Data Science

Course Description

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

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

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

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

 Lesson 7: Reproducibility in Big Data Analysis

• Reproducibility with Jupyter Notebooks
• Gathering Data in a Reproducible Way
• Code Practices and Standards
• Avoiding Repetition

 Lesson 8: Creating a Full Analysis Report

• Reading Data in Spark from Different Data Sources
• SQL Operations on a Spark DataFrame
• Generating Statistical Measurements