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Statistical Application Development with R and Python

  • Course Code: Artificial Intelligence - Statistical Application Development with R and Python
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending Software Implementation Illustrated with R and Python

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending Software Implementation Illustrated with R and Python 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This course explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this course you will be able to apply your statistical learning in major domains at work or in your projects. 

Working in a hands-on learning environment, led by our R and Python instructor, students will learn about and explore: 

  • Learn the nature of data through software which takes the preliminary concepts right away using R and Python. 
  • Understand data modeling and visualization to perform efficient statistical analysis with this guide. 
  • Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Learn the nature of data through software with preliminary concepts right away in R 
  • Read data from various sources and export the R output to other software 
  • Perform effective data visualization with the nature of variables and rich alternative options 
  • Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference 
  • Learn statistical inference through simulation combining the classical inference and modern computational power 
  • Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics 
  • Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to do a Software Implementation Illustrated with R and Python 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Data Characteristics 
  • Data Characteristics 
  • Questionnaire and its components 
  • Experiments with uncertainty in computer science 
  • Installing and setting up R 
  • Using R packages 
  • Python installation and setup 
  • IDEs for R and Python 
  • The companion code bundle 
  • Discrete distributions 
  • Continuous distributions 
  1. Import/Export Data 
  • Import/Export Data 
  • Packages and settings – R and Python 
  • Understanding data.frame and other formats 
  • Using utils and the foreign packages 
  • Exporting data/graphs 
  1. Data Visualization 
  • Data Visualization 
  • Packages and settings – R and Python 
  • Visualization techniques for categorical data 
  • Visualization techniques for continuous variable data 
  • Pareto chart 
  • A brief peek at ggplot2 
  1. Exploratory Analysis 
  • Exploratory Analysis 
  • Packages and settings – R and Python 
  • Essential summary statistics 
  • Techniques for exploratory analysis 
  1. Statistical Inference 
  • Statistical Inference 
  • Packages and settings – R and Python 
  • Maximum likelihood estimator 
  • Confidence intervals 
  • Hypothesis testing 
  1. Linear Regression Analysis 
  • Linear Regression Analysis 
  • Packages and settings – R and Python 
  • The essence of regression 
  • The simple linear regression model 
  • Multiple linear regression model 
  • Regression diagnostics 
  • Model selection 
  1. Logistic Regression Model 
  • Logistic Regression Model 
  • Packages and settings – R and Python 
  • Model validation and diagnostics 
  • Logistic regression for the German credit screening dataset 
  1. Regression Models with Regularization 
  • Regression Models with Regularization 
  • Packages and settings – R and Python 
  • Regression spline 
  • Ridge regression for linear models 
  1. Classification and Regression Trees 
  • Classification and Regression Trees 
  • Packages and settings – R and Python 
  • Splitting the data 
  1. CART and Beyond 
  • CART and Beyond 
  • Packages and settings – R and Python 
  • Understanding bagging 
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