This document provides an overview and introduction to learning Python for data science. It outlines the structure, which includes introductions to NumPy, basic plotting, and pandas. It encourages asking questions and discusses why people want to learn Python and how they would use it. It also briefly covers Python's history and development, its use in data science and machine learning today, and different modes like IPython and Python scripts. Finally, it introduces Jupyter notebooks as an interactive coding environment.
Мы уделяем много внимания темам автоматизации тестирования. Но что, если мы посмотрим на сам процесс нашей работы? Сколько рутинных задач мы выполняем каждый день? Насколько эффективно мы их выполняем? Иван расскажет о своем опыте эффективной работы с подобными ежедневными задачами
Мы уделяем много внимания темам автоматизации тестирования. Но что, если мы посмотрим на сам процесс нашей работы? Сколько рутинных задач мы выполняем каждый день? Насколько эффективно мы их выполняем? Иван расскажет о своем опыте эффективной работы с подобными ежедневными задачами
This is a python course for beginners, intended both for frontal class learning as well as self-work.
The Course is designed for 2 days and then another week of HW assignments.
5 Thoughts on Staying Sharp and Relevant (Chicago)Scott Lowe
In this presentation, I share two thoughts on learning and three things I think you should be learning in order to stay sharp and relevant in today's fast-moving IT world.
What's with the 1s and 0s? Making sense of binary data at scale with Tika and...gagravarr
If you have one or two files, you can take the time to manually work out what they are, what they contain, and how to get the useful bits out (probably....). However, this approach really doesn't scale, mechanical turks or no! Luckily, there are Apache projects out there which can help!
In this talk, we'll first look at how we can work out what a given blob of 1s and 0s actually is, be it textual or binary. We'll then see how to extract common metadata from it, along with text, embedded resources, images, and maybe even the kitchen sink! We'll see how to do all of this with Apache Tika, and how to dive down to the underlying libraries (including its Apache friends like POI and PDFBox) for specialist cases. Finally, we'll look a little bit about how to roll this all out on a Big Data or Large-Search case.
Towards TextPy, a module for processing text.
If we define annotated text as a graph with additional structure, we can make text processing more efficient, in the same way that Pandas makes processing dataframes more efficient.
Computational workflows for omics analyses at the IARCMatthieu Foll
In this presentation I describe our use of nextflow to perform analyses of omics data at the International Agency for Research on Cancer. I explain why we needed such a tool and how we selected it over alternative ones. I will discuss our choices of workflow design in the context of the multidisciplinary research performed in our Agency. Finally, I describe how nextflow has improved the overall quality of our reseach with a cost-benefit analysis and report the problems or limitations we have identified.
Presented at the nextflow hackathon 2017 at CRG.
This is a python course for beginners, intended both for frontal class learning as well as self-work.
The Course is designed for 2 days and then another week of HW assignments.
5 Thoughts on Staying Sharp and Relevant (Chicago)Scott Lowe
In this presentation, I share two thoughts on learning and three things I think you should be learning in order to stay sharp and relevant in today's fast-moving IT world.
What's with the 1s and 0s? Making sense of binary data at scale with Tika and...gagravarr
If you have one or two files, you can take the time to manually work out what they are, what they contain, and how to get the useful bits out (probably....). However, this approach really doesn't scale, mechanical turks or no! Luckily, there are Apache projects out there which can help!
In this talk, we'll first look at how we can work out what a given blob of 1s and 0s actually is, be it textual or binary. We'll then see how to extract common metadata from it, along with text, embedded resources, images, and maybe even the kitchen sink! We'll see how to do all of this with Apache Tika, and how to dive down to the underlying libraries (including its Apache friends like POI and PDFBox) for specialist cases. Finally, we'll look a little bit about how to roll this all out on a Big Data or Large-Search case.
Towards TextPy, a module for processing text.
If we define annotated text as a graph with additional structure, we can make text processing more efficient, in the same way that Pandas makes processing dataframes more efficient.
Computational workflows for omics analyses at the IARCMatthieu Foll
In this presentation I describe our use of nextflow to perform analyses of omics data at the International Agency for Research on Cancer. I explain why we needed such a tool and how we selected it over alternative ones. I will discuss our choices of workflow design in the context of the multidisciplinary research performed in our Agency. Finally, I describe how nextflow has improved the overall quality of our reseach with a cost-benefit analysis and report the problems or limitations we have identified.
Presented at the nextflow hackathon 2017 at CRG.
Between Filth and Fortune- Urban Cattle Foraging Realities by Devi S Nair, An...Mansi Shah
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The results underscore the pivotal role of cohesive branding, social media influence, and website usability in shaping positive brand perceptions, influencing consumer decisions, and ultimately bolstering sales and profitability. This paper provides actionable insights and strategic recommendations for businesses seeking to leverage branding, social media, and website design as potent tools to enhance their market position and financial success.
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White wonder, Work developed by Eva TschoppMansi Shah
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2. Structure
• Part 1 - Introduction, recap & warm-up exercises
• Part 2 - NumPy (Numerical Python)
• Part 3 - Basic plotting
• Part 4 - pandas (Python Data Analysis Library)
3. Ask!
The art and science of asking questions is the source of all knowledge.
- Thomas Berger
• Do not hesitate to ask!
• During exercises (you can also ask others).
Image by mohamed Hassan from Pixabay
4. Now let me ask something..
• Why do you want to learn Python/programming?
• What would you use Python for?
5. Failure
• Coding is all about trial and error.
• Don't be afraid of it.
• Error messages aren't scary, they are useful.
7. History
• Started by Guido Van
Rossum as a hobby
• Now widely spread
• Open Source! Free!
• Versatile
Guido Van Rossum
by Doc Searls on
Flickr CC-BY-SA
8. Python today
• Developed a large and active scientific computing and data analysis
community
• Now one of the most important languages for
• Data science
• Machine learning
• General software development
• Packages: NumPy, pandas, matplotlib, SciPy, scikit-learn, statsmodels
9. 2 Modes
1. IPython
Python can be run interactively
Used extensively in research
2. Python scripts
What if we want to run more than a few lines of code?
Then we must write text files in .py
10. Noteable (Jupyter notebooks)
• Easy to use environment
• Web-based
• Combines both text and code
into one
• Come with a great number of
useful packages
24. Let us start
To follow along, you need to open your own notebook. But please try to
keep up with my presentation, as you still have time for exercises during
the teaching.
25. Thank you for attending!
• You can continue to access the ‘Python for Data Science’ Learn course
and notebooks after the course.
• There are solution notebooks for all the topics we have covered
today, plus notebooks covering additional topics (text analysis, signal
processing, regular expressions, network analysis, machine learning &
Conway’s game of life).
• Visit the Digital Skills Framework to find more Python learning
resources: https://www.digitalskills.ed.ac.uk/
Editor's Notes
- Welcome and thank you for coming
- Introduction
- aims:
-- introduce fundamentals
-- build up confidence for self-study!!! Not as scary as the real-world version
- assumptions:
-- basic high-school maths
-- no prior experience in coding
CC-BY Digital Skills Team at the University of Edinburgh www.ed.ac.uk/is/skills
All of us learn through asking and being curious
more of a conversation - don't hesitate to stop me and ask
make notes
You might fail 5 times while doing something but in the end you will understand how it works and be able to replicate it
- Started by Guido Van Rossum as a hobby over the Christmas vacation.
- named after Monty Python's Flying Circus
- First released in 1991, loved by Unix scripters
- Now transformed into the most popular interpreted language. You can build anything with it!
- Facts! Python was the most used language in 2016 and still most likely is!
Why choose it instead of MATLAB, R, SAS, Stata and others?
- It is free and open source
- Intuitive and easy to learn
- Extremely versatile – examples – websites, planes, robots, data
- Large and friendly community
- packages
Very important in industry and academia
Python has 2 modes of running
- IPython stands for Interactive Python
Python can be interpreted – good for a calculator
Live demo!
We will be using Noteable, just another service Jupyter Notebooks. It is free and open source
Web environment focused at Python
Easy to use – nice gui and no setup required
Block based. Has blocks which execute the code within it. Single type only
Allows to combine text and code into same page. Helps with readability
start noteable from learn - Link at the top of homepage
Agree on personal data usage
You don't have to share anything
Probably enter myed login details
Then press start on noteable homepage
Start a notebook by clicking on it.
Notebook 0 is an environment introduction.
Skip that, open notebook 1
Make sure that everybody is ready
Autosave
We need to make a course folder
Then rename it to Introduction to Python or whatever
Then go into that folder
Make sure that you are inside that folder
Run button but its best with shortcut Shift + Return/Enter
Alt + Return/Enter which creates a new block after the on you are currently on.
Checkpoints are useful!
You can download it as multiple different things:
Notebook
Python script
pdf
