Data Science Certification Training – R Programming
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Course Overview

The Data Science Certification Training - R Programming course offers a comprehensive learning experience, covering a wide range of topics in data science, statistical analysis, machine learning, and data visualization using the R programming language. Through this course, you will learn how to use R packages and tools to manipulate, analyze, and visualize data, as well as build machine learning models for predictive analytics. You will also gain practical experience by working on real-world projects that simulate industry scenarios.

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Data Science Certification Training – R Programming
Data Science Certification Training – R Programming Content

1.1 Understanding the fundamentals of data science and its applications
1.2 Introduction to R and its features for data analysis
1.3 Setting up the R environment and familiarizing with R Studio

2.1 Data exploration and cleaning techniques using R
2.2 Handling missing values and outliers
2.3 Data transformation and feature engineering with dplyr and tidyr packages

3.1 Creating static and interactive visualizations using ggplot2 package
3.2 Plotting charts, histograms, and scatter plots
3.3 Customizing visualizations for effective data communication

4.1 Descriptive statistics and data summarization
4.2 Analyzing distributions and relationships between variables
4.3 Extracting insights from data using statistical techniques

5.1 Hypothesis testing and confidence intervals
5.2 ANOVA, t-tests, and chi-square tests
5.3 Correlation and regression analysis

6.1 Introduction to machine learning algorithms and concepts
6.2 Supervised learning techniques (linear regression, logistic regression)
6.3 Unsupervised learning techniques (clustering, dimensionality reduction)

7.1 Understanding ensemble learning and its advantages
7.2 Building random forest models for classification and regression problems
7.3 Evaluating model performance and tuning hyperparameters

8.1 Preprocessing text data using R
8.2 Performing sentiment analysis on text data
8.3 Extracting insights from text data using text mining techniques

9.1 Understanding time-dependent data and its characteristics
9.2 Forecasting techniques (ARIMA, exponential smoothing)
9.3 Analyzing trends, seasonality, and anomalies in time series data

10.1 Introduction to deep learning concepts
10.2 Building deep neural networks using the Keras package in R
10.3 Training and evaluating deep learning models for image classification and natural language processing tasks
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    Data Science Certification Training – R Programming Projects

    1.1 Analyze customer data to identify meaningful segments
    1.2 Use clustering techniques in R to segment customers based on their characteristics
    1.3 Develop targeted marketing strategies for each customer segment

    2.1 Predict stock prices using historical stock market data
    2.2 Build time series forecasting models in R to predict future stock prices
    2.3 Evaluate the accuracy of the prediction models

    3.1 Analyze credit data to assess creditworthiness and predict default risks
    3.2 Build classification models in R to predict credit risk
    3.3 Evaluate model performance and recommend appropriate credit decisions

    4.1 Analyze customer data to predict churn behavior
    4.2 Build machine learning models in R to identify customers at risk of churn
    4.2 Evaluate model performance and develop retention strategies

    5.1 Build a recommendation engine in R to provide personalized recommendations
    5.2 Use collaborative filtering techniques to recommend items based on user preferences
    5.3 Evaluate the performance of the recommender system

    Big Data Hadoop Course Fee

    Preffered

    Online Classroom

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    Corporate Training
    • 36 hours of instructor-led online training
    • Flexibility to choose classes
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    All Our Programs Include

    This training course is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exams.

    Real-world projects from industry experts

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Technical mentor support support

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Personal career coach and career services

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Flexible learning program

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

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    Data Science Certification Training – R Programming Certification

    Student Reviews
    4.5   (1.2k)
    FAQ’s

    This course is designed for beginners and does not require any specific prerequisites. However, a basic understanding of programming concepts and statistics would be beneficial.

    No, prior experience in R programming is not necessary. The course covers the basics of R programming from the ground up.

    Yes, this course is suitable for both beginners and experienced data professionals. It provides a comprehensive understanding of data science concepts and their application using R.

    Yes, you will have access to the course materials through our dedicated learning management system. You can access them online at any time.

    Yes, upon successful completion of the course and passing the certification exam, you will receive a certificate of completion as a Data Science Certified Professional in R Programming.

    Yes, you can interact with the instructors during the course through interactive sessions, discussion forums, and Q&A sessions.

    Yes, the course includes hands-on projects and real-time case studies to provide practical training and application of data science concepts using R.

    Yes, there will be assessments and a final certification exam to evaluate your understanding and progress.

    Yes, our technical support team is available to assist you with any technical difficulties you may encounter during the course.

    Yes, you can work on the projects at your own pace. However, it is recommended to follow the suggested project timeline to stay on track with the course schedule.

    We offer financial assistance options, including scholarships and installment plans. Please contact our admissions team for more information.

    Yes, you will have lifetime access to the course materials even after completing the course. You can refer to them for future reference or to refresh your knowledge.

    The course offers flexible learning options, allowing you to study at your own pace. However, there may be suggested timelines and deadlines for better learning outcomes.

    Yes, you will have the option to download the course materials for offline access through our learning management system.

    While this course does not guarantee job placement, we provide career guidance and support to help you explore job opportunities in the field of data science.

    We have a refund policy in place. Please refer to our refund policy for more information on eligibility and terms.

    If you decide to switch to a different course within a specific timeframe, we can assist you with the transition. Please contact our support team for more details.

    While the emphasis is on individual projects, there may be opportunities for collaboration and peer-to-peer learning with other learners.

    The course will be conducted using the latest versions of R and R Studio. Any updates or changes will be communicated to the learners.

    To enroll in the course, simply visit our website and follow the instructions for enrollment. Our admissions team will guide you through the process and assist with any questions or concerns.

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