Python & R Programming

Course Overview

In this course, you will learn how to use Python Programming and Data virtualization using R Programming to transform data into actionable insights that inform an organization’s business decisions.

Data analysis is the process of extracting information from data. Data analysis can involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics.

Courses Included are:

Python for Data Science
R Programming

Python for Data Science
Section 1: Introduction to Programming
Section 2: Control Flow
Section 3: Object Oriented Programming
Section 4: Advanced Python
Section 5: Data Science

Data Visualization using R Programming

Course Outline
Introduction to R Programming
History and overview of R
Install and configuration of R programming environment
Basic language elements and data structures
R+Knitr+Markdown+GitHub
Data input/output
Data storage formats
Subsetting objects
Vectorization
Control structures
Functions
Scoping Rules
Loop functions
Graphics and visualization
Grammar of data manipulation (dplyr and related tools)

Course Outline

1. The Context of Software Development
2. Values and Variables
3. Expressions and Arithmetic
4. Conditional Statements
5. Iteration
6. Using Functions
7. Writing Functions -1
8. Writing Functions -2
9. Objects
10. Lists
11. Tuples, Dictionaries, and Sets
12. Class Design

Advanced Python Programming

Course Objectives
• Leverage OS services
• Code graphical interfaces for applications
• Create modules
• Create and run unit tests
• Define classes
• Interact with network services
• Query databases
• Process XML data

Course Outlines
1. Python refresher
2. Operating Systems Services
3. Pythonic programming
4. Modules and packages
5. Classes
6. Metaprogramming
7. Programmer tools
8. Distributing modules
9. Database access
10. GUI programming with PyQT4
11. Network programming
12. Threads
13. XML and JSON
14. Extending Python
15. Subprocesses