R Programming Bangladesh

Training Track-1: R Programming Basics has officially started! 🚀 From 14th July 2024, we're diving deep into the world of data science and programming. Organized by SRCBD, this course is set to boost your programming skills and knowledge. Stay tuned for updates and join us on this learning journey! 📚✨

10 FREE BOOKS FOR MACHINE LEARNING & DATA SCIENCE 📚

1️⃣ Natural Language Processing with Python
📌 Analyzing Text with the Natural Language Toolkit.
🔗 https://nltk.org/book_1ed/

2️⃣ Think Stats
📌 This book covers many of the core statistical concepts for data science including data analysis, distributions, and probability.
🔗 https://greenteapress.com/wp/think-stats-2e/

3️⃣ Bayesian Methods for Hackers
📌 This book bridges the gap between theoretical Bayesian machine learning methods.
🔗 https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

4️⃣ R for Data Science
📌 This book is one of the best introductions to learning R for data science.
🔗 https://r4ds.had.co.nz

5️⃣ Machine Learning Yearning
📌 This book contains practical steps and frameworks for successful machine learning projects by Andrew Ng.
🔗 https://github.com/ajaymache/machine-learning-yearning

6️⃣ Hands-on Machine Learning with Scikit-learn and Tensorflow
📌 This book gives a very good overview of the machine learning process with Scikit-learn and Tensorflow. 🔗https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/

7️⃣ Forecasting: Principles and Practice
📌 This book offers a very comprehensive overview of methods used for forecasting.
🔗 https://otexts.com/fpp2/index.html

8️⃣ Deep learning
📌 This book is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
🔗 https://deeplearningbook.org

9️⃣ Linear Algebra
📌 This book covers the foundational concepts that would usually be covered in a typical undergraduate course.
🔗 https://joshua.smcvt.edu/linearalgebra/

🔟 Introduction to Machine Learning with Python
📌 This book focuses on the practical application of machine learning techniques rather than covering the math behind the field.
🔗 https://drive.google.com/file/d/10Vrml277NCOa6SS9GV10m847jtPynt_n/view

Credit: learnmachinelearning

R Programming Bangladesh To develop R Programming for Data science & Big data field in Bangladesh.

R প্রোগ্রামিং দিয়ে পাসওয়ার্ড বানাই-Adil Mahmud
------------------------------
কত শত কাজে এখন পাসওয়ার্ড লাগে। থাকে কত শর্ত। পাসওয়ার্ড হতে হবে শক্ত। এতে কোনো সন্দেহ নেই। R জানলে এ কাজটাও করা যায় সহজেই। যদিও কাজটা R দিয়ে করতে হবে এমন কোনো কথা নেই। আরও কত উপায় তো আছেই। তাও একটি কোডিং করেই দেখি না।

আমাদের পাসওয়ার্ডে থাকবে-
১। 0 থেকে 9 পর্যন্ত ডিজিটগুলো
২। ইংরেজি বড় ও ছোট হাতের অক্ষর
৩। বিশেষ কিছু ক্যারেক্টার (যেমন @, #, $ ইত্যাদি)

পাসওয়ার্ড বানানো যাবে যে-কোনো দৈর্ঘ্যের। প্রথমেই আমরা ওপরের সবগুলো ধরন থেকে একটি করে ক্যারেক্টার নেবো। তাহলে পেলাম চারটি ক্যারেক্টার। পাসওয়ার্ড চার ক্যারেক্টারের বেশি হলে আরও কিছু ক্যারেক্টার লাগবে। সেগুলো আমরা র‍্যান্ডমলি নেবো সব ধরনের ক্যারেক্টার থেকেই। এজন্য আগেই সবাইকে একত্র করে একটি ভেক্টর বানিয়ে রাখব।

gen_pw

𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒘𝒊𝒕𝒉 𝑹 𝒊𝒔 𝑭𝑹𝑬𝑬*

𝐎𝐧𝐥𝐢𝐧𝐞 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐨𝐫-𝐥𝐞𝐝 𝐒𝐤𝐢𝐥𝐥 𝐓𝐫𝐚𝐜𝐤 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬 𝐨𝐧 “𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐑 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞”

Community group: https://www.facebook.com/groups/962305230790563

Statistical Research Consultants Bangladesh (SRCBD) is offering 6 different Online Instructor-led skill track hands-on training sessions to learn R programming step-by-step starting from 14th July 2024. The title of our skill track is "Data Analytics with R for Data Science.". These 6 R programming course tracks are:

Training Track-1: R Programming Basics
Training Track-2: Importing and Cleaning Data in R
Training Track-3: Data Manipulation with R
Training Track-4: Data Visualization with R
Training Track-5: Statistics Fundamentals with R
Training Track-6: Machine Learning with R

Course Syllabus: https://shorturl.at/mlrg3

📣📣 Offer on Course fee:
If anyone joins all training tracks from Training Track-1 to Training Track-5, then participants will get Training Track-6: Machine Learning with R for free.

R Registration Link: https://forms.gle/egEzcRpa94HJ7AWL9

Image Credit: Data Flair

Online Instructor-led Skill Track Trainings on “Data Analytics with R for Data Science” Statistical Research Consultants Bangladesh (SRCBD) is a research-based consultancy firm from Dhaka, Bangladesh.

Online Instructor-led Skill Track Program
4-Month 6 R-programming Course Track

Anyone interested?

Check the comment below........

R is a powerful and versatile programming language widely used for statistical computing and data analysis. It provides a comprehensive set of tools for data manipulation, visualization, and statistical modeling, making it a valuable tool for researchers and data analysts across various fields. R's extensive collection of user-contributed packages further enhances its capabilities, enabling users to tackle a wide range of specialized tasks. With its strong emphasis on data exploration and visualization, R enables users to gain deep insights from their data, making it an essential tool in modern data science.

Sure, here are some statistical examples with R code: All of you can Practice it who are beginners

Example 1: Calculating descriptive statistics

Code snippet
# Create a dataset
data = 30])

Example 4: Fitting a linear regression model

# Fit a linear regression model to predict height based on age
model

R is a statistical computing and graphics language and environment. R can be seen as a separate implementation of S. There are some essential variations, but a lot of code written for S runs unaltered under R.

One of R’s strengths is the ease with which well-designed publication-quality plots, including mathematical symbols and formulas, can be generated where appropriate. The defaults for minor graphic design choices have been taken great care, but the user retains full control.

R is licenced as free software under the terms of the GNU General Public License of the Free Software Foundation in the source code form. It compiles and runs on a wide range of UNIX platforms and related systems (including FreeBSD and Linux), Windows and MacOS.

Pre-requisites
We believe that readers have a statistical history. This book is not a statistical book, but a book on how to apply statistical methods using R. We try to use the words already described on Wikipedia in such a way that people can refer to the corresponding wikipedia page any time they have doubts about the concept.

We also believe that readers are familiar with computers and that they know how to use command-line interface applications. There are some graphical user interfaces for R, but we’re not going to explain how to use them in this textbook. For the first session with R, beginners can have a look at the Sample Session. They may also take a look at the statistical analysis: an introduction using the R book.

Climate of the R
R is an integrated suite of software tools for data manipulation, calculation and graphic display. Includes

Efficient data handling and storage facility

A suite of operators for array calculations, in particular arrays,
A broad, coherent, integrated set of intermediate resources for data analysis;

Graphical data analysis and view on-screen or hardcopy, and
A well-developed, simple and efficient programming language that includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term “environment” is intended to describe it as a completely planned and cohesive framework, rather than a gradual accretion of very basic and rigid tools, as is often the case with other data analysis software.

R, like S, is built around a true programming language and allows users to add additional functionality by defining new features. Most of the system itself is written in the R dialect of S, which makes it simple for users to follow the algorithmic choices made. In the case of computer-intensive activities, C, C++ and Fortran code can be connected and named at run time. Advanced users can write C code to directly control R objects.
A lot of users think of R as a mathematical method. We like to think of it as a setting in which statistical methods are applied. R can be expanded (easily) by packages. There are about eight R-distributed packages and several more are available via the CRAN family of websites covering a very wide variety of modern statistics.

R has its own LaTeX-like paper format, which is used to provide detailed documentation, both online in a variety of formats and in hardcopy.

R Benefits and Drawbacks

R is the most general programming language for statistical modelling and analysis. Like other programming languages, R also has a range of advantages and disadvantages. It’s a language that is continually changing, which means that many issues can eventually fade away with future changes to R.

There are pros and cons to R.

Pros's

1)Open Source Open
Open-source language is a language that we can use without the need for a licence or subscription. R is an open source language. We will contribute to the growth of R by improving our packages, designing new packages and resolving issues.

2) Independent forum
R is a platform-independent language or cross-platform programming language, which means that its code can run on all operating systems. R allows programmers to develop applications for a variety of competing platforms by writing a programme only once. R is very easy to run on Windows, Linux, and Mac.

3) Machine learning operation
R helps one to perform various machine learning operations, such as classification and regression. For this function, R provides various packages and features for the creation of an artificial neural network. R is used by the world’s best data scientists.

4) Example support for data wrangling
R helps us to indulge in data wrangling. R offers packages such as dplyr, a reader capable of translating messy data to a standardised form.

5) Performance plotting and graphics
R simplifies the efficiency of plotting and graphing. R libraries such as ggplot2 and plots advocate visually pleasing and aesthetic graphics that set R apart from other programming languages.

6) The number of packages
R has a very rich collection of packages. R has more than 10,000 packages in the CRAN repository that are continuously increasing. R offers data science and machine learning packages.

7) Statistics
R is mainly regarded as the statistical language. This is the key reason why R is predominant over other programming languages for the development of statistical tools.

8) Continuous development
R is a programming language that is continually changing. Constantly changing means that as something grows, improves or develops over time, like our taste for music and clothing that evolves as we get older. R is a state of the art that offers updates whenever a new function is introduced.

The Cons

1) Handling of data
Objects are stored in the physical memory of R. This is in contrast to other programming languages such as Python. R uses more memory than Python does. It needs all the data at a single location in the memory. It’s not the best solution when dealing with Big Data.

(2) Basic Protection
R is missing the basic protection. This is an integral aspect of most programming languages such as Python. Because of this, there are a number of limitations with R since it cannot be inserted in a web application.

3) Complicated languages
R is a very complex language and has a steep learning curve. People who do not have prior knowledge or programming experience may find it difficult to learn R.

4) The weakness of origin
The key downside of R is that it does not support dynamic or 3D graphics. The explanation for this is its origin. It shares its roots with the much older “S.” programming language.

(5) Less Pace
The programming language of R is much slower than other programming languages such as MATLAB and Python. Compared to other programming languages, R packages are much slower.
In R, algorithms are distributed through a range of packages. It may be difficult for programmers who have no previous knowledge of packages to implement algorithms.

R is a free software application designed for statistical computation. There is already a lot of documentation for standard R packages on the Comprehensive R Archive Network (CRAN) and a lot of information for specialised books, forums such as Stackoverflow and personal blogs, but all of these resources are dispersed and often difficult to locate and compare. The purpose of this Wikibook is to be a place where anyone can share their knowledge and tricks on R. It is meant to be organised by task, but not by disciplineR is free software designed for statistical computing. There is already a lot of documentation for standard R packages on the

Comprehensive R Archive Network (CRAN) and a lot of information for specialised books, forums such as Stackoverflow and personal blogs, but all of these resources are dispersed and often difficult to locate and compare. The purpose of this Wikibook is to be a place where anyone can share their knowledge and tricks on R.

Data Scientist (R Proficient)

Application Deadline: April 30, 2023 (Early applications will be given preference)

Job Location: Gulshan-1, Dhaka (Currently work from home)

Details: https://www.relisource.com/job-post/data-scientist-r-proficient/

ডাটা সায়েন্সে R প্রোগ্রামিংয়ের এর জনপ্রিয় ৫ টি লাইব্রেরিঃ আবু তারেক রনি

ডেটা সায়েন্স সম্পর্কে কথা বলার সময়, R সম্পর্কে কথা না বলা অসম্ভব। আসলে, এটা বলা যেতে পারে যে R হল ডেটা সায়েন্সের জন্য অন্যতম সেরা প্রোগ্রামিং ল্যাংগুয়েজ কারণ এটি ডাটা সায়েন্টিস্ট এবং পরিসংখ্যানবিদদের জন্য পরিসংখ্যানবিদদের দ্বারা তৈরি করা হয়েছিল! বর্তমানে অনেক অত্যাধুনিক R লাইব্রেরির আছে যা খুব জনপ্রিয় (পাইথনের সাথে কঠোর প্রতিযোগিতা থাকা সত্ত্বেও!)

প্রকৃতপক্ষে, এমন অনেকগুলি R লাইব্রেরি রয়েছে যেগুলিতে ডেটা মেনেজিং এবং এনালাইসিস করার জন্য অনেকগুলি ফাংশন, টুলস এবং মেথড রয়েছে। এই লাইব্রেরির প্রতিটিরই একটি বিশেষ বিশেষ কাজ রয়েছে এবং কিছু কিছু লাইব্রেরির সাথে ছবি এবং টেক্সট ডেটা, ডেটা ম্যানিপুলেশন, ডেটা ভিজ্যুয়ালাইজেশন, ওয়েব স্ক্রাপিং, মেশিন লার্নিং ইত্যাদি পরিচালনা করার ব্যবস্থা আছে।

এখন আমরা ডেটা সায়েন্সের জন্য সেরা ৫ টি R লাইব্রেরি নিয়ে আলোচনা করবঃ

• dplyr: dplyr প্যাকেজটি মুলত ডাটা ম্যনুপুলেশনের কাজে ব্যাবহার হয়ে থাকে। এটার ৫টি কমন ফাংশন আছে। সেগুলো হল group_by(), mutate(), filter(), summarise(), arrange()। প্রাথমিক অবস্থায় install.packages(“dplyr”) এর মাধ্যমে প্যাকেজটি ইন্সটল করা হয়ে থাকে।

• ggplot2: ggplot2 হল একটি ডাটা ভিসুয়ালাইজেশন লাইব্রেরী যা গ্রামার অফ গ্রাফিক্সের উপর বেস করে বানানো। Bar charts, pie charts, histograms, scatterplots, error charts, etc. এই চার্ট গুলো ggplot2 এর সাহায্যে খুব ভাল ভাবে ড্র করা যায়।

• Rshiny: Rshiny হল একটি R প্যাকেজ যা R-এ ইন্টারেক্টিভ ওয়েব অ্যাপ্লিকেশন তৈরি করতে ব্যবহার করা যেতে পারে। মূলত, Rshiny হল R এবং আধুনিক ওয়েবের মধ্যে একটি সমন্বয় এবং আপনি কোন বিশেষ ওয়েব ডেভেলপমেন্ট দক্ষতার প্রয়োজন ছাড়াই Rshiny ব্যবহার করে সহজেই ওয়েব অ্যাপ্লিকেশন তৈরি করতে পারেন। এছাড়া ও ওয়েবপেজে স্বতন্ত্র অ্যাপ্লিকেশন তৈরি করতে পারেন, এমনকি ওয়েব ভিজ্যুয়ালাইজেশন ড্যাশবোর্ড তৈরি করতে পারেন।

• knitr: knitr হল ডায়নামিক রিপোর্ট তৈরির জন্য একটি R প্যাকেজ যা R কোডের মধ্যে বিভিন্ন ধরনের কোড একীভূত করতে ব্যবহার করা হয় যেমন; Markdown, Word, Pdf, LyX, LaTeX, AsciiDoc, HTML ইত্যাদি।

• Plotly: Plotly হল R এর জনপ্রিয় ভিসুয়ালাইজেশন লাইব্রেরী। Scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts সহ ৪০ এর ও বেশী ইউনিক চার্ট বানানো যায় plotly ইউজ করে।

এছাড়া ও R এ আছে ২৪০০+ পরিসংখ্যানের লাইব্রেরি।

Written by Abu Tareq Rony

𝗢𝗻𝗹𝗶𝗻𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗼𝗿-𝗹𝗲𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗻 “𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗥 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲”

Statistical Research Consultants Bangladesh (SRCBD) is offering 08-weeks long Online Instructor-led training on "Data Analytics with R for Data Science" starting from 𝟮𝗻𝗱 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿 2022.

📣📣 𝗘𝗮𝗿𝗹𝘆 𝗯𝗶𝗿𝗱 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Anyone who completes registration and payment by 𝟯𝗿𝗱 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿 gets 𝟭𝟱% 𝗼𝗳𝗳.

Course Syllabus and details link: https://srcbd.org/course/online-instructor-led-training-on-data-analytics-with-r

R Registration Link: https://forms.gle/6Ty8JzgK215aDyDZA