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:
Data-Types in R
Variables in R
Operators in R
Loop Control Statements
Import/Export Data Assign values to data structure
Apply function of Base R
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)
Built-in Sequence Function in Python
Control Flow Statements: if, elif, else
Control Flow Statements: for Loops
Control Flow Statements: while Loops
Mathematical Computation with NumPy in Python
Types of Arrays
Attributes of ndarray
Accessing Array Element
Copy and Views
Universal Functions (ufunc)
Data Manipulation with Pandas
Why Pandas ?
Series Access Element
Series Vectorizing operations
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 -
Independent and Dependent variables
Predictor and Outcome variables
Validity and Reliability
Two methods of data collection
Types of variation
Dispersion in distribution of Data
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.