Professional certification in Data Science

DATA SCIENCE R TRAINING

Our IT training courses are developed with industry-standards and career-focused technologies.


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Award:
Certification
Awarding Body:
Data Science R
Category:
Duration & Study Mode:
Full time, Part time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

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

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If you enrol two months in advance

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Who is this certification for?

  • Mostly the computer professionals are the key target audience of this training program. But the newbie’s may also participate in this training, as well.
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Small Groups

With small groups of students, our instructors can work closely with each student.

Schedule
Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.

Instructors
Experienced Instructors

Our instructors follow a modified are personalized approach to engage students during class

Lab-Facilities
Hi-Tech Lab Facilities

Our students can access our lab facilities anytime for practical experience during and after studies.

Syllabus

  • 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
  • Introducing Artificial Intelligence and Deep Learning
  • What is an artificial neural network? TensorFlow: The computational framework for building AI models
  • Fundamentals of building ANN using TensorFlow and working with TensorFlow in R
  • What is a time series? The techniques, applications, and components of the time series
  • Moving average, smoothing techniques, and exponential smoothing
  • Univariate time series models and multivariate time series analysis
  • ARIMA model
  • Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
  • 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

When would you like to start?

Start Date TimingDaysDuration Mode Of TrainingAvailability Reserve Now
20/02/202110:00 – 17:00Sat Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
21/02/202110:00 – 16:00Sun Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
22/02/202110:00 – 17:00Mon - Fri5 DaysClassroom / OnlinePlaces available Future Dates/Request Price
23/02/202118:30 – 21:30Tue / Thu5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
06/03/202118:30 – 21:30Sat Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
08/03/202118:30 – 21:30Mon - Fri5 DaysClassroom / OnlinePlaces available Future Dates/Request Price
10/03/2021
18:30 – 21:30Wed / Fri5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
18/03/202118:30 – 21:30Wed / Fri5 DaysClassroom / OnlinePlaces available Future Dates/Request Price
20/03/202118:30 – 21:30Sat Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
22/03/202118:30 – 21:30Mon / Tue5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
27/03/202118:30 – 21:30Sat Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
29/03/202118:30 – 21:30Mon - Fri5 DaysClassroom / OnlinePlaces available Future Dates/Request Price

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Frequently Asked Questions

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At London IT Training, you will get both academic and administrative support, whenever you need and as per your requirement. We have a team of highly skilled and professional individuals who are ready to serve you by all means. You will find our trainers available even after the scheduled class time. London IT Training is also arrange group discussion among the participants and the Instructors, that will help you to get more out from the IT course you are attending. London IT Training is also helping the candidates for a better job placement, who have successfully completed the IT courses from here.
London IT Training continuously update the course content as the paradigm and practice of IT industry is changing and evolving more rapidly than ever before. Our courses are well organized, which will help you to get deep inside the subject matter without facing any difficulties. At London IT Training, the instructors are always keeping themselves busy not only to deliver the subject matter in a quality manner but also concerned about any changes that are required for the curriculum of the course.
In terms of recognition, you have nothing to worry about the IT courses offered by London IT Training. We have already earned the reputation as a training institute in the UK by putting our consistent effort on the training that we offered and also by working in collaboration with a significant number of reputed IT companies across the UK, for our Job placement program. IT professionals, who had successfully completed IT courses from London IT Training, are working with an outstanding reputation regarding the skill and experience, at their workplace and this is one of the key competencies of our training center.
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