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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 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, NumPy, SciPy, Matplotlib, etc.

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What you'll learn

The Python Training educates participants on all the basic and advanced concepts of Python programming. It also provides training on the most important and most widely-used packages in Python. The contents of the Python Online Training are designed in a manner, which provide end-to-end training on the concepts of the Python language. Starting with an overview of Python, the training walks you through the Python environment, teaches about concepts like flow control, sequences, dictionaries and sets, working with files, how to use the standard library, regular expressions, functions, sorting, modules usage, Python classes and objects, errors and exception handling, data analytics, and more. It also entails training on packages like NumPy, SciPy, Pandas, Matplotlib and SymPy. Besides covering these topics, the Python Programming Training also teaches about other plotting libraries, web scraping, and other miscellaneous topics as well. 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, and MacOS
  • Simple to read syntax, and easy compilation features
  • Built-in debugger makes the debugging process a cakewalk
  • Increases productivity and helps create better programs
  • Free to use for commercial products
  • Most preferred language for Data Analytics


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


  • 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 enumerate ()
  • 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
  • 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
  • The Sys Module
  • Launching external programs
  • Paths, directories, and file names
  • 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
  • What is a module?
  • The import statement
  • Where did the .pyc file come from?
  • Module search path
  • Zipped libraries
  • Creating Modules
  • Package
  • Module aliases
  • 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
  • What is data analytics?
  • Various libraries been used in analytics
  • Installing Anaconda Integrated development environment
  • Ipython & Navigation in ipython
  • Launching the IPython Notebook
  • 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
  • Cleaning your dirty data
  • Image Processing and Text Recognition

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All participants who have successfully cleared the certification exam will get to know their scores within 7 working days from the date of taking the exam. The ITIL Foundation Certificate will be sent across to successful participants by mail/courier after approximately 2 weeks from the date of the exam.