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Description
Data Science with R & Python Offline Tutorial MOD APK
The markets for data science, machine learning, and artificial intelligence are expanding rapidly.
Python and R dominate the field of data science.
R is a free, open-source language built for statistical analysis and visualization. It can handle both structured (well-organized) and semi-structured (partly organized) data.
Here is an overview of everything covered in the R data science course:
Introduction
Data Types in R
Variables in R
Operators in R
Conditional Statements
Loop Statements
Loop Control Statements
R Scripts
Built-in Functions in R
Custom Functions
Data Structures
* Atomic Vectors
* Matrices
* Arrays
* Factors
* Data Frames
* Lists
Exporting/Importing Data and Adding Values to Data Structures
Data Manipulation and Transformation
Using Base R Functions
The dplyr Package
The Python topics covered include:
Setting Up the Python Environment and the Essentials
* Environment and Introduction
* Variable Assignment in Python
* Python Data Types
* Tuple Data Structure
* Data Structures
* Dictionary (Dict) Data Structure
* Set Data Structure
*Basic Operators in
*Basic Operators (addition).
*Basic Operators: *(multiplication).
* Functionalities
* Python's Built-in Sequence Functions
* Control Flow Statements: if, elif, and is not
* Loop Control Flow Statements
* Control Flow Statements for while Loops
* Exception Handling
NumPy: Python for Mathematical Computation
* Different Types of Arrays
* Attributes of ndarray
* Basic Operations
* Accessing Array Elements
* Views and Copies
* Universal Functions
* Shape Manipulation
* Broadcasting
* Linear Algebra
Pandas: Data Manipulation
* Why Pandas?
* Data Structures
*Series – Creation
*Series – Accessing Elements
*Series – Vectorized Operations
* DataFrame: Creation
* Viewing a DataFrame
* Handling Missing Values
* Data Operations Using Functions
* Data Operations: Statistical Functions
* GroupBy Data Operation
* Data Operation: Sorting
* Data Operation: Merging, Removing Duplicates, and Concatenation
* SQL Operations for Pandas
A solid grasp of statistics is essential when you first start out in this field.
Statistical terminology can feel confusing and hard to follow for beginners. We have tried to explain these terms in a way that is clear for both advanced and novice data science, machine learning, and AI enthusiasts.
We cover a wide range of statistics terms here, such as “-”.
* Hypotheses
* Quantitative Methods
* Qualitative Methods
* Dependent and Independent Variables
* Outcome and Predictor Variables
* Categorical Variables
* Binary Variable
* Nominal Variable
* Ordinal Variable
* Continuous Variable
* Interval Variable
* Ratio Variable
* Discrete Variable
* Confounding Variables
* Measurement Error
* Reliability and Validity
* There are two methods for collecting data
* Variations
* Non-systematic variation
* Systematic variation
* Frequency distribution
*
* Median
* Modular
* Data dispersion
* The range
* Interquartile range
* Quartiles
* Probability
* Standard deviation
What makes this app stand out is that everything except the sample project can be accessed online, and the content is refreshed regularly.
A mobile coder that runs right on your phone. Write your code and view the output instantly by executing it on your mobile device.
Simulator Test/Exam: This practice test checks your data science skills. Every question offers four options, with only one correct answer.
How to install
- Download the APK file above to your phone.
- Allow “Install unknown apps” when prompted.
- Open the downloaded APK and tap Install.
- If the package includes OBB/XAPK data, follow the bundled instructions.
MOD APKs are modified builds, so Play Protect may show a warning. Install only if you trust the file; use at your own risk.