Data Science using R programming language 1.1 Apk Paid

The data science machine learning and artificial intelligence market are booming.

Data Science is dominated by Python and R.

R can be used for statistical and visualization purposes. It is a free, open-source language. You can use it to deal with both structured (organised), and semi-structured data (semiorganized).

We covered every aspect of R data science learning:

Introduction

Data types in R

Variables in the R

Operators in R

Conditions Statements

Loop statements

Loop Control Statements

R Script

Functions of R

Custom Function

Data structures

* Atomic vectors

* Matrix

*

* Factors

* Data frames

*List

Export/Import Data - Values to the data structure

Data Manipulation/Transformation

Use Base R function

dplyr package

We covered the following topics for Python:

Python's Environment Setup and The Essentials

* Environment and Introduction

* Variable assignment for Python

* Data types in Python

* Data Structure: Tuple

* Data Structure

* Data Structure Dictionary (Dict).

* Set Data Structure

*Basic Operator in

*Basic Operator (plus).

*Basic Operator: *(multiply).

* Functionalities

* Python's Built-in Sequence Function

* Control Flow Statements: If elif is not

* Loops Control Flow Statements

* Control Flow Statements for while Loops

* Exception Handling

NumPy Python for Mathematical Computation

* Different types of arrays

* The Attributes for ndarray

* The Basic Operation

* Accessing Array Element

* View and Copy

* Universal Functions

* Manipulation of Shape

* Broadcasting

* Linear Algebra

Pandas Data Manipulation

* Why Pandas ?

* Data Structures

*Series - Creation

*Series - Access Elements

*Series - Vectorizing operations

* DataFrame: Creation

* Viewing DataFrame

* Handling Missing Values

* Data Operations using Functions

* Data Operations: Statistical Functions

* GroupBy Data Operation

* Data Operation: Sorting

* Data Operation: Merge Duplicate Concatenation

* SQL Operation for Pandas

Statistics are essential to begin learning in this field.

Statistics terms can be confusing and difficult to comprehend for newbies. We tried to make these terms understandable for advanced or novice data science machine learning AI guys.

We covered many statistics terms like "-" here.

* Hypotheses

* Quantitative methods

* Qualitative Methods

* Dependent and independent variables

* Outcome and Predictor variables

* Categories variables

* Binary variable

* The nominal variable

* Variable ordinal

* Continuous variable

* Interval Variable

* Ratio variable

* Discrete variable

* Confounding variables

* Measuring error

* Reliability and Validity

* There are two methods to collect data

* Variations

* Variation not systematic

* Variation systematic

* Distribution of frequency

*

* Median

* Modular

* Data dispersion

* The range

*Interquartile range

* Quartiles

* Probability

* Standard deviation

The best thing about this app is that all material, except the sample project, is accessible online. We keep updating it on a regular basis.

Mobile coder online. You can create code and see the output by running it on your mobile device.

Simulator Test/Exam: This simulation test will assess your data science knowledge. Each question has 4 choices and one correct answer.