Data Science Using R Course And Training
The “Data Science using R” course and training provided by AADS Education transforms the learners into experts of Data Science by using the R programming. AADS Education is an internationally accredited and recognised IT, Media and Management services education company that is a recipient of multiple international awards. Some of the highlights and exclusive features of our “Data Science using R” course and training are given below.
- Learners can opt for different training modules, including classroom training, e-Learning training, and live virtual classroom.
- The e-learning and training sessions of the course are as good and on par with the classroom training module.
- Five hours of highly in -demand videos.
- Highly interactive training sessions that include the videos. These sessions are simple for learners to understand and interpret.
- Learners get lifetime access to all the course materials from anywhere, and at any point in time.
- We have certified trainers having more than 10 years of the real world experience. The trainers are currently working as a Data Scientist.
- The training includes use-case application scenarios and case studies.
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24/7 support available to learners/trainees and learners through live chat and email.
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Trainees can gain access to the course from many different devices including laptops, computers, smartphones, and tablets.
- AADS Education has been providing a world-class education for the past 18 years through in-demand e-Learning online and classroom course training sessions.
- The learners/trainees will be exposed to new chapters of knowledge that may not be available in other traditional R programming courses or in the computer science coursebooks.
- Learners attain certification after completing the course.
- Assignment on concepts is also provided.
- The course/training stresses on practical understanding and the experts use the mathematical concepts of statistical tools.
- The tools are used in the form of input commands of the programming languages for better distinguishing of the results.
- The training is provided under the guidance and supervision of certified and recognised experts and also includes real-world applications.
- The hands-on training sessions include applications within the domain of business analytics, machine learning, statistics, operation research, decision science, and data engineering.
- We align with and are partners of the best of IT firms including Oracle, PeopleSoft, and Axelos among others.
- We offer 100% placement assistant to all our learners/trainers and help them get placed in leading MNCs.
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
- Course available via: e-Learning, Classroom, Live Virtual Classroom (online training)
- 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
Classroom Training*
For delegates residing in Hyderabad, Bangalore (India)
INR 25,000 +(GST)
Fill "I AM INTERESTED IN THIS COURSE" form for more details
Live Virtual Classroom Training*
Instructor-led Training:Attend from anywhere!
INR 20,000 +(GST)
Fill "I AM INTERESTED IN THIS COURSE" form for more details
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
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, bar chart commands
- 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
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
- 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
- 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
- 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
- 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
- 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
Part 1
- 5 powerful unanswered questions by regression which remain unknown
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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
- 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
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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