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IT training»IT courses»Bigdata/Hadoop courses»Data Science With R

Data Science With R Training

Learn R Programming, is an open-source software and valuable tool for data scientists and business analysts who need to analyze large datasets quickly and effectively. R is a very versatile tool and can be used for a range of data science tasks, such as data cleaning, data analysis, modelling, prediction and more.

Course duration

230 Hours

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Course syllabus
Price: $500 - $600
  • Introduction to R
  • Data Visualization with R
  • Logistic Regression with R
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About Data Science With R

  • Our Data Science with R training is designed to provide comprehensive learning for beginners and professionals. You will learn the basic R syntax, data structures, and essential programming concepts. The course covers key areas such as statistics, machine learning, and data analysis. With hands-on experience in tools like R Script, ggplot2, and R Markdown, you will use the powerful R programming language to build your data science skills. Master data wrangling, probability, and statistical inference to tackle real-world data problems.

Data Science With R Course Content

  • Overview of Our Data Science with R Course

    Our Data Science with R course offers a strong foundation in R programming, covering R syntax, data frames, and essential statistical concepts. Learn how to implement machine learning algorithms, analyse data, and use R programming for tasks like data wrangling and visualization. We provide a hands-on approach with tools such as Shiny Web App and Plumber API to help you apply concepts in real-world scenarios. The course also includes creating interactive data visualizations with ggplot2 and using regression analysis, time series analysis, and hypothesis testing for deeper data insights.

  • The Objective of Our Data Science with R Training

    Our Data Science with R training aims to equip you with the skills needed to solve complex data problems using R programming. You will master data analysis, statistical techniques, and machine learning algorithms through practical applications. Learn to analyze and visualize data with R, understand descriptive statistics, and gain proficiency in regression analysis, probability, and machine learning techniques. This course prepares you to build real-world data-driven solutions using R tools like R Script, R Notebook, and ggplot2.

  • What You Will Learn in Our Data Science with R Classes

    1. Basic R syntax and programming fundamentals for data analysis.

    2. Data wrangling using dplyr and creating data visualizations with ggplot2.

    3. Statistical concepts include probability, inference, and hypothesis testing.

    4. Hands-on experience in building machine learning models and algorithms.

    5. Applying regression, clustering, and classification techniques to datasets.

    6. Evaluating models using accuracy, precision, recall, and F1-score metrics.

  • Course details

    Duration:

    • 6-8 Weeks

    Mode:

    • Online (Live Instructor-Led) 

    • Offline (Classroom)

    Job Roles:

    • Data Scientist

    • Data Analyst

    • Statistician

    • BI Analyst

    Who Should Enroll? 

    • Beginners looking to enter data science. 


    According to edx.org, over 2.5 Million jobs are in data science and related professions (Burning Glass). Data Science and Analytics professionals earn average starting salaries of over $80,000 in the US.

Data Science With R Course Syllabus

  • Module 1: Introduction to R Programming

    Learn the fundamentals of R and RStudio, covering data types, vectors, matrices, and data frames. Gain a solid foundation for further data analysis with R.

    Course content

    • Basics of R & RStudio 

    • Data types, vectors, matrices, and data frames 

  • Module 2: Data Manipulation with Tidyverse (dplyr, tidyr)

    Master data cleaning and transformation techniques using the Tidyverse package. Learn to handle missing data and identify outliers for robust analysis.

    Course content

    • Data cleaning & transformation 

    • Handling missing data & outliers 

  • Module 3: Data Visualization (ggplot2, Plotly, Shiny)

    Explore creating and customizing visualizations with ggplot2 and Plotly, including interactive dashboards using Shiny to uncover insights.

    Course content

    • Creating graphs, histograms, and interactive dashboards 

    • Customizing plots for business insights 

  • Module 4: Statistical Analysis & Hypothesis Testing

    Understand the difference between descriptive and inferential statistics. Learn key statistical tests like t-tests, ANOVA, and chi-square for hypothesis testing.

    Course content

    • Descriptive vs. inferential statistics 

    • t-tests, ANOVA, chi-square tests 

  • Module 5: Machine Learning with R

    Dive into supervised learning methods (e.g., regression, decision trees, SVM) and unsupervised techniques (e.g., clustering, PCA) for predictive modeling.

    Course content

    • Supervised learning (Regression, Decision Trees, SVM) 

    • Unsupervised learning (Clustering, PCA) 

  • Module 6: Time Series Forecasting

    Learn to forecast trends and stock prices using ARIMA and exponential smoothing methods, applying these techniques to real-world data.

    Course content

    • ARIMA, Exponential Smoothing 

    • Forecasting stock prices & sales trends 

  • Module 7: Natural Language Processing (NLP) in R

    Explore text mining and sentiment analysis to gain insights from textual data, and build a Twitter sentiment analyzer to track public opinions.

    Course content

    • Text mining & sentiment analysis 

    • Building a Twitter sentiment analyzer 

  • Module 8: Capstone Project & Certification Prep

    Complete a comprehensive data science project with a real-world dataset while preparing for certification and enhancing your resume for job interviews.

    Course content

    • End-to-end data science project (Real-world dataset) 

    • Resume-building & interview preparation  

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Data Science With R FAQ's

  • What is Data Science with R?

    Data Science with R is the study of how to use the computer language R to analyze, display, and model statistical data. R is a famous open-source programming language and environment. Data scientists, statisticians, and researchers use it a lot because it has a large library and a community of people who use it.

  • Who can benefit from a Data Science with R course?

    A Data Science with R course can help data analysts, statisticians, researchers, students, and anyone else who wants to learn how to use the R programming language to study and display data.

  • Are there any prerequisites for taking a Data Science with R course?

    It is best if you have a basic knowledge of programming concepts and are familiar with math, statistics, and linear algebra. Having experience with another programming language, such as Python, can be helpful, but it is not necessary.

  • What will I learn in a Data Science with R course?

    A Data Science with R course typically covers the following topics:

    • Introduction to R programming and its features
    • Data manipulation using R packages like dplyr and tidyr
    • Data visualization using ggplot2 and other R packages
    • Statistical analysis and hypothesis testing
    • Linear and logistic regression
    • Machine learning algorithms, such as classification, clustering, and recommendation systems
    • Model evaluation and selection
    • Time series analysis and forecasting
    • Text mining and natural language processing
    • Network analysis and graph theory
    • Reporting and creating interactive dashboards using Shiny

  • How long does it take to complete a Data Science with R course?

    The duration of a Data Science with R course can vary depending on the course provider, format, and the individual's learning pace. Generally, Data Science with R courses ranges from a few weeks to several months.


  • Are there any certifications available for Data Science with R?

    There isn't a special certification for Data Science with R, but you can get in certifications in data science, analytics, or statistics that use R as one of the main tools. Some common certifications include Microsoft Certified: Azure Data Scientist Associate, Data Science Council of America (DASCA) Senior Data Scientist, and Cloudera Data Science Certification.

  • How much does a Data Science with R course cost?

    The price of a Data Science with R course can vary a lot based on who is teaching it, what it covers, and how it is taught (in person or online). Some online tools are free, while comprehensive in-person training programs can cost hundreds or even thousands of dollars.

  • What job opportunities are available after completing a Data Science with R course?

    After taking a study in Data Science with R, people can look for work as data scientists, data analysts, statisticians, researchers, or data engineers. Companies in many fields, such as technology, banking, healthcare, retail, and manufacturing, are investing more and more in making decisions based on data. This is driving the need for skilled workers.

  • Is learning Data Science with R a good investment for my career?

    Yes, learning Data Science with R will help you in your job. The field of data science is growing quickly, and R is one of the most popular computer languages for analyzing data and making statistical models. Getting good at Data Science with R can help you get a better job and stay competitive in the job market.

  • How can I practice and improve my skills in Data Science with R?

    To practice and improve your skills in Data Science with R, you can:


    • Work on personal projects or contribute to open-source projects
    • Participate in online competitions like Kaggle competitions
    • Join online communities and forums to discuss challenges and share ideas with other learners
    • Continuously update your knowledge by reading research papers, blogs, and attending conferences and workshops
    • Practice by analyzing various datasets and solving real-world problems


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