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Introduction to Analytics with Azure Notebooks and Python

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Introduction to Analytics with Azure Notebooks and Python for Data Science and Business Intelligence. This is one part of a full day workshop on moving from BI to Analytics

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Introduction to Analytics with Azure Notebooks and Python

  1. 1. Analytics With Python Jen Stirrup Data Whisperer, Data Relish Level: 300
  2. 2. Agenda • Python Notebooks in Azure • Python language
  3. 3. Azure Notebooks
  4. 4. Rich Web Client Text & Math Code Results Share, Reproduce
  5. 5. Using the Jupyter Notebook, you can author documents that combine your code with comments, equations, images, video, and visualizations. The documents can be shared with others on GitHub, Dropbox, and the Jupyter Notebook Viewer.
  6. 6. Uses include: Data cleaning and transformation numerical simulation statistical modeling Machine learning Data Visualization and much more.
  7. 7. Leverage big data tools Apache Spark, from Python, R and Scala. Explore with pandas, scikit-learn, ggplot2, dplyr, etc.
  8. 8. Notebook mode supports literate computing and reproducible sessions Code chunks alongside the results and additional comments
  9. 9. Notebooks deal with narratives that communicate Data and code storytelling
  10. 10. Azure Notebooks: Getting Started Jen Stirrup
  11. 11. IPython notebook is an HTML-based notebook environment for Python. It is based on the IPython shell
  12. 12. Version Controlled Science Literate format Open format consisting of executable code, text and multimedia Data Visualization
  13. 13. Python Fundamentals | DLAB @ Berkeley Python Lectures | Rajath Kumar MP Intro Programming | Eric Matthes Python Crash Course | Eric Matthes IPython Minibook | Cyrille Rossant IPython & Project Jupyter | Fernando Perez
  14. 14. Python for quick development, long maintenance
  15. 15. Opportunity To Skill up • Python will help you to diversify… • Python will help you to accelerate… • Python will help you to succeed...
  16. 16. Python Is... • A scripting language • Free and open-source • A huge ecosystem • Well used and supported
  17. 17. Used By...
  18. 18. What Is Python? • Powerful, interpreted language • iPython notebook is easy to use • Created by Guido van Rossum • Scripting language • Identation for statement grouping • Fast • High-level data types
  19. 19. Python vs. R • Both of these languages are free and very popular for data analysis. There are some differences, however. • R has a long, trusted history, and a lot of support in the data industry.
  20. 20. Python vs. R • Python is easier to master than R, especially if you have previously learned an object-oriented programming language like Java or C++. • It is more of a general-purpose programming language
  21. 21. Choosing Between Python and R • Python doesn’t have as many packages and libraries as R • Python has a lot of tools such as Pandas, Numpy, Scipy, Seaborn • Personal Preference: maths and stats folks tend to prefer R, Computer scientists tend to prefer Python
  22. 22. Python Data Structures • It’s important to familiarise yourself with the common data structures in Python in order to use them appropriately. • Lists – One of the most versatile data structures in Python.
  23. 23. Python Data Structures ● Strings - Strings can simply be defined by use of single ( ‘ ), double ( ” ) or triple ( ”’ ) inverted commas. Strings enclosed in triple quotes ( ”’ ) can span over multiple lines and are used frequently in docstrings (Python’s way of documenting functions). is used as an escape character. Please note that Python strings are immutable, so you can’t change part of strings.
  24. 24. Python Data Structures ● Tuples - represented by a number of values separated by commas. ● A tuple is a sequence of immutable Python objects. Tuples are sequences, just like lists. The differences between tuples and lists are, the tuples cannot be changed unlike lists and tuples use parentheses, whereas lists use square brackets. ● Creating a tuple is as simple as putting different comma-separated values. Optionally you can put these comma-separated values between parentheses also.
  25. 25. User-Defined Functions Here are some basic guidelines to follow when defining a function in Python: ● Function blocks begin with the keyword def followed by the function name and parentheses ( ( ) ). ● Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses. ● The first statement of a function can be an optional statement - the documentation string of the function or docstring. ● The code block within every function starts with a colon (:) and is indented. ● The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.
  26. 26. Installing Python • Generally, installing Python on your system is very easy. The majority of Linux and UNIX distributions include a recent version of Python. Some Windows computers also come with Python pre-installed. If you don’t have Python already, installation is unremarkable on almost all platforms.
  27. 27. Who Can Use Python? Because Python is so easy to learn and logic-based, it isn’t just reserved for programmers and data scientists the way that some programming languages feel like they are. Because of this, it’s being increasingly adopted by non-programmers, and ‘regular’ users with less to no experience. The dynamic of the ‘typical’ Python user, and therefore the typical coder or programmer, is evolving because of Python’s accessibility. Python is also evolving very quickly within data science circles. There are so many data science tools in the Python ecosystem now that a large amount of the work being carried out in data science is being done using Python.

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