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Python Workshop

Learn the most versatile programming language extensively used by tech giants like Google, Facebook, and Nasa.

(PYTHON-WRK.AJ2) / ISBN : 978-1-64459-600-5
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About This Course

Python’s popularity isn’t just a trend. It’s here to rule the world of programming! Join our Python Workshop to learn this versatile coding language that powers technology worldwide. 

The comprehensive syllabus covers everything from the fundamentals to Object-Oriented Programming (OOP), advanced Python concepts to data science & machine learning.

You’ll be building dynamic apps using frameworks like Django and Flask You’ll get hands-on coding practice with our Lab projects, solving coding challenges, and building your own Python apps.

Skills You’ll Get

  • Expertise with R programming fundamentals including basic syntax, data structures, control flow, functions & packages
  • Skilled at data manipulation, and analysis including import, export, cleaning, processing & exploration
  • Ability to perform statistical analysis including descriptive statistics, hypothesis testing, correlation, regression
  • Expert at machine learning with decision trees, random forests, support vector machines, neural networks, and clustering
  • Create data visualizations using ggplot2 and base R graphics
  • Ability to use the shiny framework to build interactive web apps
Download Course Outline

1

Introduction 

  • About the Course
2

Vital Python – Math, Strings, Conditionals, and Loops

  • Introduction
  • Vital Python
  • Numbers: Operations, Types, and Variables
  • Python as a Calculator
  • Strings: Concatenation, Methods, and input()
  • String Interpolation
  • String Indexing and Slicing
  • Slicing
  • Booleans and Conditionals
  • Loops
  • Summary
3

Python Structures

  • Introduction
  • The Power of Lists
  • Matrix Operations
  • List Methods
  • Dictionary Keys and Values
  • Dictionary Methods
  • Tuples
  • A Survey of Sets
  • Choosing Types
  • Summary
4

Executing Python – Programs, Algorithms, and Functions

  • Introduction
  • Python Scripts and Modules
  • Python Algorithms
  • Basic Functions
  • Iterative Functions
  • Recursive Functions
  • Dynamic Programming
  • Helper Functions
  • Variable Scope
  • Lambda Functions
  • Summary
5

Extending Python, Files, Errors, and Graphs

  • Introduction
  • Reading Files
  • Writing Files
  • Preparing for Debugging (Defensive Code)
  • Plotting Techniques
  • The Don'ts of Plotting Graphs
  • Summary
6

Constructing Python – Classes and Methods

  • Introduction
  • Classes and Objects
  • Defining Classes
  • The __init__ method
  • Methods
  • Properties
  • Inheritance
  • Summary
7

The Standard Library

  • Introduction
  • The Importance of the Standard Library
  • Dates and Times
  • Interacting with the OS
  • Using the subprocess Module
  • Logging
  • Collections
  • Functools
  • Summary
8

Becoming Pythonic

  • Introduction
  • Using List Comprehensions
  • Set and Dictionary Comprehensions
  • Default Dictionary
  • Iterators
  • Itertools
  • Generators
  • Regular Expressions
  • Summary
9

Software Development

  • Introduction
  • Debugging
  • Automated Testing
  • Creating a PIP Package
  • Creating Documentation the Easy Way
  • Source Management
  • Summary
10

Practical Python – Advanced Topics

  • Introduction
  • Developing Collaboratively
  • Dependency Management
  • Deploying Code into Production
  • Multiprocessing
  • Parsing Command-Line Arguments in Scripts
  • Performance and Profiling
  • Profiling
  • Summary
11

Data Analytics with pandas and NumPy

  • Introduction
  • NumPy and Basic Stats
  • Matrices
  • The pandas Library
  • Data
  • Null Values
  • Visual Analysis
  • Summary
12

Machine Learning

  • Introduction
  • Introduction to Linear Regression
  • Cross-Validation
  • Regularization: Ridge and Lasso
  • K-Nearest Neighbors, Decision Trees, and Random Forests
  • Classification Models
  • Boosting Methods
  • Summary

1

Vital Python – Math, Strings, Conditionals, and Loops

  • Finding the LCM
  • Assigning Values to a Variable
  • Calculating the Pythagorean Distance between Three Points
  • Displaying Strings in Python
  • Using the input() Function
  • Using the if-else Syntax
  • Using the for Loop
2

Python Structures

  • Using a Nested List to Store Employee Data
  • Implementing Matrix Operations
  • Accessing an Item from a List
  • Adding Items to a List
  • Storing Company Employee Table Data Using a List and a Dictionary
  • Implementing Set Operations
3

Executing Python – Programs, Algorithms, and Functions

  • Writing and Executing a Script
  • Implementing Linear Search
  • Implementing Binary Search
  • Using Bubble Sort
  • Finding the Maximum Number Using Pseudocode
  • Checking Whether a Number is Prime
  • Finding the Factorial of a Number Using Recursion
4

Extending Python, Files, Errors, and Graphs

  • Reading a Text File
  • Generating a Density Plot
  • Creating a Pie Chart
  • Drawing a Scatter Plot to Study the Data
  • Visualizing the Titanic Dataset Using a Pie Chart and Bar Plot
5

Constructing Python – Classes and Methods

  • Creating a Class
  • Using the init Method
  • Implementing Inheritance
6

The Standard Library

  • Comparing datetime across Time Zones
  • Calculating the Time Delta between Two datetime Objects
7

Becoming Pythonic

  • Building a Scorecard Using Dictionary Comprehension and Multiple Lists
  • Implementing the __iter__() Method
  • Using Regular Expressions to Replace Text
  • Using Regular Expressions to Find Winning Customers
8

Software Development

  • Debugging a Sample Python Code for an Application
  • Checking Sample Code with Unit Testing
9

Practical Python – Advanced Topics

  • Using the Multiprocessing Package
  • Using the Argparse Library
10

Data Analytics with pandas and NumPy

  • Finding the Mean and Median from a Collection of Income Data
  • Using DataFrames to Manipulate Data
  • Reading and Viewing the Boston Housing Dataset
  • Performing Visual Data Analysis
11

Machine Learning

  • Using Machine Learning to Predict Customer Return Rate Accuracy
  • Using Linear Regression to Predict the Accuracy of the Median Values of a Dataset

Any questions?
Check out the FAQs

Here’s a sneak peek into some of the most commonly asked questions for our Python workshop.

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It is a class where you learn Python coding concepts to level up your programming skills and build your own web apps.

It’s a self-paced Python workshop that allows you to complete your learning at your own convenience from anytime, anywhere.

There are no formal prerequisites. It is ideal for beginners.

Python is the most in-demand programming language. Learning Python can help in career advancement by adding weightage to your resume. It can help you stand out in the crowd of applicants, and aim for high-paying jobs. 

Yes, it covers several advanced topics including:

  • Regular Expressions
  • Functional Programming
  • Iterators and Generators
  • Debugging and Testing
  • Data Science and Machine Learning

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