DATACAMP . COM {}

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Keywords {🔍}

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Topics {✒️}

mobile courses predict future events data science machine learning scientist tracks helped 3 feature engineering feature engineering process data data skills data center bayesian statistics skill track learner stories careers machine learning perspective machine learning prior statistics machine learning models learn career track machine learning 5 machine learning full datacamp platform privacy policy career city monument bank generalized additive models natural language processing pickard predictives linkedin profile teams enroll business loved unsupervised learning 1 supervised learning 2 supervised learning 4 unsupervised learning business training 2 start track instructors faqs teams business track prepare datacamp 2025 datacamp programming language free included perform supervised random forests tidymodels framework dimensionality reduction

Questions {❓}

• Do I need knowledge of machine learning prior to taking this track?
• How long does it take to complete this Track?
• How will this Track prepare me for my career?
• Is this Track suitable for beginners?
• Training 2 or more people?
• What is the programming language of this Track?
• What topics will I learn during this track?
• What's the difference between a skill track and a career track?
• Which jobs will benefit from this Track?

Schema {🗺️}

Course:
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      about:
         R
         Machine Learning
      availableLanguage:
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      description:Master the essential skills to land a job as a machine learning scientist! You'll augment your R programming skillset with the toolbox to perform supervised and unsupervised learning. You'll learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance. In the process, you'll get an introduction to Bayesian statistics, natural language processing, and Spark.
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               name:DataCamp
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            description:Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
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               name:DataCamp
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      name:Unsupervised Learning in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/unsupervised-learning-in-r
      description:Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
      name:Machine Learning in the Tidyverse
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/machine-learning-in-the-tidyverse
      description:Learn to perform linear and logistic regression with multiple explanatory variables.
      name:Intermediate Regression in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/intermediate-regression-in-r
      description:Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
      name:Cluster Analysis in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/cluster-analysis-in-r
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      name:Machine Learning with caret in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/machine-learning-with-caret-in-r
      description:Learn to streamline your machine learning workflows with tidymodels.
      name:Modeling with tidymodels in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/modeling-with-tidymodels-in-r
      description:Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
      name:Machine Learning with Tree-Based Models in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/machine-learning-with-tree-based-models-in-r
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      name:Dimensionality Reduction in R
      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/dimensionality-reduction-in-r
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      name:Support Vector Machines in R
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         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/support-vector-machines-in-r
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         name:DataCamp
      url:https://www.datacamp.com/course/fundamentals-of-bayesian-data-analysis-in-r
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         name:DataCamp
      url:https://www.datacamp.com/course/hyperparameter-tuning-in-r
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         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/bayesian-regression-modeling-with-rstanarm
      description:Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
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      provider:
         type:Organization
         name:DataCamp
      url:https://www.datacamp.com/course/introduction-to-spark-with-sparklyr-in-r
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      category:Subscription
      category:Subscription
CourseInstance:
      courseMode:online
      courseWorkload:PT65H0M
      instructor:
            type:Person
            description:VP, Data Science at DataRobot
            image:https://assets.datacamp.com/users/avatars/000/000/223/original/zach.jpg?1471726055
            name:Zachary Deane-Mayer
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         postalCode:10119
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      description:What is DataCamp? Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.
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            type:Question
            acceptedAnswer:
               type:Answer
               text:No, this track is not suitable for absolute beginners. This track is designed for students who are already familiar with R programming and have a basic understanding of machine learning. Before starting this track, we recommend that users should have a basic understanding of statistics, linear algebra, and calculus.
            name:Is this Track suitable for beginners?
            type:Question
            acceptedAnswer:
               type:Answer
               text:This track uses R programming language. R is a popular open-source programming language for data analysis and statistical computing.
            name:What is the programming language of this Track?
            type:Question
            acceptedAnswer:
               type:Answer
               text:This track is best suited for those who want to land a job as a machine learning scientist. It will help users learn the essential skills needed to work as a data scientist, research scientist, or AI engineer. Beyond this, people wanting to gain a more in-depth knowledge of machine learning and R programming can also make use of this track.
            name:Which jobs will benefit from this Track?
            type:Question
            acceptedAnswer:
               type:Answer
               text:This track will equip you with the in-depth knowledge of R programming and machine learning algorithms. You will learn about supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.
            name:How will this Track prepare me for my career?
            type:Question
            acceptedAnswer:
               type:Answer
               text:This track is self-paced so users can spend as long or as little time as they like working through exercises and courses. Generally, it takes around 65 hours to go through the entire track, as it consists of multiple courses.
            name:How long does it take to complete this Track?
            type:Question
            acceptedAnswer:
               type:Answer
               text:A skill track focuses on a specific technique or technology related to a certain job. Whereas, a career track focuses on a broader set of skills and expertise that can help in a career as a whole, such as a data scientist or software developer.
            name:What's the difference between a skill track and a career track?
            type:Question
            acceptedAnswer:
               type:Answer
               text:This track covers topics such as machine learning algorithms using R, supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.
            name:What topics will I learn during this track?
            type:Question
            acceptedAnswer:
               type:Answer
               text:No, it is not necessary to have prior knowledge of machine learning prior to taking this track. However, this track is best suited for those who have a basic understanding of R programming and machine learning concepts.
            name:Do I need knowledge of machine learning prior to taking this track?
Question:
      acceptedAnswer:
         type:Answer
         text:No, this track is not suitable for absolute beginners. This track is designed for students who are already familiar with R programming and have a basic understanding of machine learning. Before starting this track, we recommend that users should have a basic understanding of statistics, linear algebra, and calculus.
      name:Is this Track suitable for beginners?
      acceptedAnswer:
         type:Answer
         text:This track uses R programming language. R is a popular open-source programming language for data analysis and statistical computing.
      name:What is the programming language of this Track?
      acceptedAnswer:
         type:Answer
         text:This track is best suited for those who want to land a job as a machine learning scientist. It will help users learn the essential skills needed to work as a data scientist, research scientist, or AI engineer. Beyond this, people wanting to gain a more in-depth knowledge of machine learning and R programming can also make use of this track.
      name:Which jobs will benefit from this Track?
      acceptedAnswer:
         type:Answer
         text:This track will equip you with the in-depth knowledge of R programming and machine learning algorithms. You will learn about supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.
      name:How will this Track prepare me for my career?
      acceptedAnswer:
         type:Answer
         text:This track is self-paced so users can spend as long or as little time as they like working through exercises and courses. Generally, it takes around 65 hours to go through the entire track, as it consists of multiple courses.
      name:How long does it take to complete this Track?
      acceptedAnswer:
         type:Answer
         text:A skill track focuses on a specific technique or technology related to a certain job. Whereas, a career track focuses on a broader set of skills and expertise that can help in a career as a whole, such as a data scientist or software developer.
      name:What's the difference between a skill track and a career track?
      acceptedAnswer:
         type:Answer
         text:This track covers topics such as machine learning algorithms using R, supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.
      name:What topics will I learn during this track?
      acceptedAnswer:
         type:Answer
         text:No, it is not necessary to have prior knowledge of machine learning prior to taking this track. However, this track is best suited for those who have a basic understanding of R programming and machine learning concepts.
      name:Do I need knowledge of machine learning prior to taking this track?
Answer:
      text:No, this track is not suitable for absolute beginners. This track is designed for students who are already familiar with R programming and have a basic understanding of machine learning. Before starting this track, we recommend that users should have a basic understanding of statistics, linear algebra, and calculus.
      text:This track uses R programming language. R is a popular open-source programming language for data analysis and statistical computing.
      text:This track is best suited for those who want to land a job as a machine learning scientist. It will help users learn the essential skills needed to work as a data scientist, research scientist, or AI engineer. Beyond this, people wanting to gain a more in-depth knowledge of machine learning and R programming can also make use of this track.
      text:This track will equip you with the in-depth knowledge of R programming and machine learning algorithms. You will learn about supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.
      text:This track is self-paced so users can spend as long or as little time as they like working through exercises and courses. Generally, it takes around 65 hours to go through the entire track, as it consists of multiple courses.
      text:A skill track focuses on a specific technique or technology related to a certain job. Whereas, a career track focuses on a broader set of skills and expertise that can help in a career as a whole, such as a data scientist or software developer.
      text:This track covers topics such as machine learning algorithms using R, supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.
      text:No, it is not necessary to have prior knowledge of machine learning prior to taking this track. However, this track is best suited for those who have a basic understanding of R programming and machine learning concepts.

Social Networks {👍}(5)

• https://www.facebook.com/datacampinc/
• https://twitter.com/datacamp
• https://www.linkedin.com/school/datacampinc/
• https://www.youtube.com/channel/UC79Gv3mYp6zKiSwYemEik9A
• https://www.instagram.com/datacamp/

External Links {🔗}(1)

• How much does https://www.rdocumentation.org make?

Analytics and Tracking {📊}

• Site Verification - Google

Libraries {📚}

• D3.js

Emails and Hosting {✉️}

Mail Servers:

• aspmx.l.google.com
• alt1.aspmx.l.google.com
• alt2.aspmx.l.google.com
• aspmx2.googlemail.com
• aspmx3.googlemail.com

Name Servers:

• ns-1317.awsdns-36.org
• ns-1648.awsdns-14.co.uk
• ns-343.awsdns-42.com
• ns-543.awsdns-03.net