Applied Python for Scientists and Engineers Training

About Python

Starting Python

Using the interpreter

Running a Python script

Python scripts on UNIX/Windows

Using the Spyder editor

Using variables

Built-in functions

Strings

Numbers

Converting among types

Writing to the screen

String formatting

Command line parameters

About flow control

White space

Conditional expressions (if,else)

Relational and Boolean operators

While loops

Alternate loop exits

About sequences

Lists and tuples

Indexing and slicing

Iterating through a sequence

Sequence functions, keywords, and operators

List comprehensions

Generator expressions

Nested sequences

File overview

Opening a text file

Reading a text file

Writing to a text file

Raw (binary) data

Creating dictionaries

Iterating through a dictionary

Creating sets

Working with sets

Defining functions

Parameters

Variable scope

Returning values

Lambda functions

Syntax errors

Exceptions

Using try/catch/else/finally

Handling multiple exceptions

Ignoring exceptions

The O module

Environment variables

Launching external commands

Walking directory trees

Paths, directories, and filenames

Working with file systems

Dates and times

Small Pythonisms

Lambda functions

Packing and unpacking sequences

List Comprehensions

Generator Expressions

Initialization code

Namespaces

Executing modules as scripts

Documentation

Packages and name resolution

Naming conventions

Using imports

Defining classes

Constructors

Instance methods and data

Attributes

Inheritance

Multiple inheritance

Analyzing programs with pylint

Creating and running unit tests

Debugging applications

Benchmarking code

Profiling applications

Using ElementTree

Creating a new XML document

Parsing XML

Finding by tags and XPath

Parsing JSON into Python

Parsing Python into JSON

iiPython basics

Terminal and GUI shells

Creating and using notebooks

Saving and loading notebooks

Ad hoc data visualization

numpy basics

Creating arrays

Indexing and slicing

Large number sets

Transforming data

Advanced tricks

What can scipy do?

Most useful functions

Curve fitting

Modeling

Data visualization

Statistics

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

We conduct online instructor led virtual training all across the United States and Canada.

Yes, you will get a certificate after successfully passing the 4850 Advanced Python Programming exam that can be scheduled after completing four days online training.

During this five days career booster course, you learn Numpy nuts and bolts, Making exhibits, Ordering and cutting, Vast number sets, Changing information, Propelled traps etc. For more details, plz check the 4870 Applied Python for Scientists and Engineers course training syllabus or talk with our online counselor.

Our highly trained and experienced online instructors help you get and install Python program and to apply the learned skills in lab assignments; Applied Python for Scientists and Engineers is 50:50 -lab to lecture course.