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Python in Industry


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Slides from the talk given at Nirma University. Audience comprised of fifth semester Electronics & Communications (EC) Engineering students.

Published in: Technology
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Python in Industry

  1. 1. Python in Industry Dharmit Shah
  2. 2. GOAL To make you aware of awesome career options for Python Programmers!
  3. 3. Topics ● Who uses Python? How do they use Python? ● What kind of problems does Python help solve? ● What are different career options for a Python programmer? ● How to become a better Python programmer?
  4. 4. Who am I? ● Developer at Red Hat ● Working on open source projects that are built using Python & Golang ● I organize and speak at various Meetups in Ahmedabad ○ Docker ○ Ansible ○ Kubernetes ○ DigitalOcean ● Firm believer in lifelong learning
  5. 5. Brief intro to Python
  6. 6. History of Python ● Developed by Guido van Rossum in late 1980s ● Named after a TV show called “Monty Python” ● Python interpreter developed using C language ● Open source - so you can contribute to it as well!
  7. 7. Which organizations use Python? ● Red Hat ● YouTube ● Instagram, Facebook ● NASA ● Google ● Microsoft ● ...many more
  8. 8. How do organizations use Python? ● Web framework for backend ● Data analysis (scientific and numeric computing) ● Machine learning & Artificial Intelligence ● Automating the boring tasks ● Configuration management ● Web Scraping ● Embedded systems
  9. 9. Web Frameworks
  10. 10. What is a framework and why use it? ● Framework is a collection of code that makes it easier to develop applications ● Web framework makes it easy to write, scale and maintain web applications ● They provide features like: ○ URL routing ○ Database manipulation ○ Security ○ Session control ● Different frameworks have different set of features
  11. 11. django ( ● Most popular web framework of Python ● Provides a lot of features to develop websites; probably most feature-rich! ● Works with number of databases using ORM (no need to learn SQL!) ● But doesn’t work so well with NoSQL ● Has a huge community of developers using it ● Extensive documentation available online!
  12. 12. Flask ( ● A minimalistic framework ● Very easy to get started with as a beginner ● Doesn’t follow batteries included approach of django ● Excellent online documentation and great community of developers
  13. 13. Few other frameworks ● Bottle ( ● Pyramid ( ● Falcon (
  14. 14. Data Analysis
  15. 15. What is Data Analysis? ● Process of producing meaningful information from a big (huge?) chunk of data ● Encompasses various other domains: ○ Data Mining ○ Business Intelligence ○ Predictive Analysis, etc. ● Closely related to Data Visualization ● Helps make decisions that might change the future!
  16. 16. SciPy Ecosystem ( ● An ecosystem of opensource software for maths, science & engineering ● NumPy ○ Package for numerical computation. ○ Helps define numerical arrays and matrices ○ Perform operations on arrays & matrices ● SciPy library ○ Collection of numerical algorithms and domain-specific toolboxes ○ Signal processing, optimization, statistics and more
  17. 17. SciPy Ecosystem (contd..) ● Matplotlib ○ Popular plotting package ○ Helps plot 2D and basic 3D plots ● Pandas: provides high-performance, easy to use data structures ● scikit-image: collection of algorithms for image processing ● scikit-learn: collection of algorithms for machine learning ● IPython: an alternate interface to interact with Python interpreter
  18. 18. Data Analysis ● Data is continuously increasing! ● Making sense of data is a hot skill ● People from varying educational background are picking it up! ● Jobs & opportunities up for grabs! ● Plenty of MOOC (massive open online course) available
  19. 19. Machine Learning And Artificial Intelligence
  20. 20. What is ML and AI? ● Ability of computers to learn without being programmed! ● Ability to perform data driven decisions ● Significant overlap with Data Mining ● ML focuses on prediction, based on known properties ● Data Mining focuses on the discovery of (previously) unknown properties
  21. 21. Frameworks and libraries ● Mostly the same as the ones we covered in Data Analysis ● It’s about how we use those libraries ● Also, TensorFlow
  22. 22. Automating the Boring Tasks
  23. 23. A book! ● Automating tasks that would otherwise take hours if done manually ● Great book titled “Automate the Boring Stuff with Python” ○ Search for text in a file or across multiple files ○ Create, update, move, and rename files and folders ○ Search the Web and download online content ○ Update and format data in Excel spreadsheets of any size ○ Split, merge, watermark, and encrypt PDFs ○ Send reminder emails and text notifications ○ Fill out online forms ● (free to read online!)
  24. 24. Python for Electronics Engineers
  25. 25. Python and Embedded systems ● Steadily increasing adaption ● Boards ○ MicroPython ○ Raspberry Pi ○ Arduino ● Lots and lots of documentation and tutorials available online!
  26. 26. Few projects ● Motion sensor with alarm ● Home automation system ● Use Lego toys to make robotic cars ● Send board to space (PITS : Pi In The Sky) ● ...many, many more!
  27. 27. Web Scraping
  28. 28. What is it? ● Process of extracting data from websites ● Data from websites is downloaded for later analysis ● This data is then extracted ● The extracted content may be parsed, searched, reformatted, etc.
  29. 29. Python tools for scraping ● BeautifulSoup ● Mechanize ● Scrapemark ● Scrapy
  30. 30. What else can be done with Python? ● Chatbots ● Blockchain ● Configuration Management tools ● Desktop and mobile applications
  31. 31. Career Options
  32. 32. ● Developer ○ Backend developer ○ Full Stack developer ● System Administrator ● Data Scientist ● Many more specialized roles!
  33. 33. How to get better at Python?
  34. 34. Talk is cheap, write some code! ● Participate in open source projects ● Read more Python code and then write more ● Subscribe to newsletters ○ Python Weekly ○ Import Python ○ Pycoder’s Weekly ○ Full Stack Python ● Read and learn from ton of free online material about Python ● Find a mentor if you can
  35. 35. Feedback! Contact Me: