Data Science with R Certification

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Learner rating9.5

Data Science with R certification, you will gain expertise in Machine Learning Algorithms and will be implementing real life cases on various aspects.


Data Science with R Certification

Edureka’s Data Science with R Training lets you gain expertise in Machine Learning Algorithms. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation, and HR. So, let’s see what is data science and why R is used for data science

What is data science?

Data science, in its most basic terms, can be defined as obtaining insights and information, really anything of value, out of data. Like any new field, it’s often tempting but counterproductive to try to put concrete bounds on its definition. This is data science. This is not. In reality, data science is evolving so fast and has already shown such an enormous range of possibilities that a wider definition is essential to understanding it.

Edureka Data-Science-Tutorial01

“We’re entering a new world in which data may be more important than software.”

– Tim O’Reilly, founder, O’Reilly Media

And while it’s hard to pin down a specific definition, it’s relatively easy to see and feel its impact. Data science, when applied to different fields can lead to incredible new insights. And the folks that are using it are already reaping the benefits.

What is R Programming?

R is a programming language and an analytics tool and It is extensively used by Software Programmers, Statisticians, Data Scientists, and Data Miners. It is one of the most popular analytics tool used in Data Analytics and Business Analytics.

What-is-R-Programming

It has numerous applications in domains like healthcare, academics, consulting, finance, media, and many more. Its vast applicability in Statistics, Data Visualization, and Machine Learning have given rise to the demand for certified trained professionals in R.

About the Data Science Certification

Data science is a “concept to unify statistics, data analysis, and their related methods” to “understand and analyses actual phenomena” with data. Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyses and visualize different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. Moreover, you will be implementing real-life cases from various domains like Media, Healthcare, Social Media, Aviation, and HR.

What you will learn

  • Gain insight into the ‘Roles’ played by a Data Scientist.
  • Analyze several types of data using R programming.
  • Describe the Data Science Life Cycle.
  • Work with different data formats like XML, CSV, etc.
  • Learn tools and techniques for Data Transformation.
  • Discuss Data Mining techniques and their implementation.
  • Analyze data using Machine Learning algorithms in R programming.
  • Explain Time Series and it’s related concepts.
  • Perform Text Mining and Sentimental analyses on text data.
  • Gain insight into Data Visualization and Optimization techniques
  • Understand the concepts of Deep Learning.

Who this course is for

The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques to different types of Data.

What are the objectives of our Data Science Online Course?

  • In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
  • Comprehensive knowledge of various tools and techniques for Data Transformation
  • The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques
  • The exposure to many real-life industry-based projects which will be executed in R Studio
  • Projects which are diverse in nature cover media, healthcare, social media, aviation, and HR
  • Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices

Syllabus:

1. Introduction to Data Science

  • What is Data Science and What does Data Science involve?
  • The era of Data Science, the Life cycle of Data Science, and Tools of Data Science.
  • Business Intelligence vs Data Science.
  • Introduction to data science with R: Big Data, Hadoop, R, Spark, and Machine Learning.

2. Statistical Inference

  • What is Statistical Inference?
  • Terminologies of Statistics.
  • Measures of Centers and Spread.
  • Normal Distribution and Binary Distribution.

3. Data Extraction, Wrangling, and Exploration

  • Data Analysis Pipeline and Types of Data
  • What is Data Extraction
  • Raw and Processed Data and Data Wrangling.
  • Exploratory Data Analysis and Visualization of Data.

4. Introduction to Machine Learning

  • What is Machine Learning?
  • Machine Learning Use-Cases, Process Flow and
  • Supervised Learning algorithm: Linear Regression and Logistic Regression.

5. Data Science with R: Classification Techniques

  • What are classification and its use cases?
  • What is a Decision Tree?
  • Algorithm for Decision Tree Induction and Creating a Perfect Decision Tree.
  • Confusion Matrix.
  • What is a Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification.

7. Unsupervised Learning

  • What is Clustering & its use cases?
  • What are K-means, C-means, Canopy, and Hierarchical Clustering?

8. Recommender Engines

  • What is Association Rules & its use cases?
  • What is the Recommendation Engine & it’s working?
  • Types of Recommendations and Recommendation use cases.
  • User-Based Recommendation and Item-Based Recommendation.
  • Difference: User-Based and Item-Based Recommendation.

9. Text Mining

  • The concepts of text-mining and Use cases.
  • Text Mining Algorithms.
  • Quantifying text.
  • TF-IDF and Beyond TF-IDF.

Project Description

Edureka’s Data Science with R Training includes real-time industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Data Scientist roles.

Prerequisites:

There is no specific pre-requisite for Data Science Training. However, a basic understanding of R programming can be beneficial. Edureka offers you a complimentary self-paced course, i.e. “R Essentials” when you enroll in Data Science Training.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

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  • Edureka
  • Online Course
  • Instructor-led
  • All levels
  • 1-4 Weeks
  • Paid Course (Paid certificate)
  • English
  • Data Science Data Science with 'R' Hadoop Machine learning Spark
Expert Score
9.5
Learner rating
9.5
PROS: Excellent content will clear your conceptually doubts. Good collaboration of practical & theoretical knowledge. Systematic, well organized and supportive faculties.
CONS: Lengthy elementary content. Need brief information about R programming.

Description

Data Science with R Certification

Edureka’s Data Science with R Training lets you gain expertise in Machine Learning Algorithms. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation, and HR. So, let’s see what is data science and why R is used for data science

What is data science?

Data science, in its most basic terms, can be defined as obtaining insights and information, really anything of value, out of data. Like any new field, it’s often tempting but counterproductive to try to put concrete bounds on its definition. This is data science. This is not. In reality, data science is evolving so fast and has already shown such an enormous range of possibilities that a wider definition is essential to understanding it.

Edureka Data-Science-Tutorial01

“We’re entering a new world in which data may be more important than software.”

– Tim O’Reilly, founder, O’Reilly Media

And while it’s hard to pin down a specific definition, it’s relatively easy to see and feel its impact. Data science, when applied to different fields can lead to incredible new insights. And the folks that are using it are already reaping the benefits.

What is R Programming?

R is a programming language and an analytics tool and It is extensively used by Software Programmers, Statisticians, Data Scientists, and Data Miners. It is one of the most popular analytics tool used in Data Analytics and Business Analytics.

What-is-R-Programming

It has numerous applications in domains like healthcare, academics, consulting, finance, media, and many more. Its vast applicability in Statistics, Data Visualization, and Machine Learning have given rise to the demand for certified trained professionals in R.

About the Data Science Certification

Data science is a “concept to unify statistics, data analysis, and their related methods” to “understand and analyses actual phenomena” with data. Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyses and visualize different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. Moreover, you will be implementing real-life cases from various domains like Media, Healthcare, Social Media, Aviation, and HR.

What you will learn

  • Gain insight into the ‘Roles’ played by a Data Scientist.
  • Analyze several types of data using R programming.
  • Describe the Data Science Life Cycle.
  • Work with different data formats like XML, CSV, etc.
  • Learn tools and techniques for Data Transformation.
  • Discuss Data Mining techniques and their implementation.
  • Analyze data using Machine Learning algorithms in R programming.
  • Explain Time Series and it’s related concepts.
  • Perform Text Mining and Sentimental analyses on text data.
  • Gain insight into Data Visualization and Optimization techniques
  • Understand the concepts of Deep Learning.

Who this course is for

The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques to different types of Data.

What are the objectives of our Data Science Online Course?

  • In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
  • Comprehensive knowledge of various tools and techniques for Data Transformation
  • The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques
  • The exposure to many real-life industry-based projects which will be executed in R Studio
  • Projects which are diverse in nature cover media, healthcare, social media, aviation, and HR
  • Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices

Syllabus:

1. Introduction to Data Science

  • What is Data Science and What does Data Science involve?
  • The era of Data Science, the Life cycle of Data Science, and Tools of Data Science.
  • Business Intelligence vs Data Science.
  • Introduction to data science with R: Big Data, Hadoop, R, Spark, and Machine Learning.

2. Statistical Inference

  • What is Statistical Inference?
  • Terminologies of Statistics.
  • Measures of Centers and Spread.
  • Normal Distribution and Binary Distribution.

3. Data Extraction, Wrangling, and Exploration

  • Data Analysis Pipeline and Types of Data
  • What is Data Extraction
  • Raw and Processed Data and Data Wrangling.
  • Exploratory Data Analysis and Visualization of Data.

4. Introduction to Machine Learning

  • What is Machine Learning?
  • Machine Learning Use-Cases, Process Flow and
  • Supervised Learning algorithm: Linear Regression and Logistic Regression.

5. Data Science with R: Classification Techniques

  • What are classification and its use cases?
  • What is a Decision Tree?
  • Algorithm for Decision Tree Induction and Creating a Perfect Decision Tree.
  • Confusion Matrix.
  • What is a Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification.

7. Unsupervised Learning

  • What is Clustering & its use cases?
  • What are K-means, C-means, Canopy, and Hierarchical Clustering?

8. Recommender Engines

  • What is Association Rules & its use cases?
  • What is the Recommendation Engine & it’s working?
  • Types of Recommendations and Recommendation use cases.
  • User-Based Recommendation and Item-Based Recommendation.
  • Difference: User-Based and Item-Based Recommendation.

9. Text Mining

  • The concepts of text-mining and Use cases.
  • Text Mining Algorithms.
  • Quantifying text.
  • TF-IDF and Beyond TF-IDF.

Project Description

Edureka’s Data Science with R Training includes real-time industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Data Scientist roles.

Prerequisites:

There is no specific pre-requisite for Data Science Training. However, a basic understanding of R programming can be beneficial. Edureka offers you a complimentary self-paced course, i.e. “R Essentials” when you enroll in Data Science Training.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • Edureka
  • Online Course
  • Instructor-led
  • All levels
  • 1-4 Weeks
  • Paid Course (Paid certificate)
  • English
  • Data Science Data Science with 'R' Hadoop Machine learning Spark

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Data Science with R Certification
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