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Introduction to R & Python

Course Summary

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 begins by comparing and contrasting the benefits and limitations of both R and Python. Students will then review the language fundamentals, commonly used libraries, and advanced concepts necessary to prepare data analytics. The course concludes by exploring common data analytics and graphing best practices.

Purpose
 Learn both R and Python programming languages by providing comparisons and recommendations between both.
Audience
 Students looking to learn R and Python.
Role
Data Engineer - Data Scientist - Software Developer - Technical Manager
Skill Level
Introduction
Style
Workshops
Duration
3 Days
Related Technologies
R | Python

 

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
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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