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- Introduction to Data Analytics and R Programming
- R Installation & Setting R Environment
- Variables, Operators & Data types
- Structures
- Vectors
- Vector Manipulation & Sub Setting
- Constants
- RStudio Installation & Lists Part 1
- Lists Part 2
- List Manipulation, Sub Setting & Merging
- List to Vector & Matrix Part 1
- Matrix Part 2
- Matrix Accessing
- Matrix Manipulation, rep fn & Data Frame
- Column Bind & Row Bind
- Data Frame Accessing
- Merging Data Frames Part 1
- Merging Data Frames Part 2
- Melting & Casting
- Arrays
- Factors
- Functions & Control Flow Statements
- Strings & String Manipulation with Base Package
- String Manipulation with Stringi Package Part 1
- String Manipulation with Stringi Package Part 2 & Date and Time Part 1
- Date and Time Part 2
- Data Extraction from CSV File
- Data Extraction from EXCEL File
- Data Extraction from CLIPBOARD, URL, XML & JSON Files
- Database management systems
- Structured Query Language
- Data Definition Language Commands
- Data Manipulation Language Commands
- Sub Queries & Constraints
- Aggregate Functions, Clauses & Views
- Data Extraction from Databases Part 1
- Data Extraction from Databases Part 2 & DPlyr Package Part 1
- DPlyr Package Part 2
- DPlyr Functions on Air Quality DataSet
- Plyr Package for Data Analysis
- Tidyr Package with Functions
- Factor Analysis
- Prob.Table & Cross Table
- Statistical Observations Part 1
- Statistical Observations Part 2
- Statistical Analysis on Credit Data set
- Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
- Box Plots
- Histograms & Line Graphs
- Scatter Plots & Scatter plot Matrices
- Low Level Plotting
- Bar Plot & Density Plot
- Combining Plots
- Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot
- MatPlot, ECDF & BoxPlot with IRIS Data set
- Additional Box Plot Style Parameters
- Set.Seed Function & Preparing Data for Plotting
- QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
- ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal
- Data Exploration and Visualization
- Machine Learning, Types of ML with Algorithms
- How Machine Solve Real Time Problems
- K-Nearest Neighbor(KNN) Classification
- KNN Classification with Cancer Data set Part 1
- KNN Classification with Cancer Data set Part 2
- Navie Bayes Classification
- Navie Bayes Classification with SMS Spam Data set & Text Mining
- WordCloud & Document Term Matrix
- Train & Evaluate a Model using Navie Bayes
- MarkDown using Knitr Package
- Decision Trees
- Decision Trees with Credit Data set Part 1
- Decision Trees with Credit Data set Part 2
- Support Vector Machine, Neural Networks & Random Forest
- Regression & Linear Regression
- Multiple Regression
- Generalized Linear Regression, Non Linear Regression & Logistic Regression
- Clustering
- K-Means Clustering with SNS Data Analysis
- Association Rules (Market Basket Analysis)
- Market Basket Analysis using Association Rules with Groceries Data set
- Python Libraries for Data Science
- Home
- Simpliv Learning
- Courses
- Data Analytics Using R Programming
Data Analytics Using R Programming
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Data Analytics Using R Programming course, created by DATAhill Solutions Srinivas Reddy, is an online certificate course that enables you to understand how to do different data analysis functions by making use of R Programming. The curriculum provides you with comprehensive training on all aspects of data analytics with R programming including Variables, Operators & Data types, Structures, Vector Manipulation & Sub Setting, etc.
Offered by Simpliv Learning, Data Analytics Using R Programming online course is meant for learners who have previous experience of handling vast volumes of unprocessed data at an organizational level. The proposed cohort of participants by this short programme consists of readers who use R programming widely to prepare charts, tables and professional and complex data. The online programme gives the participants the opportunity to develop a mini-project on their own to get exposure to find solutions for real-world problems using Data Analytics.
Data Analytics Using R Programming certification also provides the participants unlimited and lifetime availability of the learning materials with a one-time payment and a 20-day money-back guarantee.
The highlights
- Online course
- 20-Day Money-Back Guarantee
- Learn at your own pace
- Lifetime Access
- Certificate on Completion
- Access on Android and iOS App
Program offerings
- Certificate on completion
- Access on android and ios app
- 82 lectures
- English videos
- Certification of completion
- 68+ hours completion time
Course and certificate fees
certificate availability
Yes
certificate providing authority
Simpliv Learning
Who it is for
What you will learn
Machine learning
Data science knowledge
R programming
Knowledge of python
At the end of the Data Analytics Using R Programming online certification, the learners will be able to gain knowledge enough to pursue their careers as software professionals equipped with Data Analytics using R Programming.
The syllabus
Data Analytics using R Programming
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