R Programming Statistical Analysis
Unlocking the Power of R Programming for Statistical Analysis
At AdviTech IT Services, we offer a comprehensive R Programming for Statistical Analysis course designed to equip you with the skills to analyze and interpret complex data using R. Our expert instructors guide you through core statistical concepts, data visualization techniques, and advanced analytical methods using R. Through hands-on projects, case studies, and real-world data sets, you will gain practical experience in applying statistical analysis to solve business problems. With flexible learning options and personalized support, AdviTech IT Services ensures you master R programming and statistical techniques for a successful career in data science and analytics.
Why Choose Us?
- Expert Instructors: Learn from industry professionals with deep expertise in R programming and statistical analysis techniques.
- Comprehensive Curriculum: Master the essential statistical methods, data visualization, and data analysis techniques using R.
- Hands-On Experience: Work with real-world data sets and practical exercises to apply statistical analysis concepts effectively.
- Flexible Learning Options: Choose from various schedules, including weekend and evening classes, to fit your lifestyle.
- Data Science Focus: Learn how R programming is used in data science, enabling you to analyze, model, and interpret data efficiently.
- Career Assistance: Receive placement support to connect with top organizations looking for R programming and statistical analysis skills.
- State-of-the-Art Tools: Access advanced tools and software for data analysis to enhance your learning experience.
- Certification: Earn a recognized certification to validate your skills in R programming and statistical analysis.
- Global Alumni Network: Join a community of successful data scientists and analysts who have benefited from training at AdviTech IT Services.
Course Content
R Programming Statistical Analysis
1. Probability & Statistics
- Introduction to Statistics – Descriptive Statistics, Summary Statistics Basic probability theory
- Statistical Concepts ( uni-variate and bi-variate sampling, distributions, re-sampling, statistical Inference, prediction error)
- Probability Distribution (Continuous and discrete-Normal, Bernoulli, Binomial, Negative Binomial, Geometric and Poisson distribution)
- Bayes’ Theorem
- Central Limit Theorem
- Data Exploration & preparation Concepts of Correlation
- Regression
- Covariance
- Outliers etc.
2. R Programming
- Introduction & Installation of R
- R Basics
- Finding Help
- Code Editors for R
- Command Packages
- Manipulating and Processing Data in R
- Reading and Getting Data into R
- Exporting Data from R
- Data Objects- Data Types & Data Structure
- Viewing Named Objects
- Structure of Data Items
- Manipulating and Processing Data in R ( Creating, Accessing, Sorting Data Frames, Extracting, Combining, Merging, Reshaping Data Frames)
- Control Structures, Functions in R (numeric, character, statistical)
- Working with objects
- Viewing Objects within Objects
- Constructing Data Objects
- Building R Packages
- Running and Manipulating Packages
- Non Parametric Tests – ANOVA
- Chi – Square
- T – Test
- U – Test
- Introduction to Graphical Analysis
- Using Plots (Box Plots Scatter Plot, Pie Charts, Bar Charts, Line Chart)
- Plotting variables
- Designing Special Plots
- Simple Liner Regression
- Multiple Regression
