
  • Random forest and implementing it in R
  • What is Naive Bayes? Computing probabilities
  • Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
  • What is Clustering? Its use cases
  • what is k-means clustering? What is canopy clustering?
  • What is hierarchical clustering?
  • Introduction to unsupervised learning
  • Feature extraction, clustering algorithms, and the k-means clustering algorithm
  • Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means and finding out the right number of clusters using a scree plot it in R
  • Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
  • Introduction to association rule mining and MBA
  • Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
  • Introduction to recommendation engines
  • User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
  • Recommendation engine use cases
  • Introduction to Support Vector Machine (SVM)
  • Data classification using SVM
  • SVM algorithms using separable and inseparable cases
  • Linear SVM for identifying margin hyperplane
  • What is the Bayes theorem?
  • What is Naïve Bayes Classifier?
  • Classification Workflow
  • How Naive Bayes classifier works and classifier building in Scikit-Learn
  • Building a probabilistic classification model using Naïve Bayes and the zero probability problem
  • Introduction to the concepts of text mining
  • Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
  • Text mining algorithms and the quantification of the text
  • TF-IDF and after TF-IDF

Training Offers & Packages

Cloud Infrastructure

£ 2250 Save: £400
  • Microsoft Azure Fundamental AZ-900
  • Microsoft Azure Administrator AZ-104
  • Office 365 Fundamentals MS-900

Cloud Specialist

£ 3200 Save: £500
  • Microsoft Azure Fundamental AZ-900
  • Microsoft Azure Administrator AZ-104
  • MCEAE Office-365

3rd Line IT Support Engineer

£ 3200 Save: £250
  • MCMDAA Windows-10
  • MCEAE Office 365
  • CCNA (Routing & Switching)