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Applied Python for Scientists and Engineers Training


What Applied Python for Scientists and Engineers training is all about?

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.

Schedule

Contact us to customize this class with your preferred dates, times and location.
You can call us on 1-800-961-0337 or Chat with our representative.

What are the course objectives for Applied Python for Scientists and Engineers training?
  • How to solve equations with SYMPY.
  • How to manipulate images with PIL.
  • How iPython notebooks are used for plots, calculations and purpose.
  • How to diverse SUBPACKAGES that makes up SCIPY.
  • How to process JSON and XML.
  • How to manipulate arrays with NUMPY.
  • How profiling and benchmarks speed up programs.
  • How to code, and design modules covering classes.
  • Implementing run unit tests.
  • How to create and execute run basic programs.
Who should attend Applied Python for Scientists and Engineers training?

This training is recommended for Python professionals who are seeking to apply specialized Python skills to engineering or scientific relevant roles. However, engineers and scientists can seek to sign up for this course as it is highly beneficial.

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

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.

5 Days | $ 2745
4.2
  258 Ratings

1586 Learners

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