Duration: Hours

Python is a general-purpose language that is used for the deployment and development of various projects. Python has all the tools required to bring a project into the production environment. R is a statistical language used for the analysis and visual representation of data.

Training Mode: Online


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    Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn’t specialized for any specific problems. R is an open-source statistical programming language and framework that’s used for a wide range of scientific applications, including machine learning. R is a popular skill requirement for job openings in artificial intelligence and data science.In the real world of data science, Python and R users intersect a lot. So which ever industry or discipline you are interested in you are likely to run into projects done in both languages. To appreciate it all you need to have at least a basic understanding of both R and Python.

    1. Introduction to Python

    a). Installation

    b). Packages and Installing Packages,

    2. Basic Operations

    a). Programming Language Basics

    b). Numbers, Strings Lists, Dictionaries, Tuples Files

    c). Exercise/Case Study

    3. Data Manipulation to python

    a). Conditional Processing

    b). Loops, Iterations, and other iterative processing

    c). Exercise/Case Study

    4. Data manipulation

    a). Functions, arguments, and modules

    b). Transforming Variables, Exercise/Case Study

    5. Overview of Packages/Libraries

    a). Popular Packages/Libraries

    b). Overview of Python application in analytics industry

    1. Introduction to R

    a). Installation of R-Studio

    b). Packages in R, Installing Packages

    c). Setting Directories

    2. Basic Operations 

    a). Programming Language Basics

    b). Scalars, Vectors, Simple Calculations Data Structure

    c). Data Frames, Exercise/Case Study

    3. Data Manipulation in R

    a). Data Acquisition (Import & Export)

    b). Sub-setting observations, Subsetting variables

    c). Transforming Variables, Renaming and Recoding Variables, Exercise/Case Study

    4. Data Manipulation

    a). Conditional Processing

    b). Missing Values, Merging and Concatenating Datasets

    c). Exercise/Case Study


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