Self Paced Training
Rs. 15000
Enrolment validity: Lifetime
Data Science with R Online Training
Mentors Pool Data Science with R certification training lets you gain expertise in Machine Learning Algorithms such as K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. This Data Science with R Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout the R Programming Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.
In collaboration with
36 Hrs
Online Class
Online Class
5 Hrs
Projects
Projects
25 Hrs
Hands-On
Hands-On
n/a
36 Hrs Instructor-led Training
Mock Interview Session
Project Work & Exercises
Flexible Schedule
24 x 7 Lifetime Support & Access
Certification and Job Assistance
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Learner's Career Outcome
Career Services
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Mentors Pool Experience
Data Science with R Online Training Course Overview
1. Tools & Technologies
Get acquainted with various analysis and visualization tools such as Ggplot and plotly
2. Statistics for Data Science
Understand the behavior of data; build significant models to understand Statistics Fundamentals
3. R for Data Science
Learn about the various R libraries like Dplyr, Data.table used to manipulate data
4. Exploratory Data Analysis
Use R libraries and work on data manipulation, data preparation and data explorations
5. Data Visualization using R
Use of R graphics libraries like Ggvis, Plotly etc.
6. Advanced Statistics & Predictive Modeling
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
- Those Interested in the field of data science
- Those looking for a more robust, structured R learning program
- Those wanting to use R for effective analysis of large datasets
- Software or Data Engineers interested in quantitative analysis with R
While there are no prerequisites, elementary programming knowledge will benefit those who attend this course.
Mentors Pool follows a rigorous certification process. To become a certified Data Science with R , you must fulfill the following criteria:
- Online Instructor-led Course:
- Successful completion of all projects, which will be evaluated by trainers
- Scoring a minimum of 60 percent in the Data Science with R quiz conducted by Mentors Pool
The Big Data Analytics market is expected to reach $40.6 billion by 2023, at a growth rate of 29.7-percent. Randstad reports that pay hikes in the analytics industry are 50-percent higher than the IT industry. Learning R can help you begin a career in data science.
Talk to Us
IN: +91-8197658094
The average salary for a Data Scientist is $120k as per Glassdoor, Businesses analysing data will see $430 billion in productivity benefits over their rivals not analysing data by 2020, The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 – Forbes
Skills Covered
Fees
Online Classroom
- 36 Hrs of Instructor-led Training
- 1:1 Doubt Resolution Sessions
- Attend as many batches for Lifetime
- Flexible Schedule
Batches
Dates
29th June
15th July
20th July
5th Aug
Days
Sat-Sun ( Weekend Class )
Mon-Fri ( Weekdays Class )
Sat-Sun ( Weekend Class )
Mon-Fri ( Weekdays Class )
Timings
7:00 PM – 10:00 PM IST (GMT +5:30)
7:00 AM – 9:00 AM IST (GMT +5:30)
8:00 PM – 10:00 PM IST (GMT +5:30)
7:00 AM – 9:00 AM IST (GMT +5:30)
₹18,000
Enrolment validity: Lifetime
EMI Option Available with different credit cards
Corporate Training
- Customised Learning
- Enterprise grade learning management system (LMS)
- 24x7 Support
- Enterprise grade reporting
Course Content
Data Science with R Online Training Course Content
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
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
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
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
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.Â
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
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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
Learning Objectives – Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.Â
Topics:
- What is Clustering & its use cases
- What is K-means Clustering?
- What is C-means Clustering?
- What is Canopy Clustering?
- What is Hierarchical Clustering?
Hands-On/Demo:
- Implementing K-means Clustering in R
- Implementing C-means Clustering in R
- Implementing Hierarchical Clustering in R
Learning Objectives – In this module, you should learn about association rules and different types of Recommender Engines.
Topics:
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Types of Recommendations
- User-Based Recommendation
- Item-Based Recommendation
- Difference: User-Based and Item-Based Recommendation
- Recommendation use cases
Hands-On/Demo:
- Implementing Association Rules in R
- Building a Recommendation Engine in R
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
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
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
Course Projects
Predict chronic kidney disease using KNN
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics. In this project you will learn to predict Kidney disease using KNN Approach.
Predict Credit Card Defaulter Using Logistic Regression
With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This will help the bank be proactive in collecting dues.
Predict quality of Wine using Decision Tree
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
Course Certification
R is a programming language and free software developed in 1993, made up of a collection of libraries architectures especially for data science. As a tool, R is considered to be clear and accessible.
Anyone who is looking to get started in IT or willing to further their IT career should consider learning R. We at Mentors Pool have compiled an extensive content for Data Science beginners, along with supporting blogs and YouTube videos to help you understand the Data Science basics and importance of R in the dynamic field of data science.
You will:
- Get advanced knowledge of data science and how to use it in real life business
- Understand the statistics and probability of Data science
- Get an understanding of data collection, data mining and machine learning
- Learn tools like R
The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
- Developers aspiring to be a ‘Data Scientist’
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘R’ professionals who wish to work Big Data
- Analysts wanting to understand Data Science methodologies
If you have a Windows system, you should have:
- Microsoft Windows 7 or newer (32-bit and 64-bit)
- Microsoft Server 2008 R2 or newer
- Intel Pentium 4 or AMD Opteron processor or newer
- 2 GB memory
- 1.5 GB minimum free disk space
- 1366 x 768 screen resolution or higher
If you have a MAC system, you should have:
- iMac/MacBook computers 2009 or newer
- OSX 10.10 or newer
- 5 GB minimum free disk space
- 1366 x 768 screen resolution or higher
For executing the practicals, you will set-up R programming IDE on your machine, you can:
- Download RStudio Desktop Open Source License from the Rstudio Official Website for free
- Or, purchase the licensed Full- version of RStudio Desktop Commercial License
The detailed step by step installation guides will be present in your LMS which will help you to install and set-up the required environment. In case you come across any doubt, the 24*7 support team will promptly assist you.
Certification Course Reviews
Very interactive session, It was a very interesting session. There was a lot of stuff to learn, analyze and implement in our career. I want to give 10/10 to a mentor pool for their experts.
Very good,wonderful explanation by trainer, They did handsOn based on real time scenarios Improved my skills Highly recommended. The most important thing in training is hand-on and the training was 80- 85 % handson that's the plus point of Mentors Pool
Trainer explains each and every concept with perfect real time examples, which makes it really easy to understand. I gained more knowledge through him. The way of explaining is awesome.
The way the trainer explained to me is very interactive, he solved all my queries with perfect examples. Helped me in cracking the TCS interview. I am very grateful that I came across Mentors Pool
Certification Course FAQs
On completing R and knowing the fundamentals of Data science, you can aim for a rewarding career in data science. Since the evolution of big data, data science and data analysis have become the most sought after career paths because of the huge demand for data science professionals. Not only high profile technology companies such as Google and Facebook but companies across sectors are hiring data scientists who can generate business and solve complex data related problems. This is the perfect course for you to step into the world of data science and make a career in what has been rated as the best job in America by Glassdoor.com
By the end of this course, you would have gained knowledge on the use of data science techniques and build applications on data statistics. This will help you land jobs as Data Scientists.
No, this R course is not officially accredited.
You can re-attempt it immediately.
Upon successful completion of the Data Science with R training and passing the exam, you will receive the certificate through our Learning Management System which you can download or share via email or Linkedin.
Your instructors are R experts who have years of industry experience.
Talk to Us
IN: +91-8197658094
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