- Data Types
- Introduction to Data Science Tools
- Statistics
- Approach to Business Problems
- An overview of Analytics
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Data Science With R Training
Master the major aspects of Data Science With R by joining the online Data Science With R Training by Mindamajix.
Online
Quick facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays, Weekends
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Course overview
Data Science With R Training Course helps the learners to get an overview of various strands of data sciences with R, the widely used data analytics tool, and enables them to make a move ahead in their career. The curriculum will walk the students through the whole range of aspects related to Data Science with R including data visualization, data manipulation, hypothesis testing, charts, machine learning algorithms, and many more.
Data Science With R Training online course, offered by Mindmajix Technologies, has some prerequisites that the learners must have to be eligible for the programme, namely, the basic understanding of Data Analytics and R. Data Science With R Training certification will also introduce the students to Business Analytics, Test Statistic, Logistic Regression Analysis, Clustering Models and whatnot along with the practical exposure through projects. The interested students can attend the training programme via either of the three enrolment options made available by Mindamjix.
The highlights
- 100% online course
- Offered by Mindmajix Technologies
- FREE Demo on Request
- Flexible Schedule
- Online Live and Self-paced Training Options
- 24/7 Lifetime Support
- Life-Time Self-Paced Videos Access
- One-on-One Doubt Clearing
- Certification Oriented Curriculum
Program offerings
- One-on-one doubt clearing sessions
- Certification oriented curriculum
- Real-time project use cases
- 20 hours of labs
- Free demo on request
- 24/7 lifetime support
- 30 hours of sessions
- Online live and self-paced training options
Course and certificate fees
certificate availability
certificate providing authority
What you will learn
After the completion of Data Science With R Training online certification, the student will learn many concepts of Data Science With R such as R Data Visualization, Cluster Analysis, Regression Analysis, Apriori Algorithm, and the like. Plus, the learners will explore business analytics, R-studio, R packages, the data structure used in R, and importing and exporting data in R.
Who it is for
The syllabus
Introduction to Data Science Methologies
An Overview of R and Business Analytics
Overview of R
- Introduction to R
- Data Structures and Manipulation in R
- Installing R on Vsrious Operating Systems
- IDEs for R
- Steps in R Initiation
- Installing an R Package
Overview of Business Analytics
- Introduction to Business Analytics
- Analytics Technology and Resources
- Need of Business Analytics
- Types and Features of Business Analytics
- Descriptive and Predictive Analytics
- Business Decisions
- Analytical Tools
- Data Science as a Strategic Asset
Data Visualization and Manipulation
- Introduction
- Types of Graphics
- Save Graphics to a File
- Graphics in R
- Create a word cloud
- Exporting Graphs in EStudio
Hypothesis Testing
- Need of Hypothesis Testing in Businesses
- Chances of Errors in Sampling
- Level of Significance
- Types of Statistical Hypothesis Tests
- Test Statistic
- Types of Errors
- Use Normal and Student Probability Distribution Functions
- Objectives of Null Hypothesis Test
- Use Chi-Squared Test Statistics
Logistic Regression Analysis
- Introduction to Regression Analysis and Usage
- Types of Regression Analysis
- Interaction Regression Model
- Correlation
- Logit Function
- Lift charts
- Decile Analysis
Cluster Analysis Classification Models
- Introduction to Cluster Techniques
- Examples of Classification
- Classification Process - Model Construction
- Data Preparation Issues
- Basic Algorithm for a Decision Tree
- Decision Trees in Data Mining
- Naive Bayes Classifier
- Clustering Models
- Use Cases of Clustering
- DBSCAN Clustering Algorithm
- Distance Methodologies
- Hierarchical and Non-Hierarchical Procedure
- K-Means clustering