Python Developer Certification 

Python is one of the most widely used general-purpose, high-level programming languages. It supports multiple programming paradigms. The reasons for its popularity are its features like Dynamic Type System and Automatic Memory Management. Python also has a large and comprehensive standard library. The language is not only easy to learn but it also makes the processes of data manipulation and analysis an easy task with the help of its distinctive features. This is the reason behind a decade long usage of Python in the field of scientific computing.

The Python Online Training encompasses the basic as well as the advanced concepts of Python like writing python scripts, sequence and file operations in python, Web Scraping, number crunching etc. This Python Programming Training will also walk you through the most widely used packages like, pandas, numpyscipy, matplotlib etc.

 Topics Covered during the Python Training & It’s Objective 

The Python Training educates the participants on all the basic and advanced concepts of Python programming. It also trains on important and most widely used packages in python. The contents of the Python Online Training are designed in a manner which provides end-to-end training on the concepts of Python language. Starting with an overview of python, the training walks you through the python environment, teaches about the concepts like flow control, sequences, dictionaries and sets, working with files, how to use standard library, regular expressions, functions, sorting, modules usage, python classes & objects, errors and exception handling and introduction to data analytics etc. it also entails the training on packages like numpy, scipy, pandas, matpotlib and sympy. Besides covering these topics the Python Programming Training also teaches about other plotting libraries, web scraping and covers other miscellaneous topics as well.

Who can benefit from this training?

Anyone who wants to learn Python Programming can get enrolled for this training. An experienced professional or a beginner, both can benefit from this training equally. The Python Training has been designed for professionals who wish to make a career in Analytics using Python. Professionals from the field of computer software, analytics, ETL Developers and testing professionals etc. can gain a lot from this training. Besides these professionals, anyone who wishes to get a hands-on training on Python programming should opt for this course.

 Pre-Requisites for the Training

There are no set requirements for doing this training. Although, a prior experience in programming and understanding of the basic concepts like variables/scopes, flow-control and functions would help the participant to learn in a better manner.

 Why should you do this training?

Here are a few reasons why Python is liked and recommended by a large number of programmers –

  • Fast and Easy Usage
  • Open-Source which means it works with Windows, Linux, MacOS Simple to read Syntax and Easy Compilation Feature
  • Built-in debugger makes the debugging process a cakewalk
  • Increases productivity and helps in creating better programs
  • Python is free to use for commercial products
  • Most preferred language for Data Analytics

An Overview of Python What is Python?

  • What is Python?
  • The Birth of Python
  • Python Timeline
  • About Interpreted Languages
  • Advantages of Python
  • Disadvantages of Python
  • How to get Python
  • Which version of Python?
  • The of 2.x
  • Getting Help
  • pydoc

  • Starting Python
  • If the interpreter is not in your PATHs
  • Using the interpreter
  • Trying out a few commands
  • Running a Python script
  • Python scripts on UNIX
  • Python scripts on Windows
  • Python editors and IDEs

  • Using Variables
  • Keywords
  • Built-in Functions
  • Variable Typing
  • Strings
  • Single-quoted string literals
  • Tripe-quoted string literals
  • Raw String literals
  • Unicode literals
  • String operators and methods
  • Numeric literals
  • Math operators and expressions
  • Converting among types
  • Writing to the screen
  • String formatting
  • Legacy string formatting
  • Command line parameters
  • Reading from the keyboard

  • About flow control
  • What’s with the white space?
  • if and else if
  • Conditional expressions
  • Relational operators
  • Boolean operators while loops
  • Alternate ways to exit a loop

  • About sequences
  • Lists
  • Tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Functions for all sequences
  • Using enumerat e()
  • Operators and keywords for sequences
  • The xrange () function
  • Nested sequences
  • List comprehensions
  • Generator expressions

  • About dictionaries
  • When to use dictionaries
  • Creating dictionaries
  • Getting dictionary values
  • Iterating through a dictionary
  • Reading file data into a dictionary
  • Counting with dictionaries
  • About sets
  • Creating sets
  • Working with sets

Working with file

  • Text file I/O
  • Opening a text file
  • The with block
  • Reading a text file
  • Writing to a text file
  • “Binary” (raw, or non – delimited) data

Using the Standard Library

  • The sys module
  • STDIO
  • Launching external programs
  • Paths, directories, and filenames
  • Walking directory trees
  • Math functions
  • Random values
  • Dates and times

  • RE syntax overview
  • Regular expression metacharacters
  • RE Objects Searching for patterns
  • Matching without re objects
  • Compilation flags
  • Grouping Special groups
  • Replacing text
  • Replacing with a callback
  • Splitting a string

  • Defining a function
  • Function parameters
  • Global variables
  • Variable scope
  • Returning values

  • Sorting overview
  • The sorted () function
  • Alternate keys
  • Lambda functions
  • Sorting collection s of collections
  • Using operator.itemgetter ()
  • Sorting dictionaries
  • Sorting in reverse
  • Sorting lists in place

Using Modules

  • What is a module?
  • The import statement
  • Where did the .pyc file come from?
  • Module search path
  • Zipped libraries
  • Creating Modules
  • Packages
  • Module aliases

Python Classes & Objects

  • About OO programming
  • Defining classes
  • Initializers Instance methods
  • Properties
  • Class methods and data
  • Static methods
  • Private meth ods
  • Inheritance
  • Untangling the nomenclature

    • Syntax errors
    • Exceptions
    • Handling exceptions with try
    • Handling multiple exceptions
    • Handling generic exceptions
    • Ignoring exceptions
    • Using else
    • Cleaning up with finally
    • The standard exception hierarchy

Introduction to Data analytics

  • What is data analytics?
  • Various libraries been used in analytics
  • Installing Anaconda Integrated development environment
  • Ipython & Navigation in ipython
  • Launching the IPython Notebook.

Numpy

  • Importing the numpy module
  • The N-Dimensional Array and Available Types
  • Array creation, Array mathematics,Basic Array operations
  • Other different ways to create arrays
  • Indexing, Slicing and Iterating
  • Statistics
  • Random numbers
  • Working examples demonstration
  • Assignment

  • Importing the scipy module
  • Modules available in SciPy
  • Optimization and Minimization
  • Interpolation
  • Integration
  • Statistics
  • Spatial and Clustering Analysis
  • Signal and Image Processing
  • Statistical functions
  • Linear algebra
  • Discrete Fourier transforms (scipy.fftpack).
  • Working examples demonstration
  • Assignments related to scipy and numpy

  • Introduction to Pandas
  • Installing pandas in Windows and Linux
  • Pandas Operations
  • Indexing
  • Merging, joining
  • Group-by and cross-tabulation
  • Statistical modeling
  • Handling for Missing Data Outliers
  • Advanced Operations
  • Working with databases
  • Excel programming with pandas
  • Assignments on Pandas, numpy and scipy

  • Introduction to matplotlib and visualization
  • Installing matplotlib in python
  • IPython and the pylab mode
  • Simple plot
  • Figures, Subplots, Axes and Ticks
  • Other Types of Plots
  • Regular Plots,Scatter Plots,Bar Plots,Contour Plots,Imshow
  • Pie Charts,Quiver Plots,Grids,Multi Plots,Polar Axis,3D Plots,Text
  • Example programs
  • Assignment
  • Real time scenarios

  • What is sympy?
  • Installing sympy
  • basic operations
  • calculus
  • modules in sympy
  • coding examples
  • Assignment

  • plotly library
  • PyQtGraph

  • What is web scraping
  • storing data
  • Reading documents : CSV and pdf
  • Clearning your dirty
  • Image Processing and Text Recognition.

Other miscellaneous topics

Course Reviews

4.7

7 ratings
  • 5 stars5
  • 4 stars2
  • 3 stars0
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  1. Profile photo of Sunny Shah

    Ramesh Babu, Bengaluru, Karnataka, India

    Thank you for the good python session.

  2. Profile photo of Sunny Shah

    Indubhushan Jha, Pune, India

    Training content and delivery was very good. The trainer covered all the topics and his training delivery was very good.

  3. Profile photo of Sunny Shah

    Anish John, Bengaluru, Karnataka, India

    Informative and interactive session

  4. Profile photo of Sunny Shah

    Alagarraja Govindarajan, Hyderabad, India

    The trainer was sound very technical. The overall training delivery was good.

  5. Profile photo of Sunny Shah

    Ajitabh Pandey, New Delhi, India

    The trainer has excellent knowledge.

  6. Profile photo of Sunny Shah

    Asif khan, Hosapete, India

    It was good learning experience and learnt a lot from the tutor.

  7. Profile photo of Sunny Shah

    Manoj Satapathy, Chennai, India

    I liked the training session as it was more of practical than theoretical.

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  • $599.00
  • 30 Hours
  • 10 SEATS
  • Course Certificate
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    Course Features

    We provide 35 hours of live online training including live POC & assignments.

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