Introduction to R & Python Course

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

Python

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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.

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about our training

Real-World Content

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

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

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