Data Science with R & Python Free Offline Tutorial

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
\t Atomic vectors
\t Matrix
\t Arrays
\t Factors
\t Data Frames
\t 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
\tIntroduction and Environment Setup
\tVariable assignment in Python
\tData Types in Python
\tData Structure: Tuple
\tData Structure: List
\tData Structure: Dictionary (Dict)
\tData Structure: Set
\tBasic Operator: in
\tBasic Operator: + (plus)
\tBasic Operator: * (multiply)
\tFunctions
\tBuilt-in Sequence Function in Python
\tControl Flow Statements: if, elif, else
\tControl Flow Statements: for Loops
\tControl Flow Statements: while Loops
\tException Handling
Mathematical Computation with NumPy in Python
\tTypes of Arrays
\tAttributes of ndarray
\tBasic Operations
\tAccessing Array Element
\tCopy and Views
\tUniversal Functions (ufunc)
\tShape Manipulation
\tBroadcasting
\tLinear 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.

Page navigation:

• < Prev
• Next >