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Data Science Certification Course using R (Virtual Instructor-led Training)

> Data Science certification with R programming training help trainees master data analytics using the R programming language

> As per Statista, the Big Data Analytics market is expected to reach 274.3 billion U.S. dollars by 2022

>  As per Indeed.com, the average salary of a data scientist is $ 122,694 

$ 479 $ 599

CORPORATE TRAINING

Customized to your team's needs
  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Course Overview

Data Science with R Certification help trainees gain expertise in data analytics using the R programming language. This course provides learners with an in-depth understanding of data visualization, predictive analytics, and descriptive analytics techniques, R packages,Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest and Naive Bayes using R. 

Key Highlights

  • 30 Hours of Online Virtual Instructor-led Training
  • Real-life Case Studies
  • Each class has practical assignments
  • Lifetime access to the LMS
  • 24 x 7 Expert Support
  • Certification
  • Community forum for all our learners 
  • Industry based Projects
  • No exam Included 

What You'll Learn

  • Learning about Data structures and data visualization
  • Learning Business analytics
  • Understanding R programming and its packages
  • Learning about Apriori algorithm 
  • Learning Apply functions and DPLYR function
  • Understanding Kmeans and DBSCAN clustering

SCHEDULE

    • Delivery Format: Virtual Classroom Live
    • Location: Online
    • Access Period: 5 Weeks
    • Course Date: NOV 27 th
    • Course Time: 09:30 PM to 12:30 AM (EST)
    • Session: Weekdays
    • Total Class: FRI & SAT (10 Sessions)
    QTY
    $ 479 $ 599

Career Benefits

  • Career opportunities in Data and Analytics
  • Lucrative pay packages 
  • Growing demand 

Who Can Attend

  • Analytics Managers
  • Business Analysts
  • Information Architects
  • R professionals
  • Software developers
  • IT professionals
  • Aspiring data scientists 

Exam Formats

Exam not included 

Course Delivery

This course is available in the following formats:

  • Self-Paced Learning Duration: 30 Hrs

Related Courses

Course Syllabus


Introduction to Data Science

Learning Objectives - Get an introduction to Data Science in this module and see how Data Science helps to analyze large and unstructured data with different tools. 

 

Topics:

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark
  • Introduction to Machine Learning

Statistical Inference

Learning Objectives - In this module, you will learn about different statistical techniques and terminologies used in data analysis. 

 

Topics:

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

Data Extraction, Wrangling and Exploration

Learning Objectives - Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format. 

 

Topics:

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

 

Hands-On/Demo:

  • Loading different types of dataset in R
  • Arranging the data
  • Plotting the graphs

Introduction to Machine Learning

Learning Objectives - Get an introduction to Machine Learning as part of this module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms. 

 

Topics:

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

 

Hands-On/Demo:

  • Implementing Linear Regression model in R
  • Implementing Logistic Regression model in R

Classification Techniques

Learning Objectives - In this module, you should learn the Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, etc. 

 

Topics:

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

 

Hands-On/Demo:

  • Implementing Decision Tree model in R
  • Implementing Linear Random Forest in R
  • Implementing Naive Bayes model in R
  • Implementing Support Vector Machine in R

Text Mining

Learning Objectives - Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module. 

 

Topics:

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

 

Hands-On/Demo:

  • Implementing Bag of Words approach in R
  • Implementing Sentiment Analysis on Twitter Data using R

Time Series

Learning Objectives - In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting. 

 

Topics:

  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting

 

Hands-On/Demo:

  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series Forecasting
  • Forecasting for given Time period

Deep Learning

Learning Objectives - Get introduced to the concepts of Reinforcement learning and Deep learning in this module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for Artificial Neural Networks, and few Artificial Neural Network terminologies. 

 

Topics:

  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN’s