Data Science using R programming language MOD APK 1.1

Description

Data Science using R & Python offline tutorial Mod Apk

The fields of artificial intelligence, machine learning, and data science are currently seeing massive growth.

Python and R are the primary languages driving the data science industry.

R is a popular open-source, free programming language ideal for statistical analysis and data visualization, capable of handling both semi-structured and structured datasets.

Our comprehensive R data science curriculum covers:

Introduction

Data types in R

Variables in R

Operators in R

Conditional Statements

Looping statements

Loop Control Statements

R Scripting

Functions in R

Custom Functions

Data structures

* Atomic vectors

* Matrix

* Arrays

* Factors

* Data frames

* List

Importing and Exporting Data – Values into data structures

Data Transformation and Manipulation

Using Base R functions

The dplyr package

Our Python curriculum includes the following:

Python Setup and Core Fundamentals

* Introduction and Environment configuration

* Variable assignment in Python

* Core Data Types in Python

* Tuple Data Structure Overview

* Essential Data Structures

* Working with Dictionaries (Dict)

* Set Collection and Operations

* Core Built-in Operators

* Addition Operator (+) Usage

* Multiplication Operator (*) Usage

* Key Capabilities and Features

* Native Sequence Functions in Python

* Conditional Logic: If, Elif, and Else Statements

* Iteration and Loop Control Structures

* Looping Mechanics: For and While Loops

* Robust Error and Exception Handling

NumPy for High-Performance Mathematical Computing

* Common Array Varieties and Types

* Key Properties of ndarray Objects

* Essential Array Operations

* Indexing and Accessing Array Items

* Memory Views vs Array Copies

* High-Speed Universal Functions (ufunc)

* Reshaping and Array Layout Manipulation

* Array Broadcasting Mechanics

* Fundamental Linear Algebra Operations

Data Analysis and Manipulation with Pandas

* Key Benefits of Using Pandas

* Core Pandas Data Structures

* Initializing and Creating Series

* Accessing and Indexing Series Data

* Fast Vectorized Calculations on Series

* DataFrame: How to Create

* Inspecting DataFrames

* Managing Missing Data

* Function-Based Data Processing

* Statistical Operations on Data

* GroupBy Aggregations

* Organizing and Sorting Data

* Merging, Deduplicating, and Concatenating Data

* Running SQL Queries with Pandas

A solid grasp of statistics is crucial before diving into this discipline.

Statistical jargon can often feel overwhelming for beginners. We have simplified these core concepts so that learners at any stage—whether entering data science, ML, or artificial intelligence—can follow along effortlessly.

Key statistical principles, including "-", are thoroughly broken down below.

* Formulating Hypotheses

* Quantitative Research Techniques

* Qualitative Research Approaches

* Independent vs. Dependent Variables

* Predictor and Outcome Variables

* Categorical Variables

* Binary Variables

* Nominal Variables

* Ordinal Variables

* Continuous Variables

* Interval Variables

* Ratio Variables

* Discrete Variables

* Confounding Factors

* Measurement Error

* Assessing Reliability and Validity

* Primary Approaches to Data Collection

* Data Variation and Spread

* Unsystematic Variation

* Systematic Variation

* Frequency Distributions

*

* Median Values

* Mode and Modularity

* Measures of Data Dispersion

* Statistical Range

* Interquartile Range (IQR)

* Quartile Analysis

* Principles of Probability

* Standard Deviation

One of the standout features of this application is full online access to all study materials (excluding sample projects), complete with routine content updates.

Online Mobile Code Editor: Write, compile, and instantly review execution output right from your Android device.

Practice Exam Simulator: Test and sharpen your data science expertise with realistic practice questions featuring four options and a single correct solution.