Mr. Rajat KhandelwalPython / R Trainer
AI & ML Courses
Data science with R Programming Language - Bhopal
TechSim+'s Data Analytics with R training will help you gain expertise in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualisation, Data Mining, Regression, Sentiment Analysis and using R Studio for real life case studies on Retail, Social Media, Education, Shopping.
- Lectures 25 - 30
- Duration 30 Days
- MemberShip Yes
- Projects Yes
- Skill level Basic to Advanced
- Language English
- Assessments Yes
Over past several years R has garnered immense popularity among Data Science practitioners and it is no surprise that R language is often as referred as lingua franca of Data Science!
TechSim+'s Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc.
- Understand concepts around Business Intelligence and Business Analytics
- Apply various supervised machine learning techniques
- Learn where to use algorithms
- Use various packages in R to create fancy plots
Step - 1: Basics of R Programming for Data Science
- Why learn R ?
- How to install R / R Studio ?
- How to install R packages ?
- Basic computations in R
Step - 2: R Nuts and Bolts
- Data Types and Objects in R
- Creating Vectors
- Mixing objects
- Missing Values
- Data Frames
- Useful R Packages
Step - 3: Subsetting R Objects
- Subsetting a Vector
- Subsetting a Matrix
- Subsetting Lists
- Subsetting Nested Elements of a List
- Extracting Multiple Elements of a List
- Partial Matching
- Removing NA Values
Step - 4: Vectorized Operations
- Vectorized Matrix Operations
Step - 5: Control Structures
- For Loops
- Nested For Loops
- While Loops
- Repeat Loops
- next , break
Step - 6: Functions in R
- Your First Function
- Argument Matching
- Lazy Evaluation
- The ... Argument
- Arguments Coming After the ... Argument
Step - 7: Getting Data In and Out of R
- Reading and Writing Data
- Reading Data Files with read.table()
- Reading in Larger Datasets with read.table
Step - 8: Advanced R Programmimg
- Built-in R Features
- Math Function with R
- Regular Expressions
- Dates and Timestemps
Step - 9: Data Manipulation With R
- Managing Data Frames with the dplyr package
- Common dplyr Function Properties
- Installing the dplyr package
Step - 10: Data Visualization With R
- Overview of ggplot2
- Line plots
- Pie plots
- 2 variable Plotting
- Coordinates and Faceting
- ggplot2 Exercises
- Data Visualization Projects
- Data Visualization With Plotly
Step - 11: Statistical Inference
- What is Statistical Inference?
- Terminologies of Statistics
- Measures of Centers
- Measures of Spread
- Normal Distribution
- Binary Distribution
Step - 12: Introduction to Machine Learning With R
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Supervised Learning algorithm
- Linear Regression and Logistic Regression
- Implementing Linear Regression model in R
- Implementing Logistic Regression model in R
Step - 13: Classification Techniques
- What are classification and its use cases?
- What is K-Nearest Neighbours?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
- What is Navies Bayes?
- Support Vector Machine
- Implementing Decision Tree model in R
- Implementing Linear Random Forest in R
- Implementing Navies Bayes model in R
- Implementing Support Vector Machine in R
Step - 14: Unsupervised Learning
- What is Clustering & its use cases
- What is K-means Clustering?
- Implementing K-means Clustering in R