And the Best Programming Language for Data Science goes to…
The reason for using an ellipsis in the title is that we have always looked at the wrong reasons for choosing a language. There are a bunch of factors that lead to the choice of a certain language. And with Data Science projects flooding the market, the question is NOT “which is the best language” but which one suits your project requirements and environment(work setting).
Most commonly used programming languages for Data Science
Python and R are the most widely used languages among others( for example, Java, Scala, Matlab) for statistical analysis or machine learning-centric projects.
Both of these are state-of-the-art open-source programming languages with great community support. You keep learning about new libraries and tools achieving newer levels of performance and complexity.
Python
Python is well-known for its easy to learn and readable syntax. With a general-purpose(jack of all trades) language like Python, you can build complete scie
The reason for using an ellipsis in the title is that we have always looked at the wrong reasons for choosing a language. There are a bunch of factors that lead to the choice of a certain language. And with Data Science projects flooding the market, the question is NOT “which is the best language” but which one suits your project requirements and environment(work setting).
Most commonly used programming languages for Data Science
Python and R are the most widely used languages among others( for example, Java, Scala, Matlab) for statistical analysis or machine learning-centric projects.
Both of these are state-of-the-art open-source programming languages with great community support. You keep learning about new libraries and tools achieving newer levels of performance and complexity.
Python
Python is well-known for its easy to learn and readable syntax. With a general-purpose(jack of all trades) language like Python, you can build complete scie
....continued ....
Python is well-known for its easy to learn and readable syntax. With a general-purpose(jack of all trades) language like Python, you can build complete scientific ecosystems without worrying much about the compatibility or interfacing issues.
Python codes have low maintenance cost and they are arguably more robust. From data wrangling to feature selection, web scraping, and deployment of our machine learning models, python can get almost everything done with integration support from all the major ML and deep learning APIs like Theano, Tensorflow, and PyTorch.
R was developed by academicians and statisticians over two decades ago. R today enables many statisticians, analysts, and developers to carry out their analysis. We have over 12000 packages available in CRAN (open-source repository).
Since it was developed keeping statisticians in mind, R becomes the first choice for all the core-scientific and statistical analysis. We have a package in R for almost every kind of analysis there is. Data analysis has been made very with tools like RStudio which allows you to communicate your results with concise and elegant reports.
4 Questions to learn about the BEST suited language for your project!
So, how does one make the right choice for their work at hand?
Try answering these 4 questions:
1. Which language/framework is preferred in your organisation/industry?
Depending on the industry you are working in and the most commonly used language by your peers and competitors, you might want to speak the same language. Here is an analysis carried out by David Robinson(Data Scientist), it’s a reflection of the popularity of R in an industry and you can see that R is outstandingly being used in Academia and Healthcare.
So, if you’re someone who wants to go into research, academia or bioinformatics, you might consider R over Python.
The other side of this coin is software industries, application-driven organizations, and product-based companies. You might have to go hand-in-hand with the tech stack of your organization’s infrastructure or the language that your colleagues/teams are using.
And most organizations/industries have their infrastructure based on Python including academia as well:
For an aspiring data scientist, it is a clear choice to learn something which has manifold applications and which could increase their chances of getting a job.
2. What is the scope of your project?
This is an important question because before you pick up a language, you must have an agenda for your project, the extent to which you want to work over it.
R: For example, if you want to simply solve a statistical problem through a dataset, perform some multi-variate analyses, and prepare a report or a dashboard explaining the insights, R might turn out to be a better choice because of its powerful visualization and communication libraries.
Python: On the other hand, if the aim is to first carry out exploratory analysis, develop a deep learning model and then deploy the model within a web application, Python’s web frameworks, and support from all the major cloud providers make it a clear winner.
3. How experienced are you in the field of data science?
For a beginner in data science who has limited familiarity with statistics and mathematical concepts, Python might turn out to be a better choice because it lets you code the fragments of an algorithm with ease.
With libraries like NumPy, you can manipulate matrices and code algorithms yourself. As a novice, it is always better to learn to build things from scratch rather than hopping onto using machine learning libraries.
Whereas if you already know the fundamentals of machine learning algorithms, you can pick up either of the languages to get started with.
4. How much time do you have at hand/cost of learning?
The amount of time you can invest makes another case for your choice. Depending on your experience with programming and the delivery time of your project, you might choose one language over another to get started in the field.
Python is well-known for its easy to learn and readable syntax. With a general-purpose(jack of all trades) language like Python, you can build complete scientific ecosystems without worrying much about the compatibility or interfacing issues.
Python codes have low maintenance cost and they are arguably more robust. From data wrangling to feature selection, web scraping, and deployment of our machine learning models, python can get almost everything done with integration support from all the major ML and deep learning APIs like Theano, Tensorflow, and PyTorch.
R was developed by academicians and statisticians over two decades ago. R today enables many statisticians, analysts, and developers to carry out their analysis. We have over 12000 packages available in CRAN (open-source repository).
Since it was developed keeping statisticians in mind, R becomes the first choice for all the core-scientific and statistical analysis. We have a package in R for almost every kind of analysis there is. Data analysis has been made very with tools like RStudio which allows you to communicate your results with concise and elegant reports.
4 Questions to learn about the BEST suited language for your project!
So, how does one make the right choice for their work at hand?
Try answering these 4 questions:
1. Which language/framework is preferred in your organisation/industry?
Depending on the industry you are working in and the most commonly used language by your peers and competitors, you might want to speak the same language. Here is an analysis carried out by David Robinson(Data Scientist), it’s a reflection of the popularity of R in an industry and you can see that R is outstandingly being used in Academia and Healthcare.
So, if you’re someone who wants to go into research, academia or bioinformatics, you might consider R over Python.
The other side of this coin is software industries, application-driven organizations, and product-based companies. You might have to go hand-in-hand with the tech stack of your organization’s infrastructure or the language that your colleagues/teams are using.
And most organizations/industries have their infrastructure based on Python including academia as well:
For an aspiring data scientist, it is a clear choice to learn something which has manifold applications and which could increase their chances of getting a job.
2. What is the scope of your project?
This is an important question because before you pick up a language, you must have an agenda for your project, the extent to which you want to work over it.
R: For example, if you want to simply solve a statistical problem through a dataset, perform some multi-variate analyses, and prepare a report or a dashboard explaining the insights, R might turn out to be a better choice because of its powerful visualization and communication libraries.
Python: On the other hand, if the aim is to first carry out exploratory analysis, develop a deep learning model and then deploy the model within a web application, Python’s web frameworks, and support from all the major cloud providers make it a clear winner.
3. How experienced are you in the field of data science?
For a beginner in data science who has limited familiarity with statistics and mathematical concepts, Python might turn out to be a better choice because it lets you code the fragments of an algorithm with ease.
With libraries like NumPy, you can manipulate matrices and code algorithms yourself. As a novice, it is always better to learn to build things from scratch rather than hopping onto using machine learning libraries.
Whereas if you already know the fundamentals of machine learning algorithms, you can pick up either of the languages to get started with.
4. How much time do you have at hand/cost of learning?
The amount of time you can invest makes another case for your choice. Depending on your experience with programming and the delivery time of your project, you might choose one language over another to get started in the field.
If there is a high-priority project and you don’t know either of the languages, R might be an easier option for you to get started as you need limited/no experience with programming. You can write statistical models with a few lines of code using existing libraries.
Python(a programmer’s choice) is a great option to start off with if you have some bandwidth to explore the libraries and learn about methods of exploring datasets which in case of R can be done quickly within Rstudio.
Python(a programmer’s choice) is a great option to start off with if you have some bandwidth to explore the libraries and learn about methods of exploring datasets which in case of R can be done quickly within Rstudio.
✅Dear Python learners, I would like to say thank you for joining to our channel to learn something new. Our channel needs more subscribers. Please invite your friends by sending @epythonlab . Thank you for your contribution🙏
#Question_by @Spicysuri
What is syntax?
The syntax of the Python programming language is the set of rules that defines how a Python program will be written and interpreted. The Python language has many similarities to Perl, C, and Java. However, there are some definite differences between the languages.
You can ask @pythonethbot
What is syntax?
The syntax of the Python programming language is the set of rules that defines how a Python program will be written and interpreted. The Python language has many similarities to Perl, C, and Java. However, there are some definite differences between the languages.
You can ask @pythonethbot
Epython Lab pinned «I want know your interest? I want to prepare a short video which teaches you a basic of python coding to advanced level.»
Why Use Machine Learning?
The year is 2049…
New York is overrun by bots and web crawlers. The capabilities of Machine Learning have reached new heights and the world as we know it will never be the same.
Facial recognition technology that helps users tag and share photos of friends can now tag future friends; night drones are on the prowl.
Machine learning powered self-driving cars (and flying cars) are now massively available to consumers. The steering wheel has become a thing of the past.
Recommendation engines that suggest what VR shows to watch and what products to buy will now display a different environment for each user group.
At the dawn of a new age, you can’t help but wonder, what is Machine Learning and how did it pivot our world so drastically?
The year is 2049…
New York is overrun by bots and web crawlers. The capabilities of Machine Learning have reached new heights and the world as we know it will never be the same.
Facial recognition technology that helps users tag and share photos of friends can now tag future friends; night drones are on the prowl.
Machine learning powered self-driving cars (and flying cars) are now massively available to consumers. The steering wheel has become a thing of the past.
Recommendation engines that suggest what VR shows to watch and what products to buy will now display a different environment for each user group.
At the dawn of a new age, you can’t help but wonder, what is Machine Learning and how did it pivot our world so drastically?
Forwarded from Future Data Science(FDS) (Asibeh Tenager)
You are very interesting towards learning ML. That's good in a movement. This channel is created to share thoughts, knowledge's, and experiences we have.
We are from different countries. Which country you are?
We are from different countries. Which country you are?
Anonymous Poll
57%
Ethiopia
6%
Kenya
26%
India
0%
Eritrea
11%
Other
Epython Lab via @like
What is Scikit-learn?
Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib!
As you build robust Machine Learning programs, it’s helpful to have all the sklearn commands all in one place in case you forget.
Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib!
As you build robust Machine Learning programs, it’s helpful to have all the sklearn commands all in one place in case you forget.
#LinearRegressionChallenge
Let x be 1, 2, 3
y be 5, 1, 3
m be 8
b be 40
and N is the total number of Dataset we have
Note: N is the number of points, i.e. the length of the x list or the y list.
find
-2/N(Summation of (yi -(mxi+b) if i starts from 1 to N
Use python code
post you solution @pythonEthbot
Let x be 1, 2, 3
y be 5, 1, 3
m be 8
b be 40
and N is the total number of Dataset we have
Note: N is the number of points, i.e. the length of the x list or the y list.
find
-2/N(Summation of (yi -(mxi+b) if i starts from 1 to N
Use python code
post you solution @pythonEthbot
Python_for_Beginners_An_Essential_Guide_to_Easy_Learning_with_Basic.epub
2.3 MB
Python for Beginners : An Essential Guide to Easy Learning with Basic Exercises : Python programming Crash Course for Data Analysis and for Beginner Hackers
Walsh, Conley (2020)
@epythonlab #pythonbooks
Walsh, Conley (2020)
@epythonlab #pythonbooks
#Python #List
A list is a common Python datatype that is ordered and changeable. It allows duplicate values.
Creating a list is as simple as putting different comma-separated values between square brackets:
A python list is very important especially for data science and Machine learning because the data of datasets are organized in a list form. We can further investigate the future predictions based on the list of data.
A list is a common Python datatype that is ordered and changeable. It allows duplicate values.
Creating a list is as simple as putting different comma-separated values between square brackets:
animals = ["Cow", "Ox", "Got"]
A python list is very important especially for data science and Machine learning because the data of datasets are organized in a list form. We can further investigate the future predictions based on the list of data.
👍1
#Python #Tools
Most usefully Python tools
#web Developers
-Django
-Bottle
-CherryPy
-Pyramid
-TurboGears
#Game Developers
-Pygame
-Pyglet
-PyOpenGl
-Arcade
-Panda3D
#Gui Developers
-Kivy
-Tkinter
-WxPython
-PyGUI
-PySide
#Machine Learning
-TensorFlow
-keras
-Theano
-Scikit-learn
-PyTorch
#Data Science
-NumPy
-Pandas
-SciPy
-IPython
-Plotly
#Image processing
-Opencv
-Mahotas
-SimpleTK
-Pillow
-Scikit-Image
#Web scraping
-Requests
-Beautiful Soup
-Selenium
-Lxml
-ScraPy
#Authomation Testing
-Splinter
-RoBot
-Behave
-PyUnit
-Pytest
Most usefully Python tools
#web Developers
-Django
-Bottle
-CherryPy
-Pyramid
-TurboGears
#Game Developers
-Pygame
-Pyglet
-PyOpenGl
-Arcade
-Panda3D
#Gui Developers
-Kivy
-Tkinter
-WxPython
-PyGUI
-PySide
#Machine Learning
-TensorFlow
-keras
-Theano
-Scikit-learn
-PyTorch
#Data Science
-NumPy
-Pandas
-SciPy
-IPython
-Plotly
#Image processing
-Opencv
-Mahotas
-SimpleTK
-Pillow
-Scikit-Image
#Web scraping
-Requests
-Beautiful Soup
-Selenium
-Lxml
-ScraPy
#Authomation Testing
-Splinter
-RoBot
-Behave
-PyUnit
-Pytest