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:
Data Science
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

Select your preferred training delivery mode

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

Customers
Who We Work With

REQUEST FOR TRAINING


Saumya Prasad

London IT Training helped me kick start my Networking career with Cisco CCNA. They were very professional and flexible with the class schedules. It helped me plan my time between studies and the hectic schedule of my job.

M. Abdulla

London IT Training helped me kick start my Networking career with Cisco CCNA. They were very professional and flexible with the class schedules. It helped me plan my time between studies and the hectic schedule of my job.

Alberto

I did my CompTIA A+ certification at London IT Training. I liked the teaching style of my instructor.

Mr.Ahmed

I’I can confidently recommend London IT Training to anyone looking to learn networking technologies as it’s an excellent training center. The resources and conduciveness of the learning center is above the mark, I am very thankful to the management and academic staff for their support during my course period.

Liz Maryse

I did a fast track CCNP course in London IT Training, and it was one of the best decisions I made. Lecturers are friendly and are well conversant with the course and are very excellent in delivering the course to the students. Advantages in London IT Training is that its Lecturers are familiar in industrial standard network infrastructure and functions.

I was able to find employment easily due to the reputation of London IT Training centre, even after my completion of the course I was able to consult the lecturers on any issues and continued my training which made me efficient in the job sector.

Mr.Hani

I went to London IT Training to learn CCNA. The overall appeal of the environment was pleasant and suitable for learning, the fees structure was reasonable and the staff where very pleasant and patient in explain all the details of the course structure. On completion of my course they helped me find a suitable employment, readied me for the real time challenges I would face in my job which made me an efficient worker.

The center had students from different part of the Country and World, which made the place appealing. As considering the training, I was amazed how the academic staff had an in depth knowledge of the subjects and delivered them easily to students for easy understanding. They coached us excellently throughout our course period.

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