Python Certification Course

Topics:

• Overview of Python
• The Companies using Python
• Different Applications where Python is used
• Discuss Python Scripts on UNIX/Windows
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Command Line Arguments
• Writing to the screen

Hands On/Demo:

• Creating “Hello World” code
• Variables
• Demonstrating Conditional Statements
• Demonstrating Loops

Skills:

Fundamentals of Python programming

• Declaring and using Numeric data types
• Using string data type and string operations
• Defining list and list slicing
• Use of Tuple data type

• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Powerful Lamda function in python Programming using functions, modules and external packages

• Building blocks of python programs
• Understanding string in build methods
• List manipulation using in build methods
• Dictionary manipulation
• Programming using string, list and dictionary in build functions

• Python files I/O Functions
• Numbers
• Strings and related operations
• Tuples and related operations
• Lists and related operations
• Dictionaries and related operations
• Sets and related operations

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values
• Lambda Functions
• Object-Oriented Concepts
• Standard Libraries
• Modules Used in Python
• The Import Statements
• Module Search Path
• Package Installation Ways
• Errors and Exception Handling
• Handling Multiple Exceptions

• Powerful pattern matching and searching
• Power of pattern searching using regex in Python
• Real time parsing of networking or system data using regex
• Password, email, url validation using regular expression
• Pattern finding programs using regular expression

• Avoiding code break using exception handling
• Safe guarding file operation using exception handling
• Handling and helping developer with error code
• Programming using Exception handling

• SQL Database connection using Python
• Creating and searching tables
• Reading and storing config information on database
• Programming using database connections

• Understanding threads
• Forking threads
• Synchronizing the threads
• Programming using multithreading

• Installing SMTP Python module
• Sending email
• Reading from file and sending emails to all users addressing them directly for marketing

Module 1: Introduction to HTTP and REST

Understanding HTTP: Learn the basics of the HTTP protocol and its role in web communication.
Introduction to REST: Explore REST architecture and its principles for building web services.

Module 2: Getting Started with Python Requests

Installing Requests Library: Learn how to install and set up the requests library in Python.
Making Simple Requests: Perform basic HTTP GET requests using the requests library.

Module 3: Working with HTTP Methods

GET Method: Understand how to use the GET method to retrieve data from a REST API.
POST Method: Learn how to send data to a server using the POST method.
PUT Method: Explore the PUT method for updating existing resources on a server.
DELETE Method: Discover how to use the DELETE method to remove resources from a server.

Module 4: Handling Advanced Requests

Query Parameters: Learn to pass query parameters in requests for filtering and sorting data.
Request Headers: Understand how to include headers in requests for authentication and content negotiation.
Cookies: Explore how to manage cookies in HTTP requests using the requests library.

Module 5: Error Handling and Retry Mechanisms

Timeouts: Learn how to handle timeout errors gracefully in HTTP requests.
Retry Mechanism: Implement retry logic to handle transient errors during requests.

Module 6: Practical Application

Building a Weather App: Create a simple weather application using the Open Weather Map API with Python and requests.

Module 1: Introduction to Web Scraping

Understanding Web Scraping: Explore the basics of web scraping, spiders, and crawling.
How Web Scraping Works: Learn the underlying principles of web scraping and data extraction.

Module 2: Getting Started with Scrapy

Installation: Install Scrapy using PyCharm or Terminal/Sublime Text.
Project Structure: Understand the structure of a Scrapy project.

Module 3: Web Scraping Best Practices

Robots.txt and Web Scraping Rules: Learn about web scraping etiquette and respecting robots.txt rules.

Module 4: Creating and Running Spiders

Creating Your First Spider: Build your first web crawler using Scrapy.
Running Your Spider: Execute and run your Scrapy spider to scrape websites.

Module 5: Data Extraction Techniques

Using CSS Selectors: Extract data from web pages using CSS selectors.
Using XPath: Learn to extract data using XPath expressions.

Module 6: Data Storage

Item Containers: Store scraped data using Scrapy’s item containers.
Storing Data in JSON, XML, and CSV: Save scraped data in different file formats.

Module 7: Advanced Data Pipelines

Understanding Pipelines: Implement pipelines to process and store scraped data.
Storing Data in SQLite3: Learn the basics of SQLite3 and store data in a local database.

Module 8: Database Integration

Storing Data in MySQL: Integrate Scrapy with MySQL to store scraped data in a relational database.
Storing Data in MongoDB: Explore storing scraped data in a NoSQL MongoDB database.

Module 9: Advanced Scraping Techniques

Web Crawling and Following Links: Build spiders that navigate through multiple pages and follow links.
Scraping Websites with Pagination: Implement pagination handling in Scrapy spiders.

Module 10: Handling Restrictions and Challenges

Logging in with FormRequest: Scrape websites that require authentication using Scrapy’s FormRequest.
Bypassing Restrictions with User-Agent and Proxies: Learn techniques to bypass anti-scraping measures.

Module 1: Introduction to Tkinter

Why Tkinter?: Understand the importance and versatility of Tkinter for building graphical user interfaces (GUIs) in Python.
Our First Tkinter GUI: Create a simple Tkinter window and learn the basics of GUI development.

Module 2: Tkinter Widgets and Layout

Tkinter Widgets & Attributes: Explore different widgets (like labels, buttons, entry fields) and their attributes.
Frame in Tkinter: Learn how to use frames to organize and structure your GUI components.

Module 3: Building Interactive GUIs

Accepting User Input: Use entry widgets and checkbuttons to accept user input in forms.
Handling Events: Learn how to handle user events like button clicks and key presses.

Module 4: Advanced GUI Components

Canvas Widget: Understand how to use the canvas widget for drawing graphics and shapes.
Menus and Submenus: Create dropdown menus and submenus to enhance GUI navigation.

Module 5: Enhancing User Experience

Message Box: Implement message boxes for displaying alerts and notifications.
Sliders and Scrollbars: Integrate sliders and scrollbars to control values and navigate content.

Module 6: Data Presentation Widgets

Listbox: Create listboxes to display selectable lists of items.
Radio Buttons: Learn how to use radio buttons for selecting options in GUI forms.

Module 7: Advanced GUI Techniques

Status Bar: Implement a status bar to provide feedback and display application status.
Using Classes for GUIs: Organize GUI components using classes and objects for better code structure.

Module 8: Practical Projects

Building a Calculator: Develop a functional calculator application using Tkinter.

• NumPy – Home
• NumPy – Introduction
• NumPy – Environment
• NumPy – Ndarray Object
• NumPy – Data Types
• NumPy – Array Attributes
• NumPy – Array Creation Routines
• NumPy – Array from Existing Data
• Array From Numerical Ranges
• NumPy – Indexing & Slicing
• NumPy – Advanced Indexing
• NumPy – Iterating Over Array
• NumPy – Array Manipulation
• NumPy – Binary Operators
• NumPy – String Functions
• NumPy – Mathematical Functions
• NumPy – Arithmetic Operations
• NumPy – Statistical Functions
• Sort, Search & Counting Functions

• Python Pandas – Introduction
• Python Pandas – Environment Setup
• Introduction to Data Structures
• Python Pandas – Series
• Python Pandas – DataFrame
• Python Pandas – Panel
• Python Pandas – Basic Functionality
• Descriptive Statistics
• Function Application
• Python Pandas – Reindexing
• Python Pandas – Iteration
• Python Pandas – Sorting
• Working with Text Data
• Options & Customization
• Indexing & Selecting Data

Module 1: Introduction to Matplotlib

Overview of Data Visualization: Understand the importance of data visualization and its role in data analysis.
Introduction to Matplotlib: Explore what Matplotlib is, why it’s used, and its key features.

Module 2: Getting Started with Matplotlib

Installation and Setup: Learn how to install Matplotlib and set up your environment (including Jupyter Notebook on Windows 10).
Basic Plotting: Dive into basic plotting functions like line plots and scatter plots.

Module 3: Essential Plot Types

Line Plots: Create simple and customized line plots to visualize trends and relationships.
Bar Plots: Understand how to plot categorical data using bar charts.
Scatter Plots: Learn to use scatter plots for visualizing relationships between variables.

Module 4: Advanced Plotting Techniques

Histograms: Visualize data distribution with histograms.
Pie Charts: Represent data proportions using pie charts.
Stem Plots: Explore how to use stem plots for discrete data.

Module 5: Specialized Plot Types

Box Plots: Understand how to create box plots for summarizing data distributions.
Area and Stack Plots: Learn to plot stacked area charts for displaying cumulative data.
Step Plots: Create step plots to represent data changes at distinct intervals.

Module 6: Customizing Plots

Subplots: Master the art of creating multiple plots in one figure using subplots.
Saving Plots: Learn to save your Matplotlib plots in various formats (e.g., PNG, PDF).
Axis Customization: Customize axes labels, ticks, and limits to enhance plot clarity.
Adding Text: Enhance plots by adding text annotations and labels.

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis.

For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It’s continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations.

• Programmers, Developers, Technical Leads, Architects
• Developers aspiring to be a ‘Machine Learning Engineer’
• Analytics Managers who are leading a team of analysts
• Business Analysts who want to understand Machine
• Learning (ML) Techniques
• Information Architects who want to gain expertise in
• Predictive Analytics
• ‘Python’ professionals who want to design automatic predictive models