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Applied Python for Data Science

Course Code: 1044
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$2495

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Overview

This comprehensive hands-on course combines engaging lectures, demos, activities and discussions to ensure participants learn and understand the all the concepts discussed in the course. As part of the course, participants would work in an engaging, hands-on learning environment, guided by an expert Python practitioner. The course covers details about the Python environment, flow control, sequences, lists, Tuples, dictionaries and sets, OS services, modules and packages, XML and JSON and other Python concepts.

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Trivera

Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 4 days

Live Virtual Classroom
Duration: 4 days

What You'll learn

  • Create and run basic programs
  • Design and code modules and classes
  • Implement and run unit tests
  • Use benchmarks and profiling to speed up programs
  • Process XML and JSON
  • Manipulate arrays with NumPy
  • Sub-packages constituting SciPy
  • Use iPython notebooks for ad hoc calculations, plots and what-if
  • Manipulate images with PIL
  • Solve equations with SymPy

Outline

Module 1: The Python environment

  • About Python
  • Starting Python
  • Using the interpreter
  • Running a Python script
  • Python scripts on Unix/Windows
  • Using the Spyder editor

Module 2: Getting started

  • Using variables
  • Builtin functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • String formatting
  • Command line parameters

Module 3: Flow control

  • About flow control
  • White space
  • Conditional expressions (if,else)
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits

Module 4: Sequences

  • About sequences
  • Lists and tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions, keywords, and operators
  • List comprehensions
  • Generator expressions
  • Nested sequences

Module 5: Working with files

  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Raw (binary) data

Module 6: Dictionaries and Sets

  • Creating dictionaries
  • Iterating through a dictionary
  • Creating sets
  • Working with sets

Module 7: Functions

  • Defining functions
  • Parameters
  • Variable scope
  • Returning values
  • Lambda functions

Module 8: Error and exception handling

  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions

Module 9: OS services

  • The os module
  • Environment variables
  • Launching external commands
  • Walking directory trees
  • Paths, directories, and filenames
  • Working with file systems
    • Dates and times

Module 10: Pythonic idioms

  • Small Pythonisms
  • Lambda functions
  • Packing and unpacking sequences
  • List Comprehensions
  • Generator Expressions

Module 11: Modules and packages

  • Initialization code
  • Namespaces
  • Executing modules as scripts
  • Documentation
  • Packages and name resolution
  • Naming conventions
  • Using imports

Module 12: Classes

  • Defining classes
  • Constructors
  • Instance methods and data
  • Attributes
  • Inheritance
  • Multiple inheritance

Module 13: Developer tools

  • Analyzing programs with pylint
  • Creating and running unit tests
  • Debugging applications
  • Benchmarking code
  • Profiling applications

Module 14: XML and JSON

  • Using ElementTree
  • Creating a new XML document
  • Parsing XML
  • Finding by tags and XPath
  • Parsing JSON into Python
  • Parsing Python into JSON

Module 15: iPython

  • iiPython basics
  • Terminal and GUI shells
  • Creating and using notebooks
  • Saving and loading notebooks
  • Ad hoc data visualization

Module 16: NumPy

  • numpy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks

Module 17: SciPy

  • What can SciPy do?
  • Most useful functions
  • Curve fitting
  • Modeling data visualization
  • Statistics

Module 18: A tour of SciPy sub-packages

  • Clustering
  • Physical and mathematical Constants
  • FFTs
  • Integral and differential solvers
  • Interpolation and smoothing
  • Input and Output
  • Linear Algebra
  • Image Processing
  • Distance Regression
  • Root-finding
  • Signal Processing
  • Sparse Matrices
  • Spatial data and algorithms
  • Statistical distributions and functions
  • C/C++ Integration

Module 19: Pandas

  • pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

Module 20: matplotlib

  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images

Module 21: The Python Imaging Library

  • PIL overview
  • Core image library
  • Image processing
  • Displaying images
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Prerequisites

Participants must be comfortable working with files, folders and command lines.

Who Should Attend

The Applied Python for Data Science course is highly recommended for –

  • Data analysts
  • Developers
  • Engineers
  • Anyone tasked with utilizing Python for data analytics tasks

Interested in this course? Let’s connect!

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