Introduction to R & Python

The Introduction to R & Python training course introduces the fundamentals of both programming languages required to perform data analytics. R & Python are both programming languages and software environments commonly used in creating statistical software, data mining, and data analytics. This course will compare and contrast the benefits and limitations of both. Students will review the language fundamentals, commonly used libraries, and advanced concepts necessary to prepare data analytics. In addition, we will explore common data analytics and graphing best practices.

Course Summary

Purpose: 
Introduce developers to both programming languages providing comparisons and recommendations between the two languages.
Audience: 
Students looking to learn R and Python
Skill Level: 
Learning Style: 

Workshops are instructor-led lab-intensives focused on the practical application of technologies through the facilitation of a project-related lab. Workshops are just the opposite of Seminars. They deliver the highest level of knowledge transfer of any format. Think wide (breadth) and deep (depth).

Workshop help
Duration: 
3 Days
Productivity Objectives: 
  • Identify how R and the R environment can be leveraged to perform data analytics
  • Create simple applications and models using R & Python
  • Evaluate R & Python against other analytics strategies like SAS, SPSS, Stata, etc.

What You'll Learn

In the Introduction to R & Python training course you’ll learn:

  • What is Data Science
    • Understanding the history of data science
    • Definition of Data Science
    • Commonly used tools and technologies
    • Areas of application
    • Understanding the Data Science Process
  • Introduction to R Language
    • What is R?
    • Setting up your R and Python environments
  • R Language Fundamentals
    • Core R syntax concepts
    • Variables and Types
    • Control Structures (Loops / Conditionals)
    • Writing your first R program
  • Working with R
    • R Scalars, Vectors, Lists and Matrices
    • String and Text Manipulation
    • File IO
  • Functions
    • Introducing Functions
    • Closures
    • lapply/sapply functions
  • DataFrames
  • Introduction to Python
    • Why Python?
    • Setting up a Python Data Science Environment
  • Python Language Fundamentals
    • Core Python syntax concepts
    • Variables and Types
    • Control Structures (Loops / Conditionals)
    • Writing your first Python program
  • Numpy
    • Introduction to Python Numerics
  • Pandas
    • Series and DataFrames
    • Reading data in
    • Data manipulation
  • Working with Data
    • DataFrames and File I/O
    • Reading data from files
    • Data Preparation
    • Built-in Datasets
  • Visualization
    • Basic visualization guidance
    • Visualization in R
    • Visualization in Python
  • Statistical Modeling With R and Python
    • Statistical Functions
    • Dealing With Missing Values
    • Distributions (Binomial, Poisson, Normal)
  • Linear Regression
    • Understanding Linear Regressions
    • Linear Regressions within R
    • Linear Regressions with Python
  • Machine Learning
    • Basic Machine Learning in R
    • Basic Machine Learning in Python with SciKit-Learn
  • Big Data
    • Interacting with Big Data systems via R
    • Interacting with Big Data systems via Python

Get Custom Training Quote

We'll work with you to design a custom Introduction to R & Python training program that meets your specific needs. A 100% guaranteed plan that works for you, your team, and your budget.

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