Data Science with R & Python F

R, Python and statistics tutorial for data science, machine learning and AI

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 free open source programming languages used for statistical, mathematical, data wrangling, exploration and visualization in data science. 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

• The Mean

• The Median

• The 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.