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.
By the end of this course, participants will have the leadership toolkit to shape and steer GenAI portfolios across their organization.
Strategic Application
- 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
- STDIO
- 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
hands-on learning model
Access to sanitized process maps, KPI definitions, candidate initiative lists, and basic cost baselines (time, cycle time, error or rework rates)
Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.
One of our specialists will contact you within one business day.
Asked Questions
Find details on duration, delivery formats, customization options, and post-program reinforcement.
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.
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