Applied Python for Scientists and Engineers Training

Applied Python for Scientists and Engineers Training provides in-depth knowledge of Applied Python for professionals who have a basic understanding of Python Programming.

📘 Python 👥 1586 Enrolled ⏱️ 4 Days 💼 Intermediate Level ⭐ 4.8 | 113 Reviews

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Course Overview

Applied Python for Scientists and Engineers Training provides in-depth knowledge of Applied Python for professionals who have a basic understanding of Python Programming.

This technical course helps professionals learn and master the critical elements of Python Programming and utilize its features for smooth mathematical and scientific computing.

It covers Fundamental Python Concepts and improves your scripting skills needed to master module and class designing, profile and benchmarking, NumPy Array Manipulation, JSON, and XML Processing, PIL Image Manipulation and other core concepts.

Our enterprise training program is ideal for organizations and companies. This program is specially designed to help professionals develop a better understanding of critical modules of Python Programming for Engineers and Scientists.

With this course, your Python programming skills will improve, and you will be able to build credibility in the market.

It contains all the main topics, making it suitable for individuals preparing for Applied Data Science with Python Certification.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

What you will 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
Get a grasp of the diversity of sub packages that make up scipy
Use iPython notebooks for ad hoc calculations, plots, and what-if?
Manipulate images with PIL
Solve equations with sympy

Who Should Attend This Course?

Non-object oriented developers looking to apply Python to their scientific or engineering related job roles.

Prerequisites

There are no programming prerequisites, programming experience is helpful.

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📘 Applied Python for Scientists and Engineers Outline

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

❓ Frequently Asked Questions

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.