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Data Science using R & Python offline tutorial for Android

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Key Details of Data Science using R & Python offline tutorial

  • Data science, Machine Learning and Artificial intelligence market is on boom.
  • Last updated on 4/22/2020
  • There have been 2 updates
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Developer's Description

Data science, Machine Learning and Artificial intelligence market is on boom.

Data science, Machine Learning and Artificial intelligence market is on boom.

Data science is basically converting structured or unstructured data in to insight, understanding and knowledge using scientific methods, processes and algorithms.

R and Python are most common programming languages used in Data Science.

R is free open source language used as statistical and visualization software. It can deal with structured (organised) and semi-structured (semi-organised) data.

To learn R for data science we covered all aspects as follows:

Introduction

Data-Types in R

Variables in R

Operators in R

Conditional Statements

Loop statements

Loop Control Statements

R Script

R Functions

Custom Function

Data Structures

Atomic vectors

Matrix

Arrays

Factors

Data Frames

List

Import/Export Data Assign values to data structure

Data Manipulation/Transformation

Apply function of Base R

dplyr Package

For Python we covered following -

Environment setup and Essentials of Python

Introduction and Environment Setup

Variable assignment in Python

Data Types in Python

Data Structure: Tuple

Data Structure: List

Data Structure: Dictionary (Dict)

Data Structure: Set

Basic Operator: in

Basic Operator: + (plus)

Basic Operator: * (multiply)

Functions

Built-in Sequence Function in Python

Control Flow Statements: if, elif, else

Control Flow Statements: for Loops

Control Flow Statements: while Loops

Exception Handling

Mathematical Computation with NumPy in Python

Types of Arrays

Attributes of ndarray

Basic Operations

Accessing Array Element

Copy and Views

Universal Functions (ufunc)

Shape Manipulation

Broadcasting

Linear Algebra

Data Manipulation with Pandas

Why Pandas ?

Data Structures

Series Creation

Series Access Element

Series Vectorizing operations

DataFrame Creation

Viewing DataFrame

Handling Missing Values

Data Operations with Functions

Statistical Functions for Data Operations

Data Operation with GroupBy

Data Operation: Sorting

Data Operation: Merge, Duplicate, Concatenation

SQL Operation in Pandas

Statistics is crucial part to start learning in in this field.

Terms used in statistics is very strange and hard to understand for beginners, so we tried our best to explain these terms in very easy language for Novice, Intermediate or Advanced level guys in Data Science, Machine Learning, AI field.

Here we covered so many terms used in statistics like -

Hypotheses

Quantitative methods

Qualitative methods

Independent and Dependent variables

Predictor and Outcome variables

Categorical variables

Binary variable

Nominal variable

Ordinal variable

Continuous variable

Interval variable

Ratio variable

Discrete variable

Confounding variables

Measurement error

Validity and Reliability

Two methods of data collection

Types of variation

Unsystematic variation

Systematic variation

Frequency distribution

Mean

Median

Mode

Dispersion in distribution of Data

Range

Interquartile range

Quartiles

Probability

Standard deviation

Most important advantage of this app that complete material except sample project is available offline, sample project part is online because we keep adding it web based regular.

Online compiler on Mobile device, you can write code on mobile and run it to see output.

Simulation Test/Exam - Check your knowledge in Data Science by attempting this simulation exam, each question have 4 options and 1 correct answer.


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