Data Science Using R Training

Data Science Using R Training

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Key Features

Our e-Learning course is as good as attending classroom session. If you are not happy with the course, get 100% refund.

  • Simple and interactive videos, training sessions
  • 5 hours of on-demand videos
  • Lifetime access: Access anywhere, anytime
  • Access anywhere from any device (Mobile, Laptop, Desktop, Tablet)
  • Trainer with 10 years experience in Data Analytics, currently working as a Data Scientist
  • Course focused to help beginners understand basics of R programming language and statistic concepts
  • Case Study and use-case scenarios included
  • Money Back Guarantee
  • 24/7 Support via email and live chat
  • International Accredited & Award Winning Training Organization
  • 18 Years of Trust in offering classroom, online and e-learning courses
  • Gain knowledge on data science not available in traditional R programming training or computer science textbooks
  • Assignments on concepts
  • Course completion certificate

With our course, you can become an expert in Data Science using R programming by understanding importance of data science and how you can solve problems.

This data science course explains why learning data science is important by taking relevant examples from various domains, provides understanding on statistical and machine learning concepts. For practical understanding, we use basic statistical, mathematical concepts as input commands in R programming to distinguish results. Find yourself involved with case studies and hands-on sessions in the domains of statistics, machine learning, business analytics, decision science, operations research and data engineering.

Delivery Methods for Data Science using R Programming

E-Learning Training

For delegates residing in India
INR 6000 +(GST)

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For delegates residing outside India
USD 175

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Classroom Training*

For delegates residing in Hyderabad, Bangalore (India)
INR 25,000 +(GST)

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Live Virtual Classroom Training*

Instructor-led Training:Attend from anywhere!
INR 20,000 +(GST)

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* After you book a slot for classroom or live virtual classroom training, our executive will reach out to you with schedules and details.

Topics covered in this course

1. Introduction to Data Science
Part 1
  • What is Data science and it’s 5 disruptions
  • Data science v traditional methods
  • Difference in architecture, reference architecture
  • Demystifying machine learning
  • Segmentation technique using R
  • Kmenas, GGPlot, ScatterPlot commands
Part 2
  • Basic R commands
  • Assigning values to objects
  • Creating vectors, matrices
  • Importing data into R, packages to R
  • Rstudio basic options
  • Boxplot, pie, barchart commands
2. Signaling concepts
  • Signals – Key Concepts
  • Analyzing a signal pattern
  • Signal extraction methodology
  • Simplistic nine step process
  • Commands in R – Setwd, Dim, Table, Str
  • Internalize meta-model using commands
Assignment 1: Signaling concepts
3. Uni-Analysis: Commands, Functions in R / Assignment 1
Part 1
  • Uni-variate Analysis
  • Fleet data analysis using Uni-variate concepts
  • Uni-variate outputs using R
  • Using Summary, Table, GGPlot commands
Assignment 2: Use summary, table, ggplot commands
Part 2
  • Concepts of Sample, Population
  • Hypothesis testing: Null and alternate
  • Significance levels/P value
  • Probabilities calculation
  • Pnorm, qnorm, dnorm functions
  • Abline, Rnorm commands
4. Bi-Analysis: Commands, Concepts in R
Part 1
  • Bi-variate Analysis
  • Correlation, Cross tab analysis
  • Outlet purchase behavior model
  • Correlation, Crosstab questions across industries
  • Bivrariate outputs using R
  • Xtab command for observation, dimensions in data
Assignment 3: Examine Cor, Xtab commands
Part 2
  • Correlation, Co-variance
  • Pearson correlation
  • Kendall correlation
  • Spearman rank correlation
5. Visualization with R
  • Visual construct using box, scatter plots, Geo-spatial, heat maps
  • Heats maps example using fillets, brewing industry
  • Spider charts
  • Domestic loan analysis
  • Core concepts in advanced visualization: visualization consumers
  • Creating dashboards
  • Visualization commands in R: Plot, Boxplot, Scatter.smooth, pairs, sp commands
6. Advanced visualization with R
  • Business story telling using R
  • Small multiples, bubble charts commands in R
  • Library command to display libraries
  • Union command to merge databases
  • Unique command to remove duplicate information
  • Intersect command to find common information in two datasets
7. Case Study: Exploratory Data Analysis (EDS) with R
  • Scenario 1: Survival Analysis
  • Scenario 2: Attrition Analysis
  • Scenario 3: Valuable Vulnerable
  • Scenario 4: Day to Repeat Purchase
  • Scenario 5: Identifying Patterns
  • Scenario 6: Segmenting Watch Companies
  • Scenario 7: Customer Lifetime Value
8. Machine Learning in Action
  • Support Vector Machines (SVM), Decision Trees, Random Forest algorithms
  • A/B Testing
  • Collaborative Filtering
  • Fixed Size, Threshold based Neighborhood
  • Graphs
  • Applying algorithms to structured, unstructured data
9. Regression
Part 1
  • 5 powerful unanswered questions by regression which remain unknown
  • Regression Across Sectors
    • Scenario 1: Cost of Insurance
    • Scenario 2: Model Building for Property Design
    • Scenario 3: Estimating Patients Stay at Hospital
    • Scenario 4: Estimate Defect Density
  • Population, Sample Regression Models
  • Commands in R
  • Correlation # Causation
Part 2
  • Linear regression and dependent variables
  • Lm command
  • Summary of models
  • Attribute extraction, assumptions made while fitting a linear model
  • Diagnostic plots in R
10. Dimensionality Reduction Techniques
  • Feature Engineering – Key Point
  • Feature Selection—Definition
  • Feature Selection—Optimality
  • Ranking Criteria—Correlation
  • Feature Subset Selection

What will I learn?

  • Overview of data science; how it relates to other disciplines
  • Discover, predict insights with technical applications of machine learning algorithms
  • Work on case studies in different data science domains
  • Start your career as Data Scientist using R language

Prerequisites

  • Basic Math knowledge is preferred.
  • Computer Access with administrator privileges

Frequently Asked Questions (FAQs)

What is Data Science?

Data science; data-driven science is an interdisciplinary field of scientific methods, systems and processes to extract knowledge or insights from various forms of data.

What are the job roles for Data Science?

Data Scientist, Data Analyst, Machine Learning Specialist, Analytics Manager are some roles that you can pursue.

Who should attend this data science training course?
  • Anyone; freshers looking to start their career in data science and analytics
  • Data Analysts, Analytics Managers, Database Administrators, Data Engineers, Business Intelligence Developers looking to upgrade their career
What programming languages do I have to learn for this course?

There are no prerequisites to learn for this course. However, through this course you will learn R programming basics which are necessary.

Do I get certification after completing the course?

Yes, you will receive a course completion certificate from AADSEducation

How do I become Data Scientist?

Enroll for this course to learn about what career of a data scientist looks like. After course completion, you are ready to take projects to lead your way up as a data scientist.

Why AADSEducation?
  • AADSEducation is internationally accredited and award winning training organization. Being in the education sector providing professional training programs since 2001, we have tied up with various MNCs across the globe, provide training based on industry standards and requirements.
  • Our Data Science with R course focuses on training R programming for beginners who are new to coding. This helps individuals feel easier learning the topics.
How is the training course being provided?

Training is available via e-learning currently. Learn at your own pace!

Who are the trainers?

Trainers are industry experts who currently work in top companies and provide their experience to you in the training program.

How do I implement practical sessions?

You can start working on your computer with guidelines provided from the course or use cloud services

Does AADSEducation provide support after course completion?

We are very well known for our support during the course as well as upon completion. If you have any problems, please notify us and we take care of the rest :)

About the Trainer

Prema Sai has 15 years working in banking analytics including 5 years of Data mining and mathematical modeling. She currently works as a Data Scientist from past 5 years; is set on a path to share her experience to aspirants willing to choose a career in data science. Through this course, she aims to help individuals learn the skills needed to pursue a careen in data science in 2018.


From trainer

This course contains information not established in traditional statistical R programming or computer science textbooks. This course takes one through basic statistic concepts, programming in R which in my view is the most important information you will need to start a career in data science. Learn how data science is distinct from related fields and the value it brings to organizations using big data. Start learning today and become a professional who can deliver this value to your organization.

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