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4870 Applied Python for Scientists and Engineers – An Overview

4870 Applied Python for Scientists and Engineers course provides knowledge and hands-on experience to use Python for scientific and mathematical computing. Starting with Python basics, this course drives you to the important Python modules where you learn working with data-from arrays, statistics, and plotting results.

The skill based course is designed for the learners with basic knowledge of python programming who want to apply Python toolkits like Pandas, Matplotlib, Scikit-Learn, Nltk Statistical for machine Learning, information visualization, text analysis, and social network analysis etc. The learned skills deliver expertise to gain insights into database.

For Who ‘Applied Python for Scientists and Engineers’ is Must to Join Program:

Python professionals, looking to apply Python skills to scientific or engineering related roles with intention to enhance the career prospects, are the ideal candidates for Applied Python for Scientists and Engineers course. Scientists and Engineers with basic knowledge of Python will be highly benefited with this five days online course.  

Learning Objectives of ‘Applied Python for Scientists and Engineers’ Course:

  • Create and run basic programs
  • Implement and run unit tests
  • Design and code modules and classes
  • Benchmarks and profiling to speed up programs
  • Manipulating arrays with NUMPY
  • Process XML and JSON
  • Diversity of SUBPACKAGES that make up SCIPY
  • iPython notebooks for ad hoc calculations, plots, and what-if?
  • Manipulating images with PIL
  • Solving equations with SYMPY

Future Prospects of ‘Applied Python for Scientists and Engineers’ Certification: 

Python developers in The USA are the highest paid developers particularly in data science, machine learning and web development areas. Average salary of ‘Applied Python for Scientists and Engineers’ certificate holder varies in between $70,000 USD to $150,000 USD depending upon experience and area of specialization. The growth of machine learning has been phenomenal in last decade.

 

4870 Applied Python for Scientists and Engineers Course Training Syllabus

We at Microtek Learning provide five days comprehensive training for 4870 Applied Python for Scientists and Engineers course following the most detailed syllabus and robust lab activities:

  1. The python environment
  • Beginning Python; Utilizing the translator 
  • Running a Python content; Python contents on UNIX/Windows 
  • Utilizing the Spyder editorial manage
  1. Flow Control
  • About stream control; Void area; Restrictive articulations (if,else) 
  • Social and Boolean administrators; While circles 
  • Exchange circle exits 
  1. Sequences
  • About successions; Records and tuples; Ordering and cutting; Emphasizing through a succession 
  • Grouping capacities, watchwords, and administrators 
  • Rundown perceptions; Generator articulations; Settled arrangements 
  1. Working with Files
  • Record review; Opening a content record 
  • Perusing a content record; Keeping in touch with a content record 
  • Crude (paired) information 
  1. Dictionaries and Sets
  • Making word references; Repeating through a word reference 
  • Making sets; Working with sets 
  1. Functions
  • Characterizing capacities; Parameters 
  • Variable degree; Returning esteems; Lambda capacities
  1. Errors and Exception handling
  • Linguistic structure mistakes; Special cases 
  • Utilizing attempt/get/else/at last; Taking care of different exemptions 
  • Disregarding exemptions 
  1. Os services
  • The O module; Condition factors; Propelling outside summons 
  • Strolling index trees; Ways, indexes, and filenames 
  • Working with document frameworks; Dates and times 
  1. Pythonic Idioms
  • Little Pythonisms; Lambda capacities; Pressing and unloading arrangements 
  • Rundown Comprehensions; Generator Expressions 
  1. Modules and Packages
  • Instatement code; Namespaces; Executing modules as contents 
  • Documentation; Bundles and name determination 
  • Naming traditions; Utilizing imports 
  1. Classes
  • Characterizing classes; Constructors; Example strategies and information 
  • Qualities; Legacy; Different legacy 
  1. Developer tools
  • Investigating programs with pylint; Making and running unit tests 
  • Investigating applications; Benchmarking code 
  • Profiling applications 
  1. Xml and Json
  • Utilizing ElementTree; Making another XML report 
  • Parsing XML; Finding by labels and XPath 
  • Parsing JSON into Python; Parsing Python into JSON 
  1. Ipython
  • iiPython nuts and bolts; Terminal and GUI shells;
  • Making and utilizing note pads; Sparing and stacking note pads 
  • Specially appointed information perception
  1. Numpy
  • Numpy nuts and bolts; Making exhibits 
  • Ordering and cutting; Vast number sets 
  • Changing information ; Propelled traps 
  1. Scipy
  • What can scipy do?; Most valuable capacities; Bend fitting 
  • Displaying, Information perception; Insights 
  1. A tour of Scipy Subpackages
  • Bunching; Physical and scientific Constants; FFTs 
  • Necessary and differential solvers; Addition and smoothing 
  • Info and Output; Direct Algebra; Picture Processing 
  • Separation Regression; Root-finding, Flag Processing 
  • Meager Matrices; Spatial information and calculations 
  • Factual conveyances and capacities; C/C++ Integration 
  1. Pandas
  • Pandas review; Dataframes; Perusing and composing information 
  • Information arrangement and reshaping; Favor ordering and cutting 
  • Combining and joining informational collections
  1. Matplotlib
  • Making a fundamental plot; Usually utilized plots 
  • Specially appointed information representation 
  • Propelled utilization; Trading pictures 
  1. Python Imaging Library (pil)
  • PIL diagram; Center picture library; Picture preparing 
  • Showing pictures; Labs 

 

Prerequisites for Applied Python for Scientists and Engineers Course:

The knowledge of Python basics is must. Students should be comfortable with files and folders. Completion of 4820 Mastering Python Programming and 4850 Advanced Python Programming before joining it will be added advantage for learning as well as  for career boost.

FAQs for Applied Python for Scientists and Engineers Course

Q: How can I find a 4850 Advanced Python Programming training location if I relocate? 

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

Q: Will I get any certificate upon completion of Applied Python for Scientists and Engineers course training?

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.

Q: What is the learning scope of Numpy during Applied Python for Scientists and Engineers course?

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.  

Q: From where will get help for software and lab practices?

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.