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(Class 1Hr - 1:30Hrs) / Per Session
(Class 1Hr - 1:30Hrs) / Per Session
(Class 3hr - 3:30Hrs) / Per Session
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No Interest Financing start at ₹ 5000 / month
Overview of Data Science with R Online Training
The Data Science with R course is a comprehensive, specialized curriculum designed to provide students with the skills and knowledge necessary to be successful in the field of data science using the R programming language. Just a few of the numerous topics addressed in this course include data manipulation, data visualization, statistical analysis, machine learning, and predictive modeling. Participants will learn how to harness the extensive ecosystem of R's libraries and packages to prepare and clean data, create compelling visualizations, conduct statistical analyses, and create cutting-edge machine learning models.
Get Our Resourceful Data Science with R Training:
- The Data Science with R course teaches data science concepts, methods, and tools using the R programming language.
- R, an open-source statistical computing and data analysis language, is ideal for huge datasets.
- The course includes data processing, visualization, statistical analysis, machine learning, and mining.
- Throughout the course of this certification, our highly skilled instructors will instruct you in all of the required analytical and advanced data visualization abilities through the use of actual application situations.
- A dedicated placement officer is on staff at the academy to assist students with placement.
- Our training is customized to your individual needs, and we heavily emphasise providing hands-on experience and instruction on real-world projects.
Data Science With R Trends and Techniques:
In this article, we will discuss some current R-based data science trends and methodologies. Data science is an ever-evolving discipline, therefore it's important to follow the newest news and think much beyond the scope of this article.
Interdisciplinary Collaboration: Data scientists, subject matter experts, and business analysts are increasingly working together in data science projects. The adaptability and interoperability of R make it an excellent platform for facilitating such partnerships.
Automation and AutoML: The use of software to do ML tasks automatically is becoming more common. Feature engineering, model selection, and hyperparameter tweaking are just some of the data science tasks that are being automated by R packages.
Explainable AI (XAI): The demand for interpretability and explainability of artificial intelligence (XAI) increases as the complexity of machine learning models increases. Improvements in R packages and methods that provide light on model choices are anticipated.
Preprocessing and Feature Engineering: Model performance relies heavily on the quality of data preparation and feature engineering. Data wrangling, cleansing, and transformation are frequent uses for R's dplyr and tidy packages.
Bayesian Inference: Data scientists are able to construct intricate probabilistic models and execute Bayesian inference for uncertainty estimates because to R's robust support for Bayesian modeling and probabilistic programming.
Advantages of Data Science With R:
- Data processing, statistical analysis, machine learning, visualization, and more may all be performed using R's extensive package ecosystem. These bundles cut down on prep time by delivering ready-to-use tools and functionalities.
- R's roots in statistics make it an ideal tool for conducting elaborate statistical studies and developing intricate models. It provides several statistical operations, including hypothesis testing, regression, analysis of variance, and more.
- R is often regarded as a superior tool for data visualization. Particularly useful for studying data and presenting findings is the ggplot2 package, which makes it possible to design unique visuals of publishing quality.
- R is a programming language that is freely available to anybody since it is an open-source project. This opens up data science to a wider audience and fosters community-wide cooperation.
- Data scientists, statisticians, and analysts may find helpful people in R's thriving community. This group aids in the creation of packages, provides forum help, and exchanges information and data.
- R's adaptability means that you may modify your analysis and modeling to meet the requirements of your project. Functions and packages may be written from scratch, or existing ones can be modified to meet specific needs.
Career Scope After Data Science With R Course:
Data science using R courses lead to careers in data analysis, predictive modeling, machine learning, and more. The course teaches data-driven decision-making abilities that may be used in many fields. Data Science with R graduates may pursue these careers:
Data Scientist: You will do data analysis, create prediction models, and draw useful conclusions as a data scientist. You will utilize R for data cleaning, statistical analysis, model construction for machine learning, and data visualization for reporting results.
Machine Learning Engineer: As a Machine Learning Engineer, your primary responsibilities will be in the areas of model and algorithm creation, development, and distribution. Models for a variety of machine learning tasks, including classification, regression, clustering, and more, may be built using R's machine learning packages.
Business intelligence analyst: Facts and figures are essential for running a successful company. You may use your knowledge of R to get employment as a business analyst, where you'll analyze data to spot patterns, opportunities, and trends and advise companies on how to improve their plans.
Quantitative Analyst (Quant): To study financial data and create trading strategies, quantitative analysts (or "Quants") use mathematical and statistical methodologies. R's strengths in statistical analysis and modeling make it an excellent choice for this purpose.
Data Engineer: Data engineers are responsible for the creation and upkeep of data storage and processing systems, including data pipelines, databases, and data warehouses. Prior to being imported into storage systems, data may be preprocessed and transformed using R.
Tools used for Data Science With R:
- RStudio is a very potent R IDE (integrated development environment). Code editing, package management, data visualization, and integrated documentation are just a few of the capabilities it offers.
- Data processing, statistical analysis, and modeling are all best performed in the R programming language itself. For a variety of data science jobs, it provides a comprehensive set of pre-installed tools and libraries.
- Data cleaning, visualization, and analysis are simplified with the help of Tidyverse, a set of R programs. Packages like ggplot2 and dplyr for data manipulation and tidyr for data cleaning are included.
- ggplot2 is a widely used software for generating sophisticated, adaptable plots and charts from data. Using a graphical syntax with several layers, you may construct elaborate visualizations.
- Data manipulation and transformation programs like dplyr and tidyr simplify activities like filtering, sorting, and reshaping data.
- The caret package offers a consolidated user experience for managing both model training and testing. Model selection, fine-tuning of hyper-parameters, and cross-validation are only few of the many applications.
Top Skills You Will Gain
- R Programmming, Python, SAS
- Artifical Intelligence
- Deep Learning
- Machine Learning
- Statistics, Naive Bayes
- Linear Algebra, CART
- Programming, Neural Networks
- Data Mining, Visualization
Data Science with R Course Key Features 100% Money Back Guarantee
5 Weeks TrainingFor Become a Expert
Certificate of TrainingFrom Industry Data Science with R Experts
Beginner FriendlyNo Prior Knowledge Required
Build 3+ ProjectsFor Hands-on Practices
Lifetime AccessTo Self-placed Learning
Placement AssistanceTo Build Your Career
Top Companies Placement
Annual SalaryHiring Companies
Data Science with R CourseCurriculam
Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Data Science with R are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Technical, Mathematical , Programming , SQL , Data Science , Machine Learning , Working with Unstructured Data.
Syllabus of Data Science with R Online Course Download syllabus
- What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
- Hands-on Exercise - Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
- Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.
- Hands-on Exercise -Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
- Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.
Hands-on Exercise -Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
- Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.
- Hands-on Exercise -Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
- Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
- Hands-on Exercise -– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
- Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
- Hands-on Exercise -Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
- Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression
- Hands-on Exercise -Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
- What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.
- Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
- What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.
- Hands-on Exercise -Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.
- Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.
- Hands-on Exercise -Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
- introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
- What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
- Hands-on Exercise -Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.
- 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 Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
- Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
- This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
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Wallmart Sales Data Set
Retail is another industry that extensively uses analytics to optimize business processes.
Flipkart Classification Dataset
This project is to forecast sales for each department and increasing labelled dataset using semi-supervised classification.
Employee Management System (EMS)
Create a new system to automate the regulation creation and closure process.
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- Our mock interviews will be conducted by industry experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
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Data Science with R Training Objectives
- R is a highly extensible and easy to learn language and fosters an environment for statistical computing and graphics.
- All of this makes R an ideal choice for data science, big data analysis, and machine learning.
- As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis.
- R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.
- Warming up
- Setting up your machine
- Learn the basics of R language
- Understanding the R community
- Importing and manipulating your data
- Effective Data Visualization
- Data Mining and Machine Learning
- Reporting Results
- Introduction to data science life cycle
- In depth knowledge of most popular machine learning techniques
- Supervised and unsupervised learning techniques
- Real life case studies and simulated projects to sharpen your skill sets
- R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software.
- Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better.
- If you have experience in any programming language, it takes 7 days to learn R programming spending at least 3 hours a day.
- If you are a beginner, it takes 3 weeks to learn R programming. In the second week, learn concepts like how to create, append, subset datasets, lists, join.
- Yes, many learners start with no coding experience and go on to get jobs as data analysts, data scientists, and data engineers.
- R is a great language for programming beginners to learn, and you don't need any prior experience with code to pick it up.
- You need to update your programming skills in R programming regularly. It can only be possible by practicing every day.
- Write code on your own and validate your code with the solutions on the Internet. Consider time complexity and space complexity while writing R code.
- R is a programming language and free software. R possesses an extensive catalog of statistical and graphical methods.
- Most of the R libraries are written in R, but for heavy computational tasks, C, C++ and FORTRAN codes are preferred.
- Depends on your ability but still very difficult. You may start with running small programs like file open/ close by changing directory etc.,
- Writing real program may take longer time. But you will be very strong in R if you just learn the basics for 1 month with a real effort.
Data Science with R Course & Certification
- R Programming
- Statistical Analysis
- Data Visualization
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
Yes, Learnovita certificates are often recognized by the industry. However, the recognition can vary depending on factors such as the specific certification, the reputation of the training provider, and the industry's preferences.
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Quantitative Analyst
- Business Analyst
The duration of a Data Science with R program can vary based on factors such as the program's structure, your prior knowledge, and your learning pace. Generally, these programs range from a few weeks to several months, depending on the depth of the curriculum and the time you can dedicate each day or week.
- Introduction to Data Science and R Basics
- Data Visualization with R
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Statistical Analysis with R
Pranav SrinivasSoftware Testing, Capgemini
Data Science with R Course FAQ's
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