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# Data Science using R & Python offline tutorial (Android Learning App) Temporarily FREE on Google Play (was 89p)

Posted 26th Feb

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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.
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12 Comments
That picture, it's a bit:

Edited by: "Maevoric" 27th Feb
R has really fantastic documentation that's as good as any textbook you will get. Well worth the time just getting to grips with it directly rather than with an app.
smckirdy27/02/2020 00:30

R has really fantastic documentation that's as good as any textbook you …R has really fantastic documentation that's as good as any textbook you will get. Well worth the time just getting to grips with it directly rather than with an app.

Fair point, well made but not everyone will find it convenient to carry a Linux distro around with them, this app makes learning more convenient and approachable.
Edited by: "Maevoric" 27th Feb
smckirdy27/02/2020 00:30

R has really fantastic documentation that's as good as any textbook you …R has really fantastic documentation that's as good as any textbook you will get. Well worth the time just getting to grips with it directly rather than with an app.

Agreed.

I would also really recommend that the new learner pick either R or Python and stick with that. I have used both and both are great but both are very different in usage and approach, attempting to learn both at the same time would have melted my brain, indeed when I moved to python I had to forget the R methods of doing things as they were so different
Josh.Rogan27/02/2020 06:54

Agreed.I would also really recommend that the new learner pick either R or …Agreed.I would also really recommend that the new learner pick either R or Python and stick with that. I have used both and both are great but both are very different in usage and approach, attempting to learn both at the same time would have melted my brain, indeed when I moved to python I had to forget the R methods of doing things as they were so different

As someone that’s always jumping between Python, R and Matlab for work, I 100% agree. Unless you need to, stick with one. Im surprised a beginners book is covering both, that’s intimidating for someone that will benefit from this.
Heat
Thanks addec to my library!
If you've no coding experience and want to have a quick play around with machine learning or see what it can do, check out : tensorflow.org/tut…ion and click on the colabs tab. There might be a small amount of integration needed but it's minimal and all the code is included. All you have to do is run each cell to see what it does. At the end of the tutorial the code will recognise images that you have 'trained' even if the image isn't part of your training set.

Funnily I got stuck 2 weeks ago, took a break and just moved back to this subject last night. I'm trying to get my phone to recognise images as part of a game. I've other ideas in the field so it's more proof of concept.

Thanks O/P downloaded the app.
Looks good for free, thanks OP
What's a pirate's favourite programming language?
Maevoric27/02/2020 00:48

Fair point, well made but not everyone will find it convenient to carry a …Fair point, well made but not everyone will find it convenient to carry a Linux distro around with them, this app makes learning more convenient and approachable.

R works on most platforms not just linux the manuals are all pdf and you can write code in any text editor.
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