Are you Down To Learn Python?
- Thread starter ScytheSalvation
- Start date
He does Python and DevOps bootcamps.
(Baltimore we out here)
Thanks. Going to try and get back into it.
Free IT Training Courses
www.pluralsight.com
Build team skills in AI, data, cloud, and security
Pluralsight gives enterprises the confidence to build and validate the tech skills their teams need to deliver AI, cloud, security, and data transformation.
www.pluralsight.com
Bookmarked
Took some courses in the mid eighties and every word of this i understand .....
During a Python developer interview, what are some red flags that you cannot ignore?
www.quora.com
Ben Podgursky, Co-Founder at CloudWright. Senior data engineer.
Updated September 23, 2019 · Upvoted by Alan Mellor, Started programming 8 bit computers in 1981
Me; “I have a file, where each row is an ID. I want you to describe a Python script to read the file and give me the count of unique IDs”
Interviewee: “Ok. I would import Pandas, load the file into a DataFrame, and ask for the unique count.”
“How would you do this without using Pandas?”
“…”
“I just mean, using raw Python”
“I don’t know.”
“Don’t worry about file opening syntax. Just walk me through the data structures and program flow”
“Ok. I would put the records in…. a list”
You can guess how it goes from there. There are fancy ways to solve this, and straightforward ways to solve this… but dumping everything in a list is definitely not going to cut it.
It’s really astounding how many people you’ll find who have absolutely no knowledge of Python fundamentals — and by fundamentals, I mean “able to use a set/dictionary”. They’re able to do write just enough Python to load Pandas, and then they know the right Pandas incantations to answer their queries.
As far as they are concerned, there’s a wizard behind the curtain, gutting pigeons and reading the entrails every time you query Pandas. Ask them to do anything Pandas can’t handle, and it’s a total trainwreck.
It’s actually a bit difficult to filter this out on resume screens, because you can get pretty far knowing only Data Science tool munging, so you can get programmy-sounding projects on your resume without actually knowing…. any programming.
But if you want to move from “data munger” to “software engineer”, you’re going to have to dig a bit deeper and actually learn Python.
There’s a lot of dissent in the comments here, so I do want to clarify a few things:
Not knowing a hash table / set doesn’t mean you’re not worth hiring and training. There are a lot of roles where there can be support infrastructure to get someone trained on CS basics, coming in self-trained or from a bootcamp.
What you run into though, is applicants who are applying to mid-to-senior tier jobs based on their experience with Pandas / similar libraries. These candidates are not qualified to hit the ground running in non-junior roles. This is specifically what I saw — we wanted a candidate with 2+ years experience, because the team was losing senior engineers. The resumes of these candidates did not match their actual ability to implement on day 1.
I will also note, that if someone is applying to mid-senior tier jobs, it surprises me when they have come in not having done even basic interview prep. Look guys — constant vs linear time set membership is like, literally the most basic complexity analysis you will do. This displays a lack of awareness of your own capabilities, and a lack of research into the job you’re applying for.
tl,dr: I have a lot of respect for self-taught and bootcamp graduates. The flag here is about a skill mismatch when applying for not-completely green roles. Not about inability to learn CS fundamentals on the fly. But also, you will do yourself a favor by learning cursory complexity analysis before going through interviews.
During a Python developer interview, what are some red flags that you cannot ignore?
Answer (1 of 8): I liked this question, so let me put few points from my perspective. Also, I am considering purely based on understanding of Python, and not the algo/DS/DP stuff. Checkpoint 1 How immutability and mutability is related to hashability. Like string or int or tuple are immutable...
Ben Podgursky, Co-Founder at CloudWright. Senior data engineer.
Updated September 23, 2019 · Upvoted by Alan Mellor, Started programming 8 bit computers in 1981
Me; “I have a file, where each row is an ID. I want you to describe a Python script to read the file and give me the count of unique IDs”
Interviewee: “Ok. I would import Pandas, load the file into a DataFrame, and ask for the unique count.”
“How would you do this without using Pandas?”
“…”
“I just mean, using raw Python”
“I don’t know.”
“Don’t worry about file opening syntax. Just walk me through the data structures and program flow”
“Ok. I would put the records in…. a list”
You can guess how it goes from there. There are fancy ways to solve this, and straightforward ways to solve this… but dumping everything in a list is definitely not going to cut it.
It’s really astounding how many people you’ll find who have absolutely no knowledge of Python fundamentals — and by fundamentals, I mean “able to use a set/dictionary”. They’re able to do write just enough Python to load Pandas, and then they know the right Pandas incantations to answer their queries.
As far as they are concerned, there’s a wizard behind the curtain, gutting pigeons and reading the entrails every time you query Pandas. Ask them to do anything Pandas can’t handle, and it’s a total trainwreck.
It’s actually a bit difficult to filter this out on resume screens, because you can get pretty far knowing only Data Science tool munging, so you can get programmy-sounding projects on your resume without actually knowing…. any programming.
But if you want to move from “data munger” to “software engineer”, you’re going to have to dig a bit deeper and actually learn Python.
There’s a lot of dissent in the comments here, so I do want to clarify a few things:
Not knowing a hash table / set doesn’t mean you’re not worth hiring and training. There are a lot of roles where there can be support infrastructure to get someone trained on CS basics, coming in self-trained or from a bootcamp.
What you run into though, is applicants who are applying to mid-to-senior tier jobs based on their experience with Pandas / similar libraries. These candidates are not qualified to hit the ground running in non-junior roles. This is specifically what I saw — we wanted a candidate with 2+ years experience, because the team was losing senior engineers. The resumes of these candidates did not match their actual ability to implement on day 1.
I will also note, that if someone is applying to mid-senior tier jobs, it surprises me when they have come in not having done even basic interview prep. Look guys — constant vs linear time set membership is like, literally the most basic complexity analysis you will do. This displays a lack of awareness of your own capabilities, and a lack of research into the job you’re applying for.
tl,dr: I have a lot of respect for self-taught and bootcamp graduates. The flag here is about a skill mismatch when applying for not-completely green roles. Not about inability to learn CS fundamentals on the fly. But also, you will do yourself a favor by learning cursory complexity analysis before going through interviews.
ThanksDuring a Python developer interview, what are some red flags that you cannot ignore?
During a Python developer interview, what are some red flags that you cannot ignore?
Answer (1 of 8): I liked this question, so let me put few points from my perspective. Also, I am considering purely based on understanding of Python, and not the algo/DS/DP stuff. Checkpoint 1 How immutability and mutability is related to hashability. Like string or int or tuple are immutable...
Ben Podgursky, Co-Founder at CloudWright. Senior data engineer.
Updated September 23, 2019 · Upvoted by Alan Mellor, Started programming 8 bit computers in 1981
Me; “I have a file, where each row is an ID. I want you to describe a Python script to read the file and give me the count of unique IDs”
Interviewee: “Ok. I would import Pandas, load the file into a DataFrame, and ask for the unique count.”
“How would you do this without using Pandas?”
“…”
“I just mean, using raw Python”
“I don’t know.”
“Don’t worry about file opening syntax. Just walk me through the data structures and program flow”
“Ok. I would put the records in…. a list”
You can guess how it goes from there. There are fancy ways to solve this, and straightforward ways to solve this… but dumping everything in a list is definitely not going to cut it.
It’s really astounding how many people you’ll find who have absolutely no knowledge of Python fundamentals — and by fundamentals, I mean “able to use a set/dictionary”. They’re able to do write just enough Python to load Pandas, and then they know the right Pandas incantations to answer their queries.
As far as they are concerned, there’s a wizard behind the curtain, gutting pigeons and reading the entrails every time you query Pandas. Ask them to do anything Pandas can’t handle, and it’s a total trainwreck.
It’s actually a bit difficult to filter this out on resume screens, because you can get pretty far knowing only Data Science tool munging, so you can get programmy-sounding projects on your resume without actually knowing…. any programming.
But if you want to move from “data munger” to “software engineer”, you’re going to have to dig a bit deeper and actually learn Python.
There’s a lot of dissent in the comments here, so I do want to clarify a few things:
Not knowing a hash table / set doesn’t mean you’re not worth hiring and training. There are a lot of roles where there can be support infrastructure to get someone trained on CS basics, coming in self-trained or from a bootcamp.
What you run into though, is applicants who are applying to mid-to-senior tier jobs based on their experience with Pandas / similar libraries. These candidates are not qualified to hit the ground running in non-junior roles. This is specifically what I saw — we wanted a candidate with 2+ years experience, because the team was losing senior engineers. The resumes of these candidates did not match their actual ability to implement on day 1.
I will also note, that if someone is applying to mid-senior tier jobs, it surprises me when they have come in not having done even basic interview prep. Look guys — constant vs linear time set membership is like, literally the most basic complexity analysis you will do. This displays a lack of awareness of your own capabilities, and a lack of research into the job you’re applying for.
tl,dr: I have a lot of respect for self-taught and bootcamp graduates. The flag here is about a skill mismatch when applying for not-completely green roles. Not about inability to learn CS fundamentals on the fly. But also, you will do yourself a favor by learning cursory complexity analysis before going through interviews.
.
www.datacamp.com
Learn R, Python & Data Science Online
Learn Data Science from the comfort of your browser, at your own pace with DataCamp's video tutorials & coding challenges on R, Python, Statistics & more.
Last edited:
thanks...
Im currently learning Python on Datacamp.com
I just completed the Introduction to Python and received my completion cert for that one.
I am now on Intermediate Python. Then on to Python for Data Science 1 and 2.
Then on to the R Programming then SQL Programming courses.
https://www.datacamp.com/
Thanks Master fard Muhammad who is almighty God Allah in Person
hey you summabytch..
good lookin on the link.....
I owe you a drink
ol fireball drinkin lookin ass
I’m focused in on Python right now.
Shit is very challenging to learn. Don’t think you’re going to get the hang of it in a few weeks. It’s a life long learning ordeal fucking with Python.
Shit is very challenging to learn. Don’t think you’re going to get the hang of it in a few weeks. It’s a life long learning ordeal fucking with Python.
What are the major differences between Python and R for data science?
Both Python and R have vast software ecosystems and communities, so either language is suitable for almost any data science task. That said, there are some areas in which one is stronger than the other.
Where Python Excels
If you’re too impatient to wait for a particular feature in your language of choice, it's also worth noting that there is excellent language interoperability between Python and R. That is, you can run R code from Python using the rpy2 package, and you can run Python code from R using reticulate. That means that all the features present in one language can be accessed from the other language. For example, the R version of deep learning package Keras actually calls Python. Likewise, rTorch calls PyTorch.
Beyond features, the languages are sometimes used by different teams or individuals based on their backgrounds.
Who Uses Python
Both Python and R have vast software ecosystems and communities, so either language is suitable for almost any data science task. That said, there are some areas in which one is stronger than the other.
Where Python Excels
- The majority of deep learning research is done in Python, so tools such as Keras and PyTorch have "Python-first" development. You can learn about these topics in Introduction to Deep Learning in Keras and Introduction to Deep Learning in PyTorch.
- Another area where Python has an edge over R is in deploying models to other pieces of software. Python is a general purpose programming language, so if you write an application in Python, the process of including your Python-based model is seamless. We cover deploying models in Designing Machine Learning Workflows in Python and Building Data Engineering Pipelines in Python.
- Python is often praised for being a general-purpose language with an easy-to-understand syntax
- A lot of statistical modeling research is conducted in R, so there's a wider variety of model types to choose from. If you regularly have questions about the best way to model data, R is the better option. DataCamp has a large selection of courses on statistics with R.
- The other big trick up R's sleeve is easy dashboard creation using Shiny. This enables people without much technical experience to create and publish dashboards to share with their colleagues. Python does have Dash as an alternative, but it’s not as mature. You can learn about Shiny in our course on Building Web Applications with Shiny in R.
- R's functionality was developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization.
If you’re too impatient to wait for a particular feature in your language of choice, it's also worth noting that there is excellent language interoperability between Python and R. That is, you can run R code from Python using the rpy2 package, and you can run Python code from R using reticulate. That means that all the features present in one language can be accessed from the other language. For example, the R version of deep learning package Keras actually calls Python. Likewise, rTorch calls PyTorch.
Beyond features, the languages are sometimes used by different teams or individuals based on their backgrounds.
Who Uses Python
- Python was originally developed as a programming language for software development (the data science tools were added later), so people with a computer science or software development background might feel more comfortable using it.
- Accordingly, transition from other popular programming languages like Java or C++ to Python is easier than the transition from those languages to R.
- R has a set of packages known as the Tidyverse, which provide powerful yet easy-to-learn tools for importing, manipulating, visualizing, and reporting on data. Using these tools, people without any programming or data science experience (at least anecdotally) can become productive more quickly than in Python.
- If you want to test this for yourself, try taking Introduction to the Tidyverse, which introduces R's dplyr and ggplot2 packages. It will likely be easier to pick up on than Introduction to Data Science in Python, but why not see for yourself what you prefer?
- Overall, if you or your employees don't have a data science or programming background, R might make more sense.
@darth frosty Are currently coding professionally? Did it take you a minute to get Python? Trying to get back into it man. Job market still pretty lucrative?
YES! The job market for data science is incredibly lucrative. Lots of positions hiring if you have the background to handle it.
Python will take time. You have to commit to it. There is no other way.
I'm learning it now and I work on it every day for several hours out the day.
I'm getting the hang of it now, but it's taking some grueling moments to get past language and syntax.
I am planning a career switch from teaching to more advanced data science. Companies are hiring EVERYWHERE, bruh. Even looking for people to work 100% remotely.
What are the major differences between Python and R for data science?
Both Python and R have vast software ecosystems and communities, so either language is suitable for almost any data science task. That said, there are some areas in which one is stronger than the other.
Where Python Excels
Where R Excels
- The majority of deep learning research is done in Python, so tools such as Keras and PyTorch have "Python-first" development. You can learn about these topics in Introduction to Deep Learning in Keras and Introduction to Deep Learning in PyTorch.
- Another area where Python has an edge over R is in deploying models to other pieces of software. Python is a general purpose programming language, so if you write an application in Python, the process of including your Python-based model is seamless. We cover deploying models in Designing Machine Learning Workflows in Python and Building Data Engineering Pipelines in Python.
- Python is often praised for being a general-purpose language with an easy-to-understand syntax
This list is far from exhaustive and experts endlessly debate which tasks can be done better in one language or another. Further, Python programmers and R programmers tend to borrow good ideas from each other. For example, Python's plotnine data visualization package was inspired by R's ggplot2 package, and R's rvest web scraping package was inspired by Python's BeautifulSoup package. So eventually, the best ideas from either language find their way into the other making both languages similarly useful & valuable.
- A lot of statistical modeling research is conducted in R, so there's a wider variety of model types to choose from. If you regularly have questions about the best way to model data, R is the better option. DataCamp has a large selection of courses on statistics with R.
- The other big trick up R's sleeve is easy dashboard creation using Shiny. This enables people without much technical experience to create and publish dashboards to share with their colleagues. Python does have Dash as an alternative, but it’s not as mature. You can learn about Shiny in our course on Building Web Applications with Shiny in R.
- R's functionality was developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization.
If you’re too impatient to wait for a particular feature in your language of choice, it's also worth noting that there is excellent language interoperability between Python and R. That is, you can run R code from Python using the rpy2 package, and you can run Python code from R using reticulate. That means that all the features present in one language can be accessed from the other language. For example, the R version of deep learning package Keras actually calls Python. Likewise, rTorch calls PyTorch.
Beyond features, the languages are sometimes used by different teams or individuals based on their backgrounds.
Who Uses Python
Who Uses R
- Python was originally developed as a programming language for software development (the data science tools were added later), so people with a computer science or software development background might feel more comfortable using it.
- Accordingly, transition from other popular programming languages like Java or C++ to Python is easier than the transition from those languages to R.
Wrapping up, though it may be hard to know whether to use Python or R for data analysis, both are great options. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer. Finally, I’ll share the first bit of this a handy infographic comparing the two languages. I don’t want to include it all as it’s very long and would require too much scrolling, but you can download the full image here.
- R has a set of packages known as the Tidyverse, which provide powerful yet easy-to-learn tools for importing, manipulating, visualizing, and reporting on data. Using these tools, people without any programming or data science experience (at least anecdotally) can become productive more quickly than in Python.
- If you want to test this for yourself, try taking Introduction to the Tidyverse, which introduces R's dplyr and ggplot2 packages. It will likely be easier to pick up on than Introduction to Data Science in Python, but why not see for yourself what you prefer?
- Overall, if you or your employees don't have a data science or programming background, R might make more sense.
If you know Python, then there is very little reason to switch to R.
Python can essentially do everything R can do plus more. Python can integrate R! Lol
Thanks @4 Dimensional I will stick to the coding. I have to get back on it asap. You are using Data Camp?
Same here. Computer science major teaching music production. I’ve been locked in on Dart/Flutter. Looking to design music apps and teach advanced app UI/UX design to low income kids
Naw. I'm self-teaching.
I'm using the COVID19 raw data for my training in Python. Since I already have a mathematical, statistical, data analysis, and programming background, I don't have a huge learning curve in trying to figure out how to do things.
I just had to put my loyalty aside for specific languages to want to learn Python. Analyzing COVID19 data and doing automation for my analysis has given me many reasons to learn Python.
That's what's up. App designing is one thing I haven't been able to get interested in.
I attempted a few times, but haven't found a reason to keep going yet.
this because I stopped for a month and now I have to start all over again. I'm following a course on Udemy
Man, it’s crazy how fast you can forget something when you stop messing with it.
I’m setting up scripts and making comments so I can remember what each function is for, so if I have to take a break then I can come back to something done.
It becomes a foreign language if you stop using it for a month.
This dude is pretty legit too.
Last edited:
Use Udacity for MACHINE LEARNING ENGINEER certification.
If you are learning python to get into data science, download Anaconda and install through there, it's a package manager.
https://www.anaconda.com/products/individual
Super easy to create a new python notebook.
Use some free open datasets to get going:
github.com
Hope you all are on a Mac.
There are also a lot of ML APIs already available so dig into those as there is a not a lot of need to build most models by hand, most are out the box and something called AutoML is on the rise.
eg
https://aws.amazon.com/blogs/aws/am...aged-automatic-machine-learning/?tag=vp314-20
and:
https://aws.amazon.com/blogs/openso...n-an-open-source-automl-library/?tag=vp314-20
https://aws.amazon.com/sagemaker/autopilot/?tag=vp314-20
Google:
cloud.google.com
and AutoML:
cloud.google.com
and
ML Crash course:
developers.google.com
These are just additional stuff for the more technical folks:
BigML
bigml.com
H20
www.h2o.ai
Databricks:
databricks.com
Blessing on your journeys...........
If you are learning python to get into data science, download Anaconda and install through there, it's a package manager.
https://www.anaconda.com/products/individual
Super easy to create a new python notebook.
Use some free open datasets to get going:
GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.
A topic-centric list of HQ open datasets. Contribute to awesomedata/awesome-public-datasets development by creating an account on GitHub.
github.com
Hope you all are on a Mac.
There are also a lot of ML APIs already available so dig into those as there is a not a lot of need to build most models by hand, most are out the box and something called AutoML is on the rise.
eg
https://aws.amazon.com/blogs/aws/am...aged-automatic-machine-learning/?tag=vp314-20
and:
https://aws.amazon.com/blogs/openso...n-an-open-source-automl-library/?tag=vp314-20
https://aws.amazon.com/sagemaker/autopilot/?tag=vp314-20
Google:
AI & Machine Learning Products & Services | Google Cloud
Fast, scalable, and easy-to-use AI offerings including AI Platform, video and image analysis, speech recognition, and multi-language processing.
and AutoML:
Google Cloud AutoML - Train models without ML expertise | Google Cloud
Cloud AutoML helps you easily build high quality custom machine learning models with limited machine learning expertise needed.
and
ML Crash course:
Linear regression | Machine Learning | Google for Developers
This course module teaches the fundamentals of linear regression, including linear equations, loss, gradient descent, and hyperparameter tuning.
developers.google.com
These are just additional stuff for the more technical folks:
BigML
BigML.com
Machine Learning made beautifully simple for everyone. Take your business to the next level with the leading Machine Learning platform.
H20
The Trusted Sovereign AI Platform
The trusted Sovereign AI Platform. Run predictive, generative, and agentic AI on your own infrastructure, from the cloud to fully air-gapped. You own it.
Databricks:
Databricks: Leading Data and AI Solutions for Enterprises
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data + AI Platform.
databricks.com
Blessing on your journeys...........
check out some of the links i posted about.
ML flow create the ML catalog you need and is included in Databricks.
Alternatively you can just use an existing ML API
The key is to understand the ML concepts like classification, regression, feature engineering, clustering and when to use ML
Bless
Nope I just have a wide variety of interests and like sharing stuff that may help others.
About to go through every link. Thanks
Signed up for it and started using the features last week. Very streamlined and simple platform. Definitely beneficial to use all of the guidance programs they offer with it too.
ok nice.
There are free tier credits for a lot of stuff so don't be scared to try things.
There a a number of youtube videos that will help you.
Start with something you always wanted to solve, a personal painpoint
Many thanks dude. Can you point me out to some of your preferred youtubers relating to it? Trying to take it all in mane.
Similar threads
