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Applied Python for Data Science

Course Code: 1044

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$2595

|

Applied Python for Data Science

Learn to apply data science methods and techniques and acquire analysis skills

Course Code : 1044

$2595

Overview
Schedule classes
What you'll learn
Outline
Prerequisites
Who should attend

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.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 5 days

Live Virtual Classroom
Duration: 5 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
Built-in 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
Open 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 Pythonism
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

iPython basics
Terminal and GUI shells
Creating and using notebooks
Saving and loading notebooks
Ad hoc 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
Interpolating 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 imaging library
Image processing
Displaying changes

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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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