Python® has been around for decades, but it’s still one of the most versatile and popular programming languages
out there. Whether you’re relatively new to programming or have been developing software for years, Python is an
excellent language to add to your skill set. In this course, you’ll learn the fundamentals of programming in Python,
and you’ll develop applications to demonstrate your grasp of the language.
Course Objectives:
In this course, you will develop simple command-line programs in Python. You will:
Set up Python and develop a simple application.
Declare and perform operations on simple data types, including strings, numbers, and dates.
Declare and perform operations on data structures, including lists, ranges, tuples, dictionaries, and sets.
Write conditional statements and loops. Define and use functions, classes, and modules.
Manage files and directories through code.
Deal with exceptions.
Target Student:
This course is designed for people who want to learn the Python programming language in preparation for using Python to develop web and desktop applications.
Prerequisites:
To ensure your success in the course, you should have at least a foundational knowledge of personal computer use.
Course Content:
Lesson 1: Setting Up Python and Developing a Simple Application
Set Up the Development Environment
Write Python Statements
Create a Python Application
Prevent Errors
Lesson 2: Processing Simple Data Types
Process Strings and Integers
Process Decimals, Floats, and Mixed Number Types
Lesson 3: Processing Data Structures
Process Ordered Data Structures
Process Unordered Data Structures
Lesson 4: Writing Conditional Statements and Loops in Python
Write a Conditional Statement
Write a Loop
Lesson 5: Structuring Code for Reuse
Define and Call a Function
Define and Instantiate a Class
Import and Use a Module
Lesson 6: Writing Code to Process Files and Directories
Write to a Text File
Read from a Text File
Get the Contents of a Director
Manage Files and Directories
Lesson 7: Dealing with Exceptions
Handle Exceptions
Raise Exceptions
+ Python® Programming: Advanced
Course Description
Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you’ll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, threading, unit testing, and executable applications.
Course Objectives:
In this course, you will expand your Python proficiencies..
You will:
Create object-oriented Python applications.
Design and create a GUI.
Store data in a database from Python applications.
Communicate using client/server network protocols.
Manage multiple processes with threading.
Implement unit testing.
Package an application for distribution.
Target Student:
This course is designed for existing Python programmers who want to expand their Python proficiencies..
Prerequisites:
To ensure your success in the course, you should have at least a foundational knowledge of personal computer use.
Course Content:
Lesson 1: Using Object-Oriented Python
Create and Use Classes in an Application
Use Magic Methods
Incorporate Class Factories
Lesson 2: Creating a GUI
Design a GUI
Create and Arrange a GUI Layout
Interact with User Event
Lesson 3: Using Databases
Basics of Data Management
Use SQLite Databases
Manipulate SQL Data
Lesson 4: Network Programming
Basics of Network Programming
Create a Client/Server Program
Lesson 5: Managing Multiple Processes with Threading
Create a Threaded Application
Manage Thread Resources
Lesson 6: Implementing Unit Testing
Test-Driven Development
Write and Run a Unit Test Case
Lesson 7: Packaging an Application for Distribution
Create a Package Structure
Generate the Package Distribution Files
Generate a Windows Executable
+ Applied Data Science with Python and Jupyter
Course Description
In this course, we show how Jupyter Notebooks can be used with Python for various data-science applications.
Data science is very approachable for beginners, a fact which is reflected by the strength and growing popularity of the Python ecosystem. In this course, we will cover every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modelling data.
Target Student:
This course is intended for an audience with a background in Python. As such, we do not cover the basics of Python in this course. For the best experience in this course, you should have knowledge of programming fundamentals and some experience with Python.
Course Content:
Lesson 1: Jupyter Fundamentals
Basic Functionality and Features
Our First Analysis – The Boston Housing Dataset
Lesson 2: Data Cleaning and Advanced Machine Learning
Preparing to Train a Predictive Model
Training Classification Models
Lesson 3: Web Scraping and Interactive Visualizations
Scraping Web Page Data
Interactive Visualizations
+ Big Data Analysis with Python
Course Description
Big Data Analysis with Python teaches you how to use tools that can control data avalanche. With this course, you’ll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.
Course Objectives:
By the end of this course, you’ll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.
Learning Objectives
Use Python to read and transform data into different formats
Generate basic statistics and metrics using data on the disk
Work with computing tasks distributed over a cluster
Convert data from various sources into storage or querying formats
Prepare data for statistical analysis, visualization, and machine learning
Present data in the form of effective visuals
Target Student:
Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights.
Prerequisites:
To ensure your success in the course, you should have at least a foundational knowledge of personal computer use.
Course Content:
Lesson 1: The Python Data Science Stack
Python Libraries and Packages
Using Pandas
Data Type Conversion
Aggregation and Grouping
Exporting Data from Pandas
Visualization with Pandas
Lesson 2: Statistical Visualizations
Types of Graphs and When to Use Them
Components of a Graph
Which Tool Should Be Used?
Types of Graphs
Pandas DataFrames and Grouped Data
Changing Plot Design: Modifying Graph Components
Exporting Graphs
Lesson 3: Working with Big Data Frameworks
Hadoop
Spark
Writing Parquet Files
Handling Unstructured Data
Lesson 4: Diving Deeper with Spark
Getting Started with Spark DataFrames
Writing Output from Spark DataFrames
Exploring Spark DataFrames
Data Manipulation with Spark DataFrames
Graphs in Spark
Lesson 5: Handling Missing Values and Correlation Analysis
Setting up the Jupyter Notebook
Missing Values
Handling Missing Values in Spark DataFrames
Correlation
Lesson 6: Exploratory Data Analysis
Defining a Business Problem
Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)
Structured Approach to the Data Science Project Life Cycle
Basic & Advance Excel
Data Analysis